SSME fault monitoring and diagnosis expert system

NASA Technical Reports Server (NTRS)

Ali, Moonis; Norman, Arnold M.; Gupta, U. K.

1989-01-01

An expert system, called LEADER, has been designed and implemented for automatic learning, detection, identification, verification, and correction of anomalous propulsion system operations in real time. LEADER employs a set of sensors to monitor engine component performance and to detect, identify, and validate abnormalities with respect to varying engine dynamics and behavior. Two diagnostic approaches are adopted in the architecture of LEADER. In the first approach fault diagnosis is performed through learning and identifying engine behavior patterns. LEADER, utilizing this approach, generates few hypotheses about the possible abnormalities. These hypotheses are then validated based on the SSME design and functional knowledge. The second approach directs the processing of engine sensory data and performs reasoning based on the SSME design, functional knowledge, and the deep-level knowledge, i.e., the first principles (physics and mechanics) of SSME subsystems and components. This paper describes LEADER's architecture which integrates a design based reasoning approach with neural network-based fault pattern matching techniques. The fault diagnosis results obtained through the analyses of SSME ground test data are presented and discussed.

TES: A modular systems approach to expert system development for real time space applications

NASA Technical Reports Server (NTRS)

England, Brenda; Cacace, Ralph

1987-01-01

A major goal of the space station era is to reduce reliance on support from ground based experts. The TIMES Expert System (TES) is an application that monitors and evaluates real time data to perform fault detection and fault isolation as it would otherwise be carried out by a knowledgeable designer. The development process and primary features of the TES, the modular system and the lessons learned are discussed.

Comparative Performance in Single-Port Versus Multiport Minimally Invasive Surgery, and Small Versus Large Operative Working Spaces

PubMed Central

Marcus, Hani J.; Seneci, Carlo A.; Hughes-Hallett, Archie; Cundy, Thomas P.; Nandi, Dipankar; Yang, Guang-Zhong; Darzi, Ara

2015-01-01

Background. Surgical approaches such as transanal endoscopic microsurgery, which utilize small operative working spaces, and are necessarily single-port, are particularly demanding with standard instruments and have not been widely adopted. The aim of this study was to compare simultaneously surgical performance in single-port versus multiport approaches, and small versus large working spaces. Methods. Ten novice, 4 intermediate, and 1 expert surgeons were recruited from a university hospital. A preclinical randomized crossover study design was implemented, comparing performance under the following conditions: (1) multiport approach and large working space, (2) multiport approach and intermediate working space, (3) single-port approach and large working space, (4) single-port approach and intermediate working space, and (5) single-port approach and small working space. In each case, participants performed a peg transfer and pattern cutting tasks, and each task repetition was scored. Results. Intermediate and expert surgeons performed significantly better than novices in all conditions (P < .05). Performance in single-port surgery was significantly worse than multiport surgery (P < .01). In multiport surgery, there was a nonsignificant trend toward worsened performance in the intermediate versus large working space. In single-port surgery, there was a converse trend; performances in the intermediate and small working spaces were significantly better than in the large working space. Conclusions. Single-port approaches were significantly more technically challenging than multiport approaches, possibly reflecting loss of instrument triangulation. Surprisingly, in single-port approaches, in which triangulation was no longer a factor, performance in large working spaces was worse than in intermediate and small working spaces. PMID:26464468

Cyber nursing--health 'experts' approaches in the post-modern era of virtual performances: a nethnography study.

PubMed

Eriksson, Henrik; Salzmann-Erikson, Martin

2013-03-01

The imperative to gather information online and to become an 'expert' by locating effective advice for oneself and others is a fairly new support phenomenon in relation to health advice. The creation of new positions for health 'experts' within the space of the Internet has been addressed as a cybernursing activity. A focused analysis of communication in health forums might give insight into the new roles that are available for health experts in cyberspace. The aim of this study is to describe approaches to being an 'expert' in lifestyle health choice forums on the Internet and to elaborate on the communicative performances that take place in the forums. An archival and cross-sectional observational forum study was undertaken using principles for conducting ethnographic research online. 2640 pages of data from two health Internet forums were gathered and analyzed. The results reveal three distinctive types of experts that emerge in the forums: (1) those that build their expertise by creating a presence in the forum based on lengthy and frequent postings, (2) those who build a presence through reciprocal exchanges with individual posters with questions or concerns, and (3) those who build expertise around a "life long learning" perspective based on logic and reason. The results suggest that experts not only co-exist in the forums, but more importantly they reinforce each others' positions. This effect is central; alongside one another, the posts of the three types of experts we identify constitute a whole for those seeking the forum for advice and support. Users are provided with strong opinions and advice, support and Socratic reasoning, and a problem-oriented approach. The Internet is now an integral part of everyday living, not least of which among those who seek and offer support in cyberspace. As such, cyber nursing has become an important activity to monitor, and formal health care professionals and nursing researchers must stay abreast of developments. Copyright © 2012 Elsevier Ltd. All rights reserved.

Evolving Regulatory Processes Used by Students and Experts in the Acquiring of Improvisational Skills: A Qualitative Study

ERIC Educational Resources Information Center

de Bruin, Leon R.

2018-01-01

The way an improviser practices is a vital and significant aspect to a musician's means and capacities of expression. Expert music performers utilize extensive self-regulatory processes involving planning, strategic development, and systemized approaches to learning and reflective practice. Scholars posit that these processes are constructivist…

[The debate concerning performance-based financing in Africa South of the Sahara: analysis of the nature].

PubMed

Manitu, Serge Mayaka; Meessen, Bruno; Lushimba, Michel Muvudi; Macq, Jean

2015-01-01

Performance-based financing (PBF) is a strategy designed to link thefunding of health services to predetermined results. Payment by an independent strategic purchaser is subject to verification of effective achievement of health outcomes in terms ofquantity and quality. This article investigates the complex tensions observed in relation to performance based financing (PBF) and identifies some reasons for disagreement on this approach. This study was essentially qualitative. Interviews were conducted with a panel of experts on PBF mobilizing their ability to reflect on the various arguments and positions concerning this financing mechanism. To enhance our analyses, we proposed a framework based on the main reasonsfor scientific or political controversies and factors involved in their emergence. Analysis of the information collected therefore consisted of combining experts verbatim reports with corresponding factors of controversies of our framework. Graphic representations of the differences were also established. Tensions concerning PBF are based on facts (experts' interpretation ofPBF), principles and values (around each expert's conceptual framework), balances of power between experts but also inappropriate behavior in the discussion process. Viewpoints remain isolated, each individual experience and an overview are lacking, which can interfere with decision-making and maintain the Health system reform crisis. Potential solutions to reduce these tensions are proposed. Our study shows that experts have difficulties agreeing on a theoretical priority approach to PBE. A good understanding of the nature of the tensions and an improvement in the quality of dialogue will promote a real dynamic of change and the proposal of an agenda of PBF actions.

Hierarchical mixture of experts and diagnostic modeling approach to reduce hydrologic model structural uncertainty: STRUCTURAL UNCERTAINTY DIAGNOSTICS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moges, Edom; Demissie, Yonas; Li, Hong-Yi

2016-04-01

In most water resources applications, a single model structure might be inadequate to capture the dynamic multi-scale interactions among different hydrological processes. Calibrating single models for dynamic catchments, where multiple dominant processes exist, can result in displacement of errors from structure to parameters, which in turn leads to over-correction and biased predictions. An alternative to a single model structure is to develop local expert structures that are effective in representing the dominant components of the hydrologic process and adaptively integrate them based on an indicator variable. In this study, the Hierarchical Mixture of Experts (HME) framework is applied to integratemore » expert model structures representing the different components of the hydrologic process. Various signature diagnostic analyses are used to assess the presence of multiple dominant processes and the adequacy of a single model, as well as to identify the structures of the expert models. The approaches are applied for two distinct catchments, the Guadalupe River (Texas) and the French Broad River (North Carolina) from the Model Parameter Estimation Experiment (MOPEX), using different structures of the HBV model. The results show that the HME approach has a better performance over the single model for the Guadalupe catchment, where multiple dominant processes are witnessed through diagnostic measures. Whereas, the diagnostics and aggregated performance measures prove that French Broad has a homogeneous catchment response, making the single model adequate to capture the response.« less

Does expert knowledge improve automatic probabilistic classification of gait joint motion patterns in children with cerebral palsy?

PubMed Central

Papageorgiou, Eirini; Nieuwenhuys, Angela; Desloovere, Kaat

2017-01-01

Background This study aimed to improve the automatic probabilistic classification of joint motion gait patterns in children with cerebral palsy by using the expert knowledge available via a recently developed Delphi-consensus study. To this end, this study applied both Naïve Bayes and Logistic Regression classification with varying degrees of usage of the expert knowledge (expert-defined and discretized features). A database of 356 patients and 1719 gait trials was used to validate the classification performance of eleven joint motions. Hypotheses Two main hypotheses stated that: (1) Joint motion patterns in children with CP, obtained through a Delphi-consensus study, can be automatically classified following a probabilistic approach, with an accuracy similar to clinical expert classification, and (2) The inclusion of clinical expert knowledge in the selection of relevant gait features and the discretization of continuous features increases the performance of automatic probabilistic joint motion classification. Findings This study provided objective evidence supporting the first hypothesis. Automatic probabilistic gait classification using the expert knowledge available from the Delphi-consensus study resulted in accuracy (91%) similar to that obtained with two expert raters (90%), and higher accuracy than that obtained with non-expert raters (78%). Regarding the second hypothesis, this study demonstrated that the use of more advanced machine learning techniques such as automatic feature selection and discretization instead of expert-defined and discretized features can result in slightly higher joint motion classification performance. However, the increase in performance is limited and does not outweigh the additional computational cost and the higher risk of loss of clinical interpretability, which threatens the clinical acceptance and applicability. PMID:28570616

School Turnaround in Shanghai: The Empowered-Management Program Approach to Improving School Performance

ERIC Educational Resources Information Center

Jensen, Ben; Farmer, Joanna

2013-01-01

Public-school students in the world's largest city, Shanghai, China, are academically outperforming their counterparts across the globe and becoming the talk and envy of education experts worldwide. Using an innovative partnering approach that matches successful schools with low-performing schools, Shanghai has valuable lessons to teach on turning…

Expertise in crime scene examination: comparing search strategies of expert and novice crime scene examiners in simulated crime scenes.

PubMed

Baber, Chris; Butler, Mark

2012-06-01

The strategies of novice and expert crime scene examiners were compared in searching crime scenes. Previous studies have demonstrated that experts frame a scene through reconstructing the likely actions of a criminal and use contextual cues to develop hypotheses that guide subsequent search for evidence. Novice (first-year undergraduate students of forensic sciences) and expert (experienced crime scene examiners) examined two "simulated" crime scenes. Performance was captured through a combination of concurrent verbal protocol and own-point recording, using head-mounted cameras. Although both groups paid attention to the likely modus operandi of the perpetrator (in terms of possible actions taken), the novices paid more attention to individual objects, whereas the experts paid more attention to objects with "evidential value." Novices explore the scene in terms of the objects that it contains, whereas experts consider the evidence analysis that can be performed as a consequence of the examination. The suggestion is that the novices are putting effort into detailing the scene in terms of its features, whereas the experts are putting effort into the likely actions that can be performed as a consequence of the examination. The findings have helped in developing the expertise of novice crime scene examiners and approaches to training of expertise within this population.

A comparison of CLIPS- and LISP-based approaches to the development of a real-time expert system

NASA Technical Reports Server (NTRS)

Frainier, R.; Groleau, N.; Bhatnagar, R.; Lam, C.; Compton, M.; Colombano, S.; Lai, S.; Szolovits, P.; Manahan, M.; Statler, I.

1990-01-01

This paper describes an ongoing expert system development effort started in 1988 which is evaluating both CLIPS- and LISP- based approaches. The expert system is being developed to a project schedule and is planned for flight on Space Shuttle Mission SLS-2 in 1992. The expert system will help astronauts do the best possible science for a vestibular physiology experiment already scheduled for that mission. The system gathers and reduces data from the experiment, flags 'interesting' results, and proposes changes in the experiment both to exploit the in-flight observations and to stay within the time allowed by Mission Control for the experiment. These tasks must all be performed in real time. Two Apple Macintosh computers are used. The CLIPS- and LISP- based environments are layered above the Macintosh computer Operating System. The 'CLIPS-based' environment includes CLIPS and HyperCard. The LlSP-based environment includes Common LISP, Parmenides (a frame system), and FRuleKit (a rule system). Important evaluation factors include ease of programming, performance against real-time requirements, usability by an astronaut, robustness, and ease of maintenance. Current results on the factors of ease of programming, performance against real-time requirements, and ease of maintenance are discussed.

Action and familiarity effects on self and other expert musicians’ Laban effort-shape analyses of expressive bodily behaviors in instrumental music performance: a case study approach

PubMed Central

Broughton, Mary C.; Davidson, Jane W.

2014-01-01

Self-reflective performance review and expert evaluation are features of Western music performance practice. While music is usually the focus, visual information provided by performing musicians’ expressive bodily behaviors communicates expressiveness to musically trained and untrained observers. Yet, within a seemingly homogenous group, such as one of musically trained individuals, diversity of experience exists. Individual differences potentially affect perception of the subtleties of expressive performance, and performers’ effective communication of their expressive intentions. This study aimed to compare self- and other expert musicians’ perception of expressive bodily behaviors observed in marimba performance. We hypothesized that analyses of expressive bodily behaviors differ between expert musicians according to their specialist motor expertise and familiarity with the music. Two professional percussionists and experienced marimba players, and one professional classical singer took part in the study. Participants independently conducted Laban effort-shape analysis – proposing that intentions manifest in bodily activity are understood through shared embodied processes – of a marimbists’ expressive bodily behaviors in an audio-visual performance recording. For one percussionist, this was a self-reflective analysis. The work was unfamiliar to the other percussionist and singer. Perception of the performer’s expressive bodily behaviors appeared to differ according to participants’ individual instrumental or vocal motor expertise, and familiarity with the music. Furthermore, individual type of motor experience appeared to direct participants’ attention in approaching the analyses. Findings support forward and inverse perception–action models, and embodied cognitive theory. Implications offer scientific rigor and artistic interest for how performance practitioners can reflectively analyze performance to improve expressive communication. PMID:25400601

Comparative Performance in Single-Port Versus Multiport Minimally Invasive Surgery, and Small Versus Large Operative Working Spaces: A Preclinical Randomized Crossover Trial.

PubMed

Marcus, Hani J; Seneci, Carlo A; Hughes-Hallett, Archie; Cundy, Thomas P; Nandi, Dipankar; Yang, Guang-Zhong; Darzi, Ara

2016-04-01

Surgical approaches such as transanal endoscopic microsurgery, which utilize small operative working spaces, and are necessarily single-port, are particularly demanding with standard instruments and have not been widely adopted. The aim of this study was to compare simultaneously surgical performance in single-port versus multiport approaches, and small versus large working spaces. Ten novice, 4 intermediate, and 1 expert surgeons were recruited from a university hospital. A preclinical randomized crossover study design was implemented, comparing performance under the following conditions: (1) multiport approach and large working space, (2) multiport approach and intermediate working space, (3) single-port approach and large working space, (4) single-port approach and intermediate working space, and (5) single-port approach and small working space. In each case, participants performed a peg transfer and pattern cutting tasks, and each task repetition was scored. Intermediate and expert surgeons performed significantly better than novices in all conditions (P < .05). Performance in single-port surgery was significantly worse than multiport surgery (P < .01). In multiport surgery, there was a nonsignificant trend toward worsened performance in the intermediate versus large working space. In single-port surgery, there was a converse trend; performances in the intermediate and small working spaces were significantly better than in the large working space. Single-port approaches were significantly more technically challenging than multiport approaches, possibly reflecting loss of instrument triangulation. Surprisingly, in single-port approaches, in which triangulation was no longer a factor, performance in large working spaces was worse than in intermediate and small working spaces. © The Author(s) 2015.

Handheld echocardiographic screening for rheumatic heart disease by non-experts.

PubMed

Ploutz, Michelle; Lu, Jimmy C; Scheel, Janet; Webb, Catherine; Ensing, Greg J; Aliku, Twalib; Lwabi, Peter; Sable, Craig; Beaton, Andrea

2016-01-01

Handheld echocardiography (HAND) has good sensitivity and specificity for rheumatic heart disease (RHD) when performed by cardiologists. However, physician shortages in RHD-endemic areas demand less-skilled users to make RHD screening practical. We examine nurse performance and interpretation of HAND using a simplified approach for RHD screening. Two nurses received training on HAND and a simplified screening approach. Consented students at two schools in Uganda were eligible for participation. A simplified approach (HAND performed and interpreted by a non-expert) was compared with the reference standard (standard portable echocardiography, performed and interpreted by experts according to the 2012 World Heart Federation guidelines). Reasons for false-positive and false-negative HAND studies were identified. A total of 1002 children were consented, with 956 (11.1 years, 41.8% male) having complete data for review. Diagnoses included: 913 (95.5%) children were classified normal, 32 (3.3%) borderline RHD and 11 (1.2%) definite RHD. The simplified approach had a sensitivity of 74.4% (58.8% to 86.5%) and a specificity of 78.8% (76.0% to 81.4%) for any RHD (borderline and definite). Sensitivity improved to 90.9% (58.7% to 98.5%) for definite RHD. Identification and measurement of erroneous colour jets was the most common reason for false-positive studies (n=164/194), while missed mitral regurgitation and shorter regurgitant jet lengths with HAND were the most common reasons for false-negative studies (n=10/11). Non-expert-led HAND screening programmes offer a potential solution to financial and workforce barriers that limit widespread RHD screening. Nurses trained on HAND using a simplified approach had reasonable sensitivity and specificity for RHD screening. Information on reasons for false-negative and false-positive screening studies should be used to inform future training protocols, which could lead to improved screening performance. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Techniques for capturing expert knowledge - An expert systems/hypertext approach

NASA Technical Reports Server (NTRS)

Lafferty, Larry; Taylor, Greg; Schumann, Robin; Evans, Randy; Koller, Albert M., Jr.

1990-01-01

The knowledge-acquisition strategy developed for the Explosive Hazards Classification (EHC) Expert System is described in which expert systems and hypertext are combined, and broad applications are proposed. The EHC expert system is based on rapid prototyping in which primary knowledge acquisition from experts is not emphasized; the explosive hazards technical bulletin, technical guidance, and minimal interviewing are used to develop the knowledge-based system. Hypertext is used to capture the technical information with respect to four issues including procedural, materials, test, and classification issues. The hypertext display allows the integration of multiple knowlege representations such as clarifications or opinions, and thereby allows the performance of a broad range of tasks on a single machine. Among other recommendations, it is suggested that the integration of hypertext and expert systems makes the resulting synergistic system highly efficient.

Theory, practice and performance

NASA Astrophysics Data System (ADS)

Gallagher, Shaun

2017-01-01

I focus on the enactivist and extended mind approaches to embodied cognition (EC), and specifically on the concepts of body schema, affectivity, distributed cognition and intersubjectivity to show how EC has relevance to questions about expert performance, and to the theory and practice of performing arts.

Using cognitive task analysis to create a teaching protocol for bovine dystocia.

PubMed

Read, Emma K; Baillie, Sarah

2013-01-01

When learning skilled techniques and procedures, students face many challenges. Learning is easier when detailed instructions are available, but experts often find it difficult to articulate all of the steps involved in a task or relate to the learner as a novice. This problem is further compounded when the technique is internal and unsighted (e.g., obstetrical procedures). Using expert bovine practitioners and a life-size model cow and calf, the steps and decision making involved in performing correction of two different dystocia presentations (anterior leg back and breech) were deconstructed using cognitive task analysis (CTA). Video cameras were positioned to capture movement inside and outside the cow model while the experts were asked to first perform the technique as they would in a real situation and then perform the procedure again as if articulating the steps to a novice learner. The audio segments were transcribed and, together with the video components, analyzed to create a list of steps for each expert. Consensus was achieved between experts during individual interviews followed by a group discussion. A "gold standard" list or teaching protocol was created for each malpresentation. CTA was useful in defining the technical and cognitive steps required to both perform and teach the tasks effectively. Differences between experts highlight the need for consensus before teaching the skill. In addition, the study identified several different, yet effective, techniques and provided information that could allow experts to consider other approaches they might use when their own technique fails.

A new metric for detecting change in slowly evolving brain tumors: validation in meningioma patients.

PubMed

Pohl, Kilian M; Konukoglu, Ender; Novellas, Sebastian; Ayache, Nicholas; Fedorov, Andriy; Talos, Ion-Florin; Golby, Alexandra; Wells, William M; Kikinis, Ron; Black, Peter M

2011-03-01

Change detection is a critical component in the diagnosis and monitoring of many slowly evolving pathologies. This article describes a semiautomatic monitoring approach using longitudinal medical images. We test the method on brain scans of patients with meningioma, which experts have found difficult to monitor because the tumor evolution is very slow and may be obscured by artifacts related to image acquisition. We describe a semiautomatic procedure targeted toward identifying difficult-to-detect changes in brain tumor imaging. The tool combines input from a medical expert with state-of-the-art technology. The software is easy to calibrate and, in less than 5 minutes, returns the total volume of tumor change in mm. We test the method on postgadolinium, T1-weighted magnetic resonance images of 10 patients with meningioma and compare our results with experts' findings. We also perform benchmark testing with synthetic data. Our experiments indicated that experts' visual inspections are not sensitive enough to detect subtle growth. Measurements based on experts' manual segmentations were highly accurate but also labor intensive. The accuracy of our approach was comparable to the experts' results. However, our approach required far less user input and generated more consistent measurements. The sensitivity of experts' visual inspection is often too low to detect subtle growth of meningiomas from longitudinal scans. Measurements based on experts' segmentation are highly accurate but generally too labor intensive for standard clinical settings. We described an alternative metric that provides accurate and robust measurements of subtle tumor changes while requiring a minimal amount of user input.

An integrated approach to improving noisy speech perception

NASA Astrophysics Data System (ADS)

Koval, Serguei; Stolbov, Mikhail; Smirnova, Natalia; Khitrov, Mikhail

2002-05-01

For a number of practical purposes and tasks, experts have to decode speech recordings of very poor quality. A combination of techniques is proposed to improve intelligibility and quality of distorted speech messages and thus facilitate their comprehension. Along with the application of noise cancellation and speech signal enhancement techniques removing and/or reducing various kinds of distortions and interference (primarily unmasking and normalization in time and frequency fields), the approach incorporates optimal listener expert tactics based on selective listening, nonstandard binaural listening, accounting for short-term and long-term human ear adaptation to noisy speech, as well as some methods of speech signal enhancement to support speech decoding during listening. The approach integrating the suggested techniques ensures high-quality ultimate results and has successfully been applied by Speech Technology Center experts and by numerous other users, mainly forensic institutions, to perform noisy speech records decoding for courts, law enforcement and emergency services, accident investigation bodies, etc.

Prevention of bile duct injury: the case for incorporating educational theories of expertise.

PubMed

McKinley, Sophia K; Brunt, L Michael; Schwaitzberg, Steven D

2014-12-01

Over 700,000 laparoscopic cholecystectomies are performed yearly in the US. Despite multiple advantages of laparoscopic surgery, the increased rate of bile duct injury (BDI) compared to the traditional, open approach to cholecystectomy remains problematic. Due to the seriousness of bile duct injury, the time has come for an aggressive educational campaign to better train laparoscopic surgeons in order to reduce the incidence of this life-threatening and expensive complication. We performed a literature review of what is currently known about the causes of bile duct injury during laparoscopic cholecystectomy. Based on these reviews, we identified educational theories of expertise that may be relevant in understanding variable rates of BDI between surgeons. Finally, we applied educational theories of expertise to the problem of BDI in laparoscopic cholecystectomy to propose how to develop and design an effective educational approach for the prevention of BDI. Multiple studies demonstrate that the primary causes of BDI during laparoscopic cholecystectomy are non-technical. Additionally, there exists a learning curve in which the rates of BDI are higher in a surgeon's earlier cases compared to later cases and that some surgeons perform laparoscopic cholecystectomy with significantly fewer injuries than others. Educational theories indicate that interventions that optimize novice to expert development require (1) revealing expert knowledge to novices and (2) scaffolding the mental habits of expert-like learners. BDI is an appropriate target for the application of educational theories of expertise. Designing better educational interventions for the prevention of BDI will require uncovering the hidden knowledge of expert surgeons and incorporating the processes of reinvestment and progressive problem solving that are inherent to expert performance.

A novel method for expediting the development of patient-reported outcome measures and an evaluation across several populations.

PubMed

Garrard, Lili; Price, Larry R; Bott, Marjorie J; Gajewski, Byron J

2016-10-01

Item response theory (IRT) models provide an appropriate alternative to the classical ordinal confirmatory factor analysis (CFA) during the development of patient-reported outcome measures (PROMs). Current literature has identified the assessment of IRT model fit as both challenging and underdeveloped (Sinharay & Johnson, 2003; Sinharay, Johnson, & Stern, 2006). This study evaluates the performance of Ordinal Bayesian Instrument Development (OBID), a Bayesian IRT model with a probit link function approach, through applications in two breast cancer-related instrument development studies. The primary focus is to investigate an appropriate method for comparing Bayesian IRT models in PROMs development. An exact Bayesian leave-one-out cross-validation (LOO-CV) approach (Vehtari & Lampinen, 2002) is implemented to assess prior selection for the item discrimination parameter in the IRT model and subject content experts' bias (in a statistical sense and not to be confused with psychometric bias as in differential item functioning) toward the estimation of item-to-domain correlations. Results support the utilization of content subject experts' information in establishing evidence for construct validity when sample size is small. However, the incorporation of subject experts' content information in the OBID approach can be sensitive to the level of expertise of the recruited experts. More stringent efforts need to be invested in the appropriate selection of subject experts to efficiently use the OBID approach and reduce potential bias during PROMs development.

Photolithography diagnostic expert systems: a systematic approach to problem solving in a wafer fabrication facility

NASA Astrophysics Data System (ADS)

Weatherwax Scott, Caroline; Tsareff, Christopher R.

1990-06-01

One of the main goals of process engineering in the semiconductor industry is to improve wafer fabrication productivity and throughput. Engineers must work continuously toward this goal in addition to performing sustaining and development tasks. To accomplish these objectives, managers must make efficient use of engineering resources. One of the tools being used to improve efficiency is the diagnostic expert system. Expert systems are knowledge based computer programs designed to lead the user through the analysis and solution of a problem. Several photolithography diagnostic expert systems have been implemented at the Hughes Technology Center to provide a systematic approach to process problem solving. This systematic approach was achieved by documenting cause and effect analyses for a wide variety of processing problems. This knowledge was organized in the form of IF-THEN rules, a common structure for knowledge representation in expert system technology. These rules form the knowledge base of the expert system which is stored in the computer. The systems also include the problem solving methodology used by the expert when addressing a problem in his area of expertise. Operators now use the expert systems to solve many process problems without engineering assistance. The systems also facilitate the collection of appropriate data to assist engineering in solving unanticipated problems. Currently, several expert systems have been implemented to cover all aspects of the photolithography process. The systems, which have been in use for over a year, include wafer surface preparation (HMDS), photoresist coat and softbake, align and expose on a wafer stepper, and develop inspection. These systems are part of a plan to implement an expert system diagnostic environment throughout the wafer fabrication facility. In this paper, the systems' construction is described, including knowledge acquisition, rule construction, knowledge refinement, testing, and evaluation. The roles played by the process engineering expert and the knowledge engineer are discussed. The features of the systems are shown, particularly the interactive quality of the consultations and the ease of system use.

Resolution of seven-axis manipulator redundancy: A heuristic issue

NASA Technical Reports Server (NTRS)

Chen, I.

1990-01-01

An approach is presented for the resolution of the redundancy of a seven-axis manipulator arm from the AI and expert systems point of view. This approach is heuristic, analytical, and globally resolves the redundancy at the position level. When compared with other approaches, this approach has several improved performance capabilities, including singularity avoidance, repeatability, stability, and simplicity.

An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic

NASA Astrophysics Data System (ADS)

Zhu, A.-Xing; Wang, Rongxun; Qiao, Jianping; Qin, Cheng-Zhi; Chen, Yongbo; Liu, Jing; Du, Fei; Lin, Yang; Zhu, Tongxin

2014-06-01

This paper presents an expert knowledge-based approach to landslide susceptibility mapping in an effort to overcome the deficiencies of data-driven approaches. The proposed approach consists of three generic steps: (1) extraction of knowledge on the relationship between landslide susceptibility and predisposing factors from domain experts, (2) characterization of predisposing factors using GIS techniques, and (3) prediction of landslide susceptibility under fuzzy logic. The approach was tested in two study areas in China - the Kaixian study area (about 250 km2) and the Three Gorges study area (about 4600 km2). The Kaixian study area was used to develop the approach and to evaluate its validity. The Three Gorges study area was used to test both the portability and the applicability of the developed approach for mapping landslide susceptibility over large study areas. Performance was evaluated by examining if the mean of the computed susceptibility values at landslide sites was statistically different from that of the entire study area. A z-score test was used to examine the statistical significance of the difference. The computed z for the Kaixian area was 3.70 and the corresponding p-value was less than 0.001. This suggests that the computed landslide susceptibility values are good indicators of landslide occurrences. In the Three Gorges study area, the computed z was 10.75 and the corresponding p-value was less than 0.001. In addition, we divided the susceptibility value into four levels: low (0.0-0.25), moderate (0.25-0.5), high (0.5-0.75) and very high (0.75-1.0). No landslides were found for areas of low susceptibility. Landslide density was about three times higher in areas of very high susceptibility than that in the moderate susceptibility areas, and more than twice as high as that in the high susceptibility areas. The results from the Three Gorge study area suggest that the extracted expert knowledge can be extrapolated to another study area and the developed approach can be used in large-scale projects. Results from these case studies suggest that the expert knowledge-based approach is effective in mapping landslide susceptibility and that its performance is maintained when it is moved to a new area from the model development area without changes to the knowledge base.

The Potential of Automatic Word Comparison for Historical Linguistics.

PubMed

List, Johann-Mattis; Greenhill, Simon J; Gray, Russell D

2017-01-01

The amount of data from languages spoken all over the world is rapidly increasing. Traditional manual methods in historical linguistics need to face the challenges brought by this influx of data. Automatic approaches to word comparison could provide invaluable help to pre-analyze data which can be later enhanced by experts. In this way, computational approaches can take care of the repetitive and schematic tasks leaving experts to concentrate on answering interesting questions. Here we test the potential of automatic methods to detect etymologically related words (cognates) in cross-linguistic data. Using a newly compiled database of expert cognate judgments across five different language families, we compare how well different automatic approaches distinguish related from unrelated words. Our results show that automatic methods can identify cognates with a very high degree of accuracy, reaching 89% for the best-performing method Infomap. We identify the specific strengths and weaknesses of these different methods and point to major challenges for future approaches. Current automatic approaches for cognate detection-although not perfect-could become an important component of future research in historical linguistics.

The Potential of Automatic Word Comparison for Historical Linguistics

PubMed Central

Greenhill, Simon J.; Gray, Russell D.

2017-01-01

The amount of data from languages spoken all over the world is rapidly increasing. Traditional manual methods in historical linguistics need to face the challenges brought by this influx of data. Automatic approaches to word comparison could provide invaluable help to pre-analyze data which can be later enhanced by experts. In this way, computational approaches can take care of the repetitive and schematic tasks leaving experts to concentrate on answering interesting questions. Here we test the potential of automatic methods to detect etymologically related words (cognates) in cross-linguistic data. Using a newly compiled database of expert cognate judgments across five different language families, we compare how well different automatic approaches distinguish related from unrelated words. Our results show that automatic methods can identify cognates with a very high degree of accuracy, reaching 89% for the best-performing method Infomap. We identify the specific strengths and weaknesses of these different methods and point to major challenges for future approaches. Current automatic approaches for cognate detection—although not perfect—could become an important component of future research in historical linguistics. PMID:28129337

Automated Scenario Generation: Toward Tailored and Optimized Military Training in Virtual Environments

DTIC Science & Technology

2012-01-01

us.army.mil ABSTRACT Scenario-based training exemplifies the learning-by-doing approach to human performance improvement. In this paper , we enumerate...through a narrative, mission, quest, or scenario. In this paper we argue for a combinatorial optimization search approach to selecting and ordering...the role of an expert for the purposes of practicing skills and knowledge in realistic situations in a learning-by-doing approach to performance

Automated implementation of rule-based expert systems with neural networks for time-critical applications

NASA Technical Reports Server (NTRS)

Ramamoorthy, P. A.; Huang, Song; Govind, Girish

1991-01-01

In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed.

Knowledge-based expert systems and a proof-of-concept case study for multiple sequence alignment construction and analysis.

PubMed

Aniba, Mohamed Radhouene; Siguenza, Sophie; Friedrich, Anne; Plewniak, Frédéric; Poch, Olivier; Marchler-Bauer, Aron; Thompson, Julie Dawn

2009-01-01

The traditional approach to bioinformatics analyses relies on independent task-specific services and applications, using different input and output formats, often idiosyncratic, and frequently not designed to inter-operate. In general, such analyses were performed by experts who manually verified the results obtained at each step in the process. Today, the amount of bioinformatics information continuously being produced means that handling the various applications used to study this information presents a major data management and analysis challenge to researchers. It is now impossible to manually analyse all this information and new approaches are needed that are capable of processing the large-scale heterogeneous data in order to extract the pertinent information. We review the recent use of integrated expert systems aimed at providing more efficient knowledge extraction for bioinformatics research. A general methodology for building knowledge-based expert systems is described, focusing on the unstructured information management architecture, UIMA, which provides facilities for both data and process management. A case study involving a multiple alignment expert system prototype called AlexSys is also presented.

Knowledge-based expert systems and a proof-of-concept case study for multiple sequence alignment construction and analysis

PubMed Central

Aniba, Mohamed Radhouene; Siguenza, Sophie; Friedrich, Anne; Plewniak, Frédéric; Poch, Olivier; Marchler-Bauer, Aron

2009-01-01

The traditional approach to bioinformatics analyses relies on independent task-specific services and applications, using different input and output formats, often idiosyncratic, and frequently not designed to inter-operate. In general, such analyses were performed by experts who manually verified the results obtained at each step in the process. Today, the amount of bioinformatics information continuously being produced means that handling the various applications used to study this information presents a major data management and analysis challenge to researchers. It is now impossible to manually analyse all this information and new approaches are needed that are capable of processing the large-scale heterogeneous data in order to extract the pertinent information. We review the recent use of integrated expert systems aimed at providing more efficient knowledge extraction for bioinformatics research. A general methodology for building knowledge-based expert systems is described, focusing on the unstructured information management architecture, UIMA, which provides facilities for both data and process management. A case study involving a multiple alignment expert system prototype called AlexSys is also presented. PMID:18971242

What is an expert? A systems perspective on expertise.

PubMed

Caley, Michael Julian; O'Leary, Rebecca A; Fisher, Rebecca; Low-Choy, Samantha; Johnson, Sandra; Mengersen, Kerrie

2014-02-01

Expert knowledge is a valuable source of information with a wide range of research applications. Despite the recent advances in defining expert knowledge, little attention has been given to how to view expertise as a system of interacting contributory factors for quantifying an individual's expertise. We present a systems approach to expertise that accounts for many contributing factors and their inter-relationships and allows quantification of an individual's expertise. A Bayesian network (BN) was chosen for this purpose. For illustration, we focused on taxonomic expertise. The model structure was developed in consultation with taxonomists. The relative importance of the factors within the network was determined by a second set of taxonomists (supra-experts) who also provided validation of the model structure. Model performance was assessed by applying the model to hypothetical career states of taxonomists designed to incorporate known differences in career states for model testing. The resulting BN model consisted of 18 primary nodes feeding through one to three higher-order nodes before converging on the target node (Taxonomic Expert). There was strong consistency among node weights provided by the supra-experts for some nodes, but not others. The higher-order nodes, "Quality of work" and "Total productivity", had the greatest weights. Sensitivity analysis indicated that although some factors had stronger influence in the outer nodes of the network, there was relatively equal influence of the factors leading directly into the target node. Despite the differences in the node weights provided by our supra-experts, there was good agreement among assessments of our hypothetical experts that accurately reflected differences we had specified. This systems approach provides a way of assessing the overall level of expertise of individuals, accounting for multiple contributory factors, and their interactions. Our approach is adaptable to other situations where it is desirable to understand components of expertise.

A neuro-fuzzy architecture for real-time applications

NASA Technical Reports Server (NTRS)

Ramamoorthy, P. A.; Huang, Song

1992-01-01

Neural networks and fuzzy expert systems perform the same task of functional mapping using entirely different approaches. Each approach has certain unique features. The ability to learn specific input-output mappings from large input/output data possibly corrupted by noise and the ability to adapt or continue learning are some important features of neural networks. Fuzzy expert systems are known for their ability to deal with fuzzy information and incomplete/imprecise data in a structured, logical way. Since both of these techniques implement the same task (that of functional mapping--we regard 'inferencing' as one specific category under this class), a fusion of the two concepts that retains their unique features while overcoming their individual drawbacks will have excellent applications in the real world. In this paper, we arrive at a new architecture by fusing the two concepts. The architecture has the trainability/adaptibility (based on input/output observations) property of the neural networks and the architectural features that are unique to fuzzy expert systems. It also does not require specific information such as fuzzy rules, defuzzification procedure used, etc., though any such information can be integrated into the architecture. We show that this architecture can provide better performance than is possible from a single two or three layer feedforward neural network. Further, we show that this new architecture can be used as an efficient vehicle for hardware implementation of complex fuzzy expert systems for real-time applications. A numerical example is provided to show the potential of this approach.

Use of the self-organising map network (SOMNet) as a decision support system for regional mental health planning.

PubMed

Chung, Younjin; Salvador-Carulla, Luis; Salinas-Pérez, José A; Uriarte-Uriarte, Jose J; Iruin-Sanz, Alvaro; García-Alonso, Carlos R

2018-04-25

Decision-making in mental health systems should be supported by the evidence-informed knowledge transfer of data. Since mental health systems are inherently complex, involving interactions between its structures, processes and outcomes, decision support systems (DSS) need to be developed using advanced computational methods and visual tools to allow full system analysis, whilst incorporating domain experts in the analysis process. In this study, we use a DSS model developed for interactive data mining and domain expert collaboration in the analysis of complex mental health systems to improve system knowledge and evidence-informed policy planning. We combine an interactive visual data mining approach, the self-organising map network (SOMNet), with an operational expert knowledge approach, expert-based collaborative analysis (EbCA), to develop a DSS model. The SOMNet was applied to the analysis of healthcare patterns and indicators of three different regional mental health systems in Spain, comprising 106 small catchment areas and providing healthcare for over 9 million inhabitants. Based on the EbCA, the domain experts in the development team guided and evaluated the analytical processes and results. Another group of 13 domain experts in mental health systems planning and research evaluated the model based on the analytical information of the SOMNet approach for processing information and discovering knowledge in a real-world context. Through the evaluation, the domain experts assessed the feasibility and technology readiness level (TRL) of the DSS model. The SOMNet, combined with the EbCA, effectively processed evidence-based information when analysing system outliers, explaining global and local patterns, and refining key performance indicators with their analytical interpretations. The evaluation results showed that the DSS model was feasible by the domain experts and reached level 7 of the TRL (system prototype demonstration in operational environment). This study supports the benefits of combining health systems engineering (SOMNet) and expert knowledge (EbCA) to analyse the complexity of health systems research. The use of the SOMNet approach contributes to the demonstration of DSS for mental health planning in practice.

A Bayesian Approach to Model Selection in Hierarchical Mixtures-of-Experts Architectures.

PubMed

Tanner, Martin A.; Peng, Fengchun; Jacobs, Robert A.

1997-03-01

There does not exist a statistical model that shows good performance on all tasks. Consequently, the model selection problem is unavoidable; investigators must decide which model is best at summarizing the data for each task of interest. This article presents an approach to the model selection problem in hierarchical mixtures-of-experts architectures. These architectures combine aspects of generalized linear models with those of finite mixture models in order to perform tasks via a recursive "divide-and-conquer" strategy. Markov chain Monte Carlo methodology is used to estimate the distribution of the architectures' parameters. One part of our approach to model selection attempts to estimate the worth of each component of an architecture so that relatively unused components can be pruned from the architecture's structure. A second part of this approach uses a Bayesian hypothesis testing procedure in order to differentiate inputs that carry useful information from nuisance inputs. Simulation results suggest that the approach presented here adheres to the dictum of Occam's razor; simple architectures that are adequate for summarizing the data are favored over more complex structures. Copyright 1997 Elsevier Science Ltd. All Rights Reserved.

Mechanisms and neural basis of object and pattern recognition: a study with chess experts.

PubMed

Bilalić, Merim; Langner, Robert; Erb, Michael; Grodd, Wolfgang

2010-11-01

Comparing experts with novices offers unique insights into the functioning of cognition, based on the maximization of individual differences. Here we used this expertise approach to disentangle the mechanisms and neural basis behind two processes that contribute to everyday expertise: object and pattern recognition. We compared chess experts and novices performing chess-related and -unrelated (visual) search tasks. As expected, the superiority of experts was limited to the chess-specific task, as there were no differences in a control task that used the same chess stimuli but did not require chess-specific recognition. The analysis of eye movements showed that experts immediately and exclusively focused on the relevant aspects in the chess task, whereas novices also examined irrelevant aspects. With random chess positions, when pattern knowledge could not be used to guide perception, experts nevertheless maintained an advantage. Experts' superior domain-specific parafoveal vision, a consequence of their knowledge about individual domain-specific symbols, enabled improved object recognition. Functional magnetic resonance imaging corroborated this differentiation between object and pattern recognition and showed that chess-specific object recognition was accompanied by bilateral activation of the occipitotemporal junction, whereas chess-specific pattern recognition was related to bilateral activations in the middle part of the collateral sulci. Using the expertise approach together with carefully chosen controls and multiple dependent measures, we identified object and pattern recognition as two essential cognitive processes in expert visual cognition, which may also help to explain the mechanisms of everyday perception.

Expert system and process optimization techniques for real-time monitoring and control of plasma processes

NASA Astrophysics Data System (ADS)

Cheng, Jie; Qian, Zhaogang; Irani, Keki B.; Etemad, Hossein; Elta, Michael E.

1991-03-01

To meet the ever-increasing demand of the rapidly-growing semiconductor manufacturing industry it is critical to have a comprehensive methodology integrating techniques for process optimization real-time monitoring and adaptive process control. To this end we have accomplished an integrated knowledge-based approach combining latest expert system technology machine learning method and traditional statistical process control (SPC) techniques. This knowledge-based approach is advantageous in that it makes it possible for the task of process optimization and adaptive control to be performed consistently and predictably. Furthermore this approach can be used to construct high-level and qualitative description of processes and thus make the process behavior easy to monitor predict and control. Two software packages RIST (Rule Induction and Statistical Testing) and KARSM (Knowledge Acquisition from Response Surface Methodology) have been developed and incorporated with two commercially available packages G2 (real-time expert system) and ULTRAMAX (a tool for sequential process optimization).

Development of an Expert Judgement Elicitation and Calibration Methodology for Risk Analysis in Conceptual Vehicle Design

NASA Technical Reports Server (NTRS)

Unal, Resit; Keating, Charles; Conway, Bruce; Chytka, Trina

2004-01-01

A comprehensive expert-judgment elicitation methodology to quantify input parameter uncertainty and analysis tool uncertainty in a conceptual launch vehicle design analysis has been developed. The ten-phase methodology seeks to obtain expert judgment opinion for quantifying uncertainties as a probability distribution so that multidisciplinary risk analysis studies can be performed. The calibration and aggregation techniques presented as part of the methodology are aimed at improving individual expert estimates, and provide an approach to aggregate multiple expert judgments into a single probability distribution. The purpose of this report is to document the methodology development and its validation through application to a reference aerospace vehicle. A detailed summary of the application exercise, including calibration and aggregation results is presented. A discussion of possible future steps in this research area is given.

Expertise finding in bibliographic network: topic dominance learning approach.

PubMed

Neshati, Mahmood; Hashemi, Seyyed Hadi; Beigy, Hamid

2014-12-01

Expert finding problem in bibliographic networks has received increased interest in recent years. This problem concerns finding relevant researchers for a given topic. Motivated by the observation that rarely do all coauthors contribute to a paper equally, in this paper, we propose two discriminative methods for realizing leading authors contributing in a scientific publication. Specifically, we cast the problem of expert finding in a bibliographic network to find leading experts in a research group, which is easier to solve. We recognize three feature groups that can discriminate relevant experts from other authors of a document. Experimental results on a real dataset, and a synthetic one that is gathered from a Microsoft academic search engine, show that the proposed model significantly improves the performance of expert finding in terms of all common information retrieval evaluation metrics.

Estimating structural collapse fragility of generic building typologies using expert judgment

USGS Publications Warehouse

Jaiswal, Kishor; Wald, David J.; Perkins, David M.; Aspinall, Willy P.; Kiremidjian, Anne S.

2014-01-01

The structured expert elicitation process proposed by Cooke (1991), hereafter referred to as Cooke's approach, is applied for the first time in the realm of structural collapse-fragility assessment for selected generic construction types. Cooke's approach works on the principle of objective calibration scoring of judgments couple with hypothesis testing used in classical statistics. The performance-based scoring system reflects the combined measure of an expert's informativeness about variables in the problem are under consideration, and their ability to enumerate, in a statistically accurate way through expressing their true beliefs, the quantitative uncertainties associated with their assessments. We summarize the findings of an expert elicitation workshop in which a dozen earthquake-engineering professionals from around the world were engaged to estimate seismic collapse fragility for generic construction types. Development of seismic collapse fragility-functions was accomplished by combining their judgments using weights derived from Cooke's method. Although substantial effort was needed to elicit the inputs of these experts successfully, we anticipate that the elicitation strategy described here will gain momentum in a wide variety of earthquake seismology and engineering hazard and risk analyses where physical model and data limitations are inherent and objective professional judgment can fill gaps.

Pedagogical applications of cognitive research on musical improvisation.

PubMed

Biasutti, Michele

2015-01-01

This paper presents a model for the implementation of educational activities involving musical improvisation that is based on a review of the literature on the psychology of music. Psychology of music is a complex field of research in which quantitative and qualitative methods have been employed involving participants ranging from novices to expert performers. The cognitive research has been analyzed to propose a pedagogical approach to the development of processes rather than products that focus on an expert's use of improvisation. The intention is to delineate a reflective approach that goes beyond the mere instruction of some current practices of teaching improvisation in jazz pedagogy. The review highlights that improvisation is a complex, multidimensional act that involves creative and performance behaviors in real-time in addition to processes such as sensory and perceptual encoding, motor control, performance monitoring, and memory storage and recall. Educational applications for the following processes are outlined: anticipation, use of repertoire, emotive communication, feedback, and flow. These characteristics are discussed in relation to the design of a pedagogical approach to musical improvisation based on reflection and metacognition development.

Segmentation of Brain Lesions in MRI and CT Scan Images: A Hybrid Approach Using k-Means Clustering and Image Morphology

NASA Astrophysics Data System (ADS)

Agrawal, Ritu; Sharma, Manisha; Singh, Bikesh Kumar

2018-04-01

Manual segmentation and analysis of lesions in medical images is time consuming and subjected to human errors. Automated segmentation has thus gained significant attention in recent years. This article presents a hybrid approach for brain lesion segmentation in different imaging modalities by combining median filter, k means clustering, Sobel edge detection and morphological operations. Median filter is an essential pre-processing step and is used to remove impulsive noise from the acquired brain images followed by k-means segmentation, Sobel edge detection and morphological processing. The performance of proposed automated system is tested on standard datasets using performance measures such as segmentation accuracy and execution time. The proposed method achieves a high accuracy of 94% when compared with manual delineation performed by an expert radiologist. Furthermore, the statistical significance test between lesion segmented using automated approach and that by expert delineation using ANOVA and correlation coefficient achieved high significance values of 0.986 and 1 respectively. The experimental results obtained are discussed in lieu of some recently reported studies.

Motion generation of robotic surgical tasks: learning from expert demonstrations.

PubMed

Reiley, Carol E; Plaku, Erion; Hager, Gregory D

2010-01-01

Robotic surgical assistants offer the possibility of automating portions of a task that are time consuming and tedious in order to reduce the cognitive workload of a surgeon. This paper proposes using programming by demonstration to build generative models and generate smooth trajectories that capture the underlying structure of the motion data recorded from expert demonstrations. Specifically, motion data from Intuitive Surgical's da Vinci Surgical System of a panel of expert surgeons performing three surgical tasks are recorded. The trials are decomposed into subtasks or surgemes, which are then temporally aligned through dynamic time warping. Next, a Gaussian Mixture Model (GMM) encodes the experts' underlying motion structure. Gaussian Mixture Regression (GMR) is then used to extract a smooth reference trajectory to reproduce a trajectory of the task. The approach is evaluated through an automated skill assessment measurement. Results suggest that this paper presents a means to (i) extract important features of the task, (ii) create a metric to evaluate robot imitative performance (iii) generate smoother trajectories for reproduction of three common medical tasks.

GANViz: A Visual Analytics Approach to Understand the Adversarial Game.

PubMed

Wang, Junpeng; Gou, Liang; Yang, Hao; Shen, Han-Wei

2018-06-01

Generative models bear promising implications to learn data representations in an unsupervised fashion with deep learning. Generative Adversarial Nets (GAN) is one of the most popular frameworks in this arena. Despite the promising results from different types of GANs, in-depth understanding on the adversarial training process of the models remains a challenge to domain experts. The complexity and the potential long-time training process of the models make it hard to evaluate, interpret, and optimize them. In this work, guided by practical needs from domain experts, we design and develop a visual analytics system, GANViz, aiming to help experts understand the adversarial process of GANs in-depth. Specifically, GANViz evaluates the model performance of two subnetworks of GANs, provides evidence and interpretations of the models' performance, and empowers comparative analysis with the evidence. Through our case studies with two real-world datasets, we demonstrate that GANViz can provide useful insight into helping domain experts understand, interpret, evaluate, and potentially improve GAN models.

Distinct Neural Activity Associated with Focused-Attention Meditation and Loving-Kindness Meditation

PubMed Central

Lee, Tatia M. C.; Leung, Mei-Kei; Hou, Wai-Kai; Tang, Joey C. Y.; Yin, Jing; So, Kwok-Fai; Lee, Chack-Fan; Chan, Chetwyn C. H.

2012-01-01

This study examined the dissociable neural effects of ānāpānasati (focused-attention meditation, FAM) and mettā (loving-kindness meditation, LKM) on BOLD signals during cognitive (continuous performance test, CPT) and affective (emotion-processing task, EPT, in which participants viewed affective pictures) processing. Twenty-two male Chinese expert meditators (11 FAM experts, 11 LKM experts) and 22 male Chinese novice meditators (11 FAM novices, 11 LKM novices) had their brain activity monitored by a 3T MRI scanner while performing the cognitive and affective tasks in both meditation and baseline states. We examined the interaction between state (meditation vs. baseline) and expertise (expert vs. novice) separately during LKM and FAM, using a conjunction approach to reveal common regions sensitive to the expert meditative state. Additionally, exclusive masking techniques revealed distinct interactions between state and group during LKM and FAM. Specifically, we demonstrated that the practice of FAM was associated with expertise-related behavioral improvements and neural activation differences in attention task performance. However, the effect of state LKM meditation did not carry over to attention task performance. On the other hand, both FAM and LKM practice appeared to affect the neural responses to affective pictures. For viewing sad faces, the regions activated for FAM practitioners were consistent with attention-related processing; whereas responses of LKM experts to sad pictures were more in line with differentiating emotional contagion from compassion/emotional regulation processes. Our findings provide the first report of distinct neural activity associated with forms of meditation during sustained attention and emotion processing. PMID:22905090

Using expert knowledge to incorporate uncertainty in cause-of-death assignments for modeling of cause-specific mortality

USGS Publications Warehouse

Walsh, Daniel P.; Norton, Andrew S.; Storm, Daniel J.; Van Deelen, Timothy R.; Heisy, Dennis M.

2018-01-01

Implicit and explicit use of expert knowledge to inform ecological analyses is becoming increasingly common because it often represents the sole source of information in many circumstances. Thus, there is a need to develop statistical methods that explicitly incorporate expert knowledge, and can successfully leverage this information while properly accounting for associated uncertainty during analysis. Studies of cause-specific mortality provide an example of implicit use of expert knowledge when causes-of-death are uncertain and assigned based on the observer's knowledge of the most likely cause. To explicitly incorporate this use of expert knowledge and the associated uncertainty, we developed a statistical model for estimating cause-specific mortality using a data augmentation approach within a Bayesian hierarchical framework. Specifically, for each mortality event, we elicited the observer's belief of cause-of-death by having them specify the probability that the death was due to each potential cause. These probabilities were then used as prior predictive values within our framework. This hierarchical framework permitted a simple and rigorous estimation method that was easily modified to include covariate effects and regularizing terms. Although applied to survival analysis, this method can be extended to any event-time analysis with multiple event types, for which there is uncertainty regarding the true outcome. We conducted simulations to determine how our framework compared to traditional approaches that use expert knowledge implicitly and assume that cause-of-death is specified accurately. Simulation results supported the inclusion of observer uncertainty in cause-of-death assignment in modeling of cause-specific mortality to improve model performance and inference. Finally, we applied the statistical model we developed and a traditional method to cause-specific survival data for white-tailed deer, and compared results. We demonstrate that model selection results changed between the two approaches, and incorporating observer knowledge in cause-of-death increased the variability associated with parameter estimates when compared to the traditional approach. These differences between the two approaches can impact reported results, and therefore, it is critical to explicitly incorporate expert knowledge in statistical methods to ensure rigorous inference.

A computer-aided approach to detect the fetal behavioral states using multi-sensor Magnetocardiographic recordings.

PubMed

Vairavan, S; Ulusar, U D; Eswaran, H; Preissl, H; Wilson, J D; Mckelvey, S S; Lowery, C L; Govindan, R B

2016-02-01

We propose a novel computational approach to automatically identify the fetal heart rate patterns (fHRPs), which are reflective of sleep/awake states. By combining these patterns with presence or absence of movements, a fetal behavioral state (fBS) was determined. The expert scores were used as the gold standard and objective thresholds for the detection procedure were obtained using Receiver Operating Characteristics (ROC) analysis. To assess the performance, intraclass correlation was computed between the proposed approach and the mutually agreed expert scores. The detected fHRPs were then associated to their corresponding fBS based on the fetal movement obtained from fetal magnetocardiogaphic (fMCG) signals. This approach may aid clinicians in objectively assessing the fBS and monitoring fetal wellbeing. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Challenge-Feedback Learning Approach to Teaching International Business

ERIC Educational Resources Information Center

Sternad, Dietmar

2015-01-01

This article introduces a challenge-feedback learning (CFL) approach based on the goal-setting theory of human motivation, the deliberate practice theory of expert performance, and findings from the research on active and collaborative learning. The core of the teaching concept is the CFL cycle in which students repeatedly progress through four…

An expert system/ion trap mass spectrometry approach for life support systems monitoring

NASA Technical Reports Server (NTRS)

Palmer, Peter T.; Wong, Carla M.; Yost, Richard A.; Johnson, Jodie V.; Yates, Nathan A.; Story, Michael

1992-01-01

Efforts to develop sensor and control system technology to monitor air quality for life support have resulted in the development and preliminary testing of a concept based on expert systems and ion trap mass spectrometry (ITMS). An ITMS instrument provides the capability to identify and quantitate a large number of suspected contaminants at trace levels through the use of a variety of multidimensional experiments. An expert system provides specialized knowledge for control, analysis, and decision making. The system is intended for real-time, on-line, autonomous monitoring of air quality. The key characteristics of the system, performance data and analytical capabilities of the ITMS instrument, the design and operation of the expert system, and results from preliminary testing of the system for trace contaminant monitoring are described.

Scientific expertise and the Athlete Biological Passport: 3 years of experience.

PubMed

Schumacher, Yorck Olaf; d'Onofrio, Giuseppe

2012-06-01

Expert evaluation of biological data is a key component of the Athlete Biological Passport approach in the fight against doping. The evaluation consists of a longitudinal assessment of biological variables to determine the probability of the data being physiological on the basis of the athlete's on own previous values (performed by an automated software system using a Bayesian model) and a subjective evaluation of the results in view of possible causes (performed by experts). The role of the expert is therefore a key component in the process. Experts should be qualified to evaluate the data regarding possible explanations related to the influence of doping products and methods, analytical issues, and the influence of exercise or pathological conditions. The evaluation provides a scientific basis for the decision taken by a disciplinary panel. This evaluation should therefore encompass and balance all possible causes for a given blood profile and provide a likelihood for potential scenarios (pathology, normal variation, doping) that might have caused the pattern. It should comply with the standards for the evaluation of scientific evidence in forensics. On the basis of their evaluation of profiles, experts might provide assistance in planning appropriate target testing schemes.

Expert judgments about RD&D and the future of nuclear energy.

PubMed

Anadón, Laura D; Bosetti, Valentina; Bunn, Matthew; Catenacci, Michela; Lee, Audrey

2012-11-06

Probabilistic estimates of the cost and performance of future nuclear energy systems under different scenarios of government research, development, and demonstration (RD&D) spending were obtained from 30 U.S. and 30 European nuclear technology experts. We used a novel elicitation approach which combined individual and group elicitation. With no change from current RD&D funding levels, experts on average expected current (Gen. III/III+) designs to be somewhat more expensive in 2030 than they were in 2010, and they expected the next generation of designs (Gen. IV) to be more expensive still as of 2030. Projected costs of proposed small modular reactors (SMRs) were similar to those of Gen. IV systems. The experts almost unanimously recommended large increases in government support for nuclear RD&D (generally 2-3 times current spending). The majority expected that such RD&D would have only a modest effect on cost, but would improve performance in other areas, such as safety, waste management, and uranium resource utilization. The U.S. and E.U. experts were in relative agreement regarding how government RD&D funds should be allocated, placing particular focus on very high temperature reactors, sodium-cooled fast reactors, fuels and materials, and fuel cycle technologies.

An expert systems approach to automated fault management in a regenerative life support subsystem

NASA Technical Reports Server (NTRS)

Malin, J. T.; Lance, N., Jr.

1986-01-01

This paper describes FIXER, a prototype expert system for automated fault management in a regenerative life support subsystem typical of Space Station applications. The development project provided an evaluation of the use of expert systems technology to enhance controller functions in space subsystems. The software development approach permitted evaluation of the effectiveness of direct involvement of the expert in design and development. The approach also permitted intensive observation of the knowledge and methods of the expert. This paper describes the development of the prototype expert system and presents results of the evaluation.

Parameterization of the InVEST Crop Pollination Model to spatially predict abundance of wild blueberry (Vaccinium angustifolium Aiton) native bee pollinators in Maine, USA

USGS Publications Warehouse

Groff, Shannon C.; Loftin, Cynthia S.; Drummond, Frank; Bushmann, Sara; McGill, Brian J.

2016-01-01

Non-native honeybees historically have been managed for crop pollination, however, recent population declines draw attention to pollination services provided by native bees. We applied the InVEST Crop Pollination model, developed to predict native bee abundance from habitat resources, in Maine's wild blueberry crop landscape. We evaluated model performance with parameters informed by four approaches: 1) expert opinion; 2) sensitivity analysis; 3) sensitivity analysis informed model optimization; and, 4) simulated annealing (uninformed) model optimization. Uninformed optimization improved model performance by 29% compared to expert opinion-informed model, while sensitivity-analysis informed optimization improved model performance by 54%. This suggests that expert opinion may not result in the best parameter values for the InVEST model. The proportion of deciduous/mixed forest within 2000 m of a blueberry field also reliably predicted native bee abundance in blueberry fields, however, the InVEST model provides an efficient tool to estimate bee abundance beyond the field perimeter.

What has the study of digital games contributed to the science of expert behavior?

PubMed Central

Charness, Neil

2017-01-01

I review the historical context for modeling skilled performance in games. Using Newell’s (1990) concept of time bands for explaining cognitive behavior, I categorize the current papers in terms of time scales, type of data, and analysis methodologies. I discuss strengths and weaknesses of these approaches for describing skill acquisition and why the study of digital games can address the challenges of replication and generalizability. Cognitive science needs to pay closer attention to population representativeness to enhance generalizability of findings, and to the social band of explanation, in order to explain why so few individuals reach expert levels of performance. PMID:28176450

LEADER - An integrated engine behavior and design analyses based real-time fault diagnostic expert system for Space Shuttle Main Engine (SSME)

NASA Technical Reports Server (NTRS)

Gupta, U. K.; Ali, M.

1989-01-01

The LEADER expert system has been developed for automatic learning tasks encompassing real-time detection, identification, verification, and correction of anomalous propulsion system operations, using a set of sensors to monitor engine component performance to ascertain anomalies in engine dynamics and behavior. Two diagnostic approaches are embodied in LEADER's architecture: (1) learning and identifying engine behavior patterns to generate novel hypotheses about possible abnormalities, and (2) the direction of engine sensor data processing to perform resoning based on engine design and functional knowledge, as well as the principles of the relevant mechanics and physics.

Assistant for Analyzing Tropical-Rain-Mapping Radar Data

NASA Technical Reports Server (NTRS)

James, Mark

2006-01-01

A document is defined that describes an approach for a Tropical Rain Mapping Radar Data System (TDS). TDS is composed of software and hardware elements incorporating a two-frequency spaceborne radar system for measuring tropical precipitation. The TDS would be used primarily in generating data products for scientific investigations. The most novel part of the TDS would be expert-system software to aid in the selection of algorithms for converting raw radar-return data into such primary observables as rain rate, path-integrated rain rate, and surface backscatter. The expert-system approach would address the issue that selection of algorithms for processing the data requires a significant amount of preprocessing, non-intuitive reasoning, and heuristic application, making it infeasible, in many cases, to select the proper algorithm in real time. In the TDS, tentative selections would be made to enable conversions in real time. The expert system would remove straightforwardly convertible data from further consideration, and would examine ambiguous data, performing analysis in depth to determine which algorithms to select. Conversions performed by these algorithms, presumed to be correct, would be compared with the corresponding real-time conversions. Incorrect real-time conversions would be updated using the correct conversions.

[Methodology for the comprehensive evaluation of the quality of performance of activities of medical and social experts].

PubMed

Moskalenko, V F; Gorban', Ie M; Marunich, V V; Ipatov, A V; Sergiieni, O V

2001-01-01

The paper scientifically substantiates methodology, approaches, criteria, and control indices for assessment of activities of establishments of medical-and-social performance. Most indices for efficiency and certain indices for week points in the work of establishments of the service depend on interaction thereof with curative- and prophylactic institutions; the best results with the problem of prevention of disability and rehabilitation of invalids are supposed to be achieved through collaborative efforts. Other criteria and intermediate indices having an effect on the quality of activities reflect the resource- and trained personnel supplies of establishments of the service, amount of work, organizational measures designed to raise the quality of medical-and-social expert performance.

A Concentration Training Approach for the Movement Professional

ERIC Educational Resources Information Center

Mack, Mick

2009-01-01

Recent studies have shown that the learner's focus of attention is an important factor that influences motor skill learning. Furthermore, the ability to concentrate has been deemed one of the most important keys to effective sport performance and is essential to performing one's best. Other experts have concluded that concentration is indeed a…

The Genesis of Creative Greatness: Mini-C and the Expert Performance Approach

ERIC Educational Resources Information Center

Beghetto, Ronald A.; Kaufman, James C.

2007-01-01

The authors' recent theoretical work has focused on developing the construct of mini-c creativity and illustrating how all levels of creative performance follow a trajectory that starts with novel and personally meaningful interpretations (mini-c), which can then progress to intrapersonally judged novel and meaningful contributions (little-c) and…

Leveraging the crowd for annotation of retinal images.

PubMed

Leifman, George; Swedish, Tristan; Roesch, Karin; Raskar, Ramesh

2015-01-01

Medical data presents a number of challenges. It tends to be unstructured, noisy and protected. To train algorithms to understand medical images, doctors can label the condition associated with a particular image, but obtaining enough labels can be difficult. We propose an annotation approach which starts with a small pool of expertly annotated images and uses their expertise to rate the performance of crowd-sourced annotations. In this paper we demonstrate how to apply our approach for annotation of large-scale datasets of retinal images. We introduce a novel data validation procedure which is designed to cope with noisy ground-truth data and with non-consistent input from both experts and crowd-workers.

Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models

PubMed Central

2017-01-01

We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder–decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step toward solving the challenging problem of computational retrosynthetic analysis. PMID:29104927

Development of a practical approach to expert elicitation for randomised controlled trials with missing health outcomes: Application to the IMPROVE trial.

PubMed

Mason, Alexina J; Gomes, Manuel; Grieve, Richard; Ulug, Pinar; Powell, Janet T; Carpenter, James

2017-08-01

The analyses of randomised controlled trials with missing data typically assume that, after conditioning on the observed data, the probability of missing data does not depend on the patient's outcome, and so the data are 'missing at random' . This assumption is usually implausible, for example, because patients in relatively poor health may be more likely to drop out. Methodological guidelines recommend that trials require sensitivity analysis, which is best informed by elicited expert opinion, to assess whether conclusions are robust to alternative assumptions about the missing data. A major barrier to implementing these methods in practice is the lack of relevant practical tools for eliciting expert opinion. We develop a new practical tool for eliciting expert opinion and demonstrate its use for randomised controlled trials with missing data. We develop and illustrate our approach for eliciting expert opinion with the IMPROVE trial (ISRCTN 48334791), an ongoing multi-centre randomised controlled trial which compares an emergency endovascular strategy versus open repair for patients with ruptured abdominal aortic aneurysm. In the IMPROVE trial at 3 months post-randomisation, 21% of surviving patients did not complete health-related quality of life questionnaires (assessed by EQ-5D-3L). We address this problem by developing a web-based tool that provides a practical approach for eliciting expert opinion about quality of life differences between patients with missing versus complete data. We show how this expert opinion can define informative priors within a fully Bayesian framework to perform sensitivity analyses that allow the missing data to depend upon unobserved patient characteristics. A total of 26 experts, of 46 asked to participate, completed the elicitation exercise. The elicited quality of life scores were lower on average for the patients with missing versus complete data, but there was considerable uncertainty in these elicited values. The missing at random analysis found that patients randomised to the emergency endovascular strategy versus open repair had higher average (95% credible interval) quality of life scores of 0.062 (-0.005 to 0.130). Our sensitivity analysis that used the elicited expert information as pooled priors found that the gain in average quality of life for the emergency endovascular strategy versus open repair was 0.076 (-0.054 to 0.198). We provide and exemplify a practical tool for eliciting the expert opinion required by recommended approaches to the sensitivity analyses of randomised controlled trials. We show how this approach allows the trial analysis to fully recognise the uncertainty that arises from making alternative, plausible assumptions about the reasons for missing data. This tool can be widely used in the design, analysis and interpretation of future trials, and to facilitate this, materials are available for download.

An emerging paradigm: a strength-based approach to exploring mental imagery

PubMed Central

MacIntyre, Tadhg E.; Moran, Aidan P.; Collet, Christian; Guillot, Aymeric

2013-01-01

Mental imagery, or the ability to simulate in the mind information that is not currently perceived by the senses, has attracted considerable research interest in psychology since the early 1970's. Within the past two decades, research in this field—as in cognitive psychology more generally—has been dominated by neuroscientific methods that typically involve comparisons between imagery performance of participants from clinical populations with those who exhibit apparently normal cognitive functioning. Although this approach has been valuable in identifying key neural substrates of visual imagery, it has been less successful in understanding the possible mechanisms underlying another simulation process, namely, motor imagery or the mental rehearsal of actions without engaging in the actual movements involved. In order to address this oversight, a “strength-based” approach has been postulated which is concerned with understanding those on the high ability end of the imagery performance spectrum. Guided by the expert performance approach and principles of ecological validity, converging methods have the potential to enable imagery researchers to investigate the neural “signature” of elite performers, for example. Therefore, the purpose of this paper is to explain the origin, nature, and implications of the strength-based approach to mental imagery. Following a brief explanation of the background to this latter approach, we highlight some important theoretical advances yielded by recent research on mental practice, mental travel, and meta-imagery processes in expert athletes and dancers. Next, we consider the methodological implications of using a strength-based approach to investigate imagery processes. The implications for the field of motor cognition are outlined and specific research questions, in dynamic imagery, imagery perspective, measurement, multi-sensory imagery, and metacognition that may benefit from this approach in the future are sketched briefly. PMID:23554591

Clinical Assessment of Risk Management: an INtegrated Approach (CARMINA).

PubMed

Tricarico, Pierfrancesco; Tardivo, Stefano; Sotgiu, Giovanni; Moretti, Francesca; Poletti, Piera; Fiore, Alberto; Monturano, Massimo; Mura, Ida; Privitera, Gaetano; Brusaferro, Silvio

2016-08-08

Purpose - The European Union recommendations for patient safety calls for shared clinical risk management (CRM) safety standards able to guide organizations in CRM implementation. The purpose of this paper is to develop a self-evaluation tool to measure healthcare organization performance on CRM and guide improvements over time. Design/methodology/approach - A multi-step approach was implemented including: a systematic literature review; consensus meetings with an expert panel from eight Italian leader organizations to get to an agreement on the first version; field testing to test instrument feasibility and flexibility; Delphi strategy with a second expert panel for content validation and balanced scoring system development. Findings - The self-assessment tool - Clinical Assessment of Risk Management: an INtegrated Approach includes seven areas (governance, communication, knowledge and skills, safe environment, care processes, adverse event management, learning from experience) and 52 standards. Each standard is evaluated according to four performance levels: minimum; monitoring; outcomes; and improvement actions, which resulted in a feasible, flexible and valid instrument to be used throughout different organizations. Practical implications - This tool allows practitioners to assess their CRM activities compared to minimum levels, monitor performance, benchmarking with other institutions and spreading results to different stakeholders. Originality/value - The multi-step approach allowed us to identify core minimum CRM levels in a field where no consensus has been reached. Most standards may be easily adopted in other countries.

Building confidence and credibility into CAD with belief decision trees

NASA Astrophysics Data System (ADS)

Affenit, Rachael N.; Barns, Erik R.; Furst, Jacob D.; Rasin, Alexander; Raicu, Daniela S.

2017-03-01

Creating classifiers for computer-aided diagnosis in the absence of ground truth is a challenging problem. Using experts' opinions as reference truth is difficult because the variability in the experts' interpretations introduces uncertainty in the labeled diagnostic data. This uncertainty translates into noise, which can significantly affect the performance of any classifier on test data. To address this problem, we propose a new label set weighting approach to combine the experts' interpretations and their variability, as well as a selective iterative classification (SIC) approach that is based on conformal prediction. Using the NIH/NCI Lung Image Database Consortium (LIDC) dataset in which four radiologists interpreted the lung nodule characteristics, including the degree of malignancy, we illustrate the benefits of the proposed approach. Our results show that the proposed 2-label-weighted approach significantly outperforms the accuracy of the original 5- label and 2-label-unweighted classification approaches by 39.9% and 7.6%, respectively. We also found that the weighted 2-label models produce higher skewness values by 1.05 and 0.61 for non-SIC and SIC respectively on root mean square error (RMSE) distributions. When each approach was combined with selective iterative classification, this further improved the accuracy of classification for the 2-weighted-label by 7.5% over the original, and improved the skewness of the 5-label and 2-unweighted-label by 0.22 and 0.44 respectively.

Breakfast barriers and opportunities for children living in a Dutch disadvantaged neighbourhood.

PubMed

van Kleef, Ellen; Vingerhoeds, Monique H; Vrijhof, Milou; van Trijp, Hans C M

2016-12-01

The objective of this study was to explore parents', children's, and experts' beliefs and experiences about breakfast motivation, opportunity, and ability and elicit their thoughts on effective interventions to encourage healthy breakfast consumption. The setting was a disadvantaged neighbourhood in Rotterdam, the Netherlands. Focus groups with mothers and children and semi-structured individual interviews with experts were conducted. Interview guides were developed based on the motivation, opportunity, and ability consumer psychology model. Thirty-two mothers of primary school children participated in five group discussions, eight focus groups were conducted with 44 children, and nine experts participated in interviews. Data from expert interviews and group discussions were coded and thematically analysed. The following themes emerged from the focus groups: (1) generally high motivation to have breakfast, (2) improved performance at school is key motivator, (3) limited time hinders breakfast, and (4) lack of nutritional knowledge about high quality breakfast. Experts mentioned lack of effort, knowledge, and time; financial constraints; and environmental issues (food availability) as barriers to breakfasting healthily. Several ways to encourage healthy breakfasting habits were identified: (1) involvement of both children and parents, (2) role models inspiring change, and (3) interactive educational approaches. Experts perceived more problems and challenges in achieving healthy breakfast habits than did mothers and children. Lack of opportunity (according to the children and experts) and ability (according to the experts) were identified, although the motivation to eat a healthy breakfast was present. Predominant barriers are lack of time and nutritional knowledge. Overall, findings suggest educational and social marketing approaches as interventions to encourage healthy breakfast consumption. Copyright © 2016 Elsevier Ltd. All rights reserved.

Experts' opinions on the management of medically unexplained symptoms in primary care. A qualitative analysis of narrative reviews and scientific editorials.

PubMed

Heijmans, Mieke; Olde Hartman, Tim C; van Weel-Baumgarten, Evelyn; Dowrick, Christopher; Lucassen, Peter L B J; van Weel, Chris

2011-08-01

The feasibility as well as the suitability of several therapies for medically unexplained symptoms (MUS) in primary care applied by the family physician (FP) appeared to be low. FPs need effective and acceptable strategies to manage these functionally impaired patients. To review important and effective elements in the treatment of patients with MUS in primary care according to experts in MUS research. We performed a systematic search of narrative reviews and scientific editorials in Medline and PsycINFO and triangulated our findings by conducting a focus group with MUS experts. We included 7 scientific editorials and 23 narrative reviews. According to MUS experts, the most important elements in the treatment of MUS are creating a safe therapeutic environment, generic interventions (such as motivational interviewing, giving tangible explanations, reassurance and regularly scheduled appointments) and specific interventions (such as cognitive approaches and pharmacotherapy). Furthermore, MUS experts indicate that a multi-component approach in which these three important elements are combined are most helpful for patients with MUS. In contrast to most specific interventions, opinions of MUS experts regarding generic interventions and creating a safe therapeutic relationship seem to be more based on theory and experience than on quantitative research. MUS experts highlight the importance of generic interventions and doctor-patient communication and relationship. However, studies showing the effectiveness of these elements in the management of MUS in primary care is still scarce. Research as well as medical practice should focus more on these non-specific aspects of the medical consultation.

Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection.

PubMed

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

2017-01-01

Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models. Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system. Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines. The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports.

Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection

PubMed Central

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

2017-01-01

Objectives Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models. Methods Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system. Results Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines. Conclusion The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports. PMID:28166263

Co-Constructional Task Analysis: Moving beyond Adult-Based Models to Assess Young Children's Task Performance

ERIC Educational Resources Information Center

Lee, Scott Weng Fai

2013-01-01

The assessment of young children's thinking competence in task performances has typically followed the novice-to-expert regimen involving models of strategies that adults use when engaged in cognitive tasks such as problem-solving and decision-making. Socio-constructivists argue for a balanced pedagogical approach between the adult and child that…

The juvenile face as a suitable age indicator in child pornography cases: a pilot study on the reliability of automated and visual estimation approaches.

PubMed

Ratnayake, M; Obertová, Z; Dose, M; Gabriel, P; Bröker, H M; Brauckmann, M; Barkus, A; Rizgeliene, R; Tutkuviene, J; Ritz-Timme, S; Marasciuolo, L; Gibelli, D; Cattaneo, C

2014-09-01

In cases of suspected child pornography, the age of the victim represents a crucial factor for legal prosecution. The conventional methods for age estimation provide unreliable age estimates, particularly if teenage victims are concerned. In this pilot study, the potential of age estimation for screening purposes is explored for juvenile faces. In addition to a visual approach, an automated procedure is introduced, which has the ability to rapidly scan through large numbers of suspicious image data in order to trace juvenile faces. Age estimations were performed by experts, non-experts and the Demonstrator of a developed software on frontal facial images of 50 females aged 10-19 years from Germany, Italy, and Lithuania. To test the accuracy, the mean absolute error (MAE) between the estimates and the real ages was calculated for each examiner and the Demonstrator. The Demonstrator achieved the lowest MAE (1.47 years) for the 50 test images. Decreased image quality had no significant impact on the performance and classification results. The experts delivered slightly less accurate MAE (1.63 years). Throughout the tested age range, both the manual and the automated approach led to reliable age estimates within the limits of natural biological variability. The visual analysis of the face produces reasonably accurate age estimates up to the age of 18 years, which is the legally relevant age threshold for victims in cases of pedo-pornography. This approach can be applied in conjunction with the conventional methods for a preliminary age estimation of juveniles depicted on images.

Expert performance in sport and the dynamics of talent development.

PubMed

Phillips, Elissa; Davids, Keith; Renshaw, Ian; Portus, Marc

2010-04-01

Research on expertise, talent identification and development has tended to be mono-disciplinary, typically adopting genocentric or environmentalist positions, with an overriding focus on operational issues. In this paper, the validity of dualist positions on sport expertise is evaluated. It is argued that, to advance understanding of expertise and talent development, a shift towards a multidisciplinary and integrative science focus is necessary, along with the development of a comprehensive multidisciplinary theoretical rationale. Here we elucidate dynamical systems theory as a multidisciplinary theoretical rationale for capturing how multiple interacting constraints can shape the development of expert performers. This approach suggests that talent development programmes should eschew the notion of common optimal performance models, emphasize the individual nature of pathways to expertise, and identify the range of interacting constraints that impinge on performance potential of individual athletes, rather than evaluating current performance on physical tests referenced to group norms.

Estimating structural collapse fragility of generic building typologies using expert judgment

USGS Publications Warehouse

Jaiswal, Kishor S.; Wald, D.J.; Perkins, D.; Aspinall, W.P.; Kiremidjian, Anne S.; Deodatis, George; Ellingwood, Bruce R.; Frangopol, Dan M.

2014-01-01

The structured expert elicitation process proposed by Cooke (1991), hereafter referred to as Cooke’s approach, is applied for the first time in the realm of structural collapse-fragility assessment for selected generic construction types. Cooke’s approach works on the principle of objective calibration scoring of judgments coupled with hypothesis testing used in classical statistics. The performance-based scoring system reflects the combined measure of an expert’s informativeness about variables in the problem area under consideration, and their ability to enumerate, in a statistically accurate way through expressing their true beliefs, the quantitative uncertainties associated with their assessments. We summarize the findings of an expert elicitation workshop in which a dozen earthquake-engineering professionals from around the world were engaged to estimate seismic collapse fragility for generic construction types. Development of seismic collapse fragility functions was accomplished by combining their judgments using weights derived from Cooke’s method. Although substantial effort was needed to elicit the inputs of these experts successfully, we anticipate that the elicitation strategy described here will gain momentum in a wide variety of earthquake seismology and engineering hazard and risk analyses where physical model and data limitations are inherent and objective professional judgment can fill gaps.

What Has the Study of Digital Games Contributed to the Science of Expert Behavior?

PubMed

Charness, Neil

2017-04-01

I review the historical context for modeling skilled performance in games. Using Newell's (1990) concept of time bands for explaining cognitive behavior, I categorize the current papers in terms of time scales, type of data, and analysis methodologies. I discuss strengths and weaknesses of these approaches for describing skill acquisition and why the study of digital games can address the challenges of replication and generalizability. Cognitive science needs to pay closer attention to population representativeness to enhance generalizability of findings, and to the social band of explanation, in order to explain why so few individuals reach expert levels of performance. Copyright © 2017 Cognitive Science Society, Inc.

A Model-Based Expert System for Space Power Distribution Diagnostics

NASA Technical Reports Server (NTRS)

Quinn, Todd M.; Schlegelmilch, Richard F.

1994-01-01

When engineers diagnose system failures, they often use models to confirm system operation. This concept has produced a class of advanced expert systems that perform model-based diagnosis. A model-based diagnostic expert system for the Space Station Freedom electrical power distribution test bed is currently being developed at the NASA Lewis Research Center. The objective of this expert system is to autonomously detect and isolate electrical fault conditions. Marple, a software package developed at TRW, provides a model-based environment utilizing constraint suspension. Originally, constraint suspension techniques were developed for digital systems. However, Marple provides the mechanisms for applying this approach to analog systems such as the test bed, as well. The expert system was developed using Marple and Lucid Common Lisp running on a Sun Sparc-2 workstation. The Marple modeling environment has proved to be a useful tool for investigating the various aspects of model-based diagnostics. This report describes work completed to date and lessons learned while employing model-based diagnostics using constraint suspension within an analog system.

Identification and interpretation of patterns in rocket engine data: Artificial intelligence and neural network approaches

NASA Technical Reports Server (NTRS)

Ali, Moonis; Whitehead, Bruce; Gupta, Uday K.; Ferber, Harry

1989-01-01

This paper describes an expert system which is designed to perform automatic data analysis, identify anomalous events, and determine the characteristic features of these events. We have employed both artificial intelligence and neural net approaches in the design of this expert system. The artificial intelligence approach is useful because it provides (1) the use of human experts' knowledge of sensor behavior and faulty engine conditions in interpreting data; (2) the use of engine design knowledge and physical sensor locations in establishing relationships among the events of multiple sensors; (3) the use of stored analysis of past data of faulty engine conditions; and (4) the use of knowledge-based reasoning in distinguishing sensor failure from actual faults. The neural network approach appears promising because neural nets (1) can be trained on extremely noisy data and produce classifications which are more robust under noisy conditions than other classification techniques; (2) avoid the necessity of noise removal by digital filtering and therefore avoid the need to make assumptions about frequency bands or other signal characteristics of anomalous behavior; (3) can, in effect, generate their own feature detectors based on the characteristics of the sensor data used in training; and (4) are inherently parallel and therefore are potentially implementable in special-purpose parallel hardware.

Rapid systematic assessment of the detection and attribution of regional anthropogenic climate change

NASA Astrophysics Data System (ADS)

Stone, Dáithí A.; Hansen, Gerrit

2016-09-01

Despite being a well-established research field, the detection and attribution of observed climate change to anthropogenic forcing is not yet provided as a climate service. One reason for this is the lack of a methodology for performing tailored detection and attribution assessments on a rapid time scale. Here we develop such an approach, based on the translation of quantitative analysis into the "confidence" language employed in recent Assessment Reports of the Intergovernmental Panel on Climate Change. While its systematic nature necessarily ignores some nuances examined in detailed expert assessments, the approach nevertheless goes beyond most detection and attribution studies in considering contributors to building confidence such as errors in observational data products arising from sparse monitoring networks. When compared against recent expert assessments, the results of this approach closely match those of the existing assessments. Where there are small discrepancies, these variously reflect ambiguities in the details of what is being assessed, reveal nuances or limitations of the expert assessments, or indicate limitations of the accuracy of the sort of systematic approach employed here. Deployment of the method on 116 regional assessments of recent temperature and precipitation changes indicates that existing rules of thumb concerning the detectability of climate change ignore the full range of sources of uncertainty, most particularly the importance of adequate observational monitoring.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stone, Daithi A.; Hansen, Gerrit

Despite being a well-established research field, the detection and attribution of observed climate change to anthropogenic forcing is not yet provided as a climate service. One reason for this is the lack of a methodology for performing tailored detection and attribution assessments on a rapid time scale. Here we develop such an approach, based on the translation of quantitative analysis into the “confidence” language employed in recent Assessment Reports of the Intergovernmental Panel on Climate Change. While its systematic nature necessarily ignores some nuances examined in detailed expert assessments, the approach nevertheless goes beyond most detection and attribution studies inmore » considering contributors to building confidence such as errors in observational data products arising from sparse monitoring networks. When compared against recent expert assessments, the results of this approach closely match those of the existing assessments. Where there are small discrepancies, these variously reflect ambiguities in the details of what is being assessed, reveal nuances or limitations of the expert assessments, or indicate limitations of the accuracy of the sort of systematic approach employed here. Deployment of the method on 116 regional assessments of recent temperature and precipitation changes indicates that existing rules of thumb concerning the detectability of climate change ignore the full range of sources of uncertainty, most particularly the importance of adequate observational monitoring.« less

EXSPRT: An Expert Systems Approach to Computer-Based Adaptive Testing.

ERIC Educational Resources Information Center

Frick, Theodore W.; And Others

Expert systems can be used to aid decision making. A computerized adaptive test (CAT) is one kind of expert system, although it is not commonly recognized as such. A new approach, termed EXSPRT, was devised that combines expert systems reasoning and sequential probability ratio test stopping rules. EXSPRT-R uses random selection of test items,…

Building a case-based diet recommendation system without a knowledge engineer.

PubMed

Khan, Abdus Salam; Hoffmann, Achim

2003-02-01

We present a new approach to the effective development of menu construction systems that allow to automatically construct a menu that is strongly tailored to the individual requirements and food preferences of a client. In hospitals and other health care institutions dietitians develop diets for clients which need to change their eating habits. Many clients have special needs in regards to their medical conditions, cultural backgrounds, or special levels of nutrient requirements for better recovery from diseases or surgery, etc. Existing computer support for this task is insufficient-many diets are not specifically tailored for the client's needs or require substantial time of a dietitian to be manually developed. Our approach is based on case-based reasoning, an artificial intelligence technique that finds increasing entry into industrial practice. Our approach goes beyond the traditional case-based reasoning (CBR) approach by allowing an incremental improvement of the system's competency during routine use of the system. The improvement of the system takes place through a direct expert user-system interaction while the expert is accomplishing their tasks of constructing a diet for a given client. Whenever the system performs unsatisfactorily, the expert will need to modify the system-produced diet 'manually', i.e. by entering the desired modifications into the system. Our implemented system, menu construction using an incremental knowledge acquisition system (MIKAS), asks the expert for simple explanations for each of the manual actions he/she takes and incorporates the explanations automatically into its knowledge base (KB) so that the system will perform these manually conducted actions automatically at the next occasion. We present MIKAS and discuss the results of our case study. While still being a prototype, the senior clinical dietitian involved in our evaluation studies judges the approach to have considerable potential to improve the daily routine of hospital dietitians as well as to improve the average quality of the dietary advice given to patients within the limited available time for dietary consultations. Our approach opens up a new avenue towards building highly specialised CBR systems in a more cost-effective way. Hence, our approach promises to allow a significantly more widespread development and practical deployment of CBR systems in a large variety of application domains including many medical applications.

Web-based expert system for foundry pollution prevention

NASA Astrophysics Data System (ADS)

Moynihan, Gary P.

2004-02-01

Pollution prevention is a complex task. Many small foundries lack the in-house expertise to perform these tasks. Expert systems are a type of computer information system that incorporates artificial intelligence. As noted in the literature, they provide a means of automating specialized expertise. This approach may be further leveraged by implementing the expert system on the internet (or world-wide web). This will allow distribution of the expertise to a variety of geographically-dispersed foundries. The purpose of this research is to develop a prototype web-based expert system to support pollution prevention for the foundry industry. The prototype system identifies potential emissions for a specified process, and also provides recommendations for the prevention of these contaminants. The system is viewed as an initial step toward assisting the foundry industry in better meeting government pollution regulations, as well as improving operating efficiencies within these companies.

Space Transportation System Meteorological Expert

NASA Technical Reports Server (NTRS)

Beller, Arthur E.; Stafford, Sue P.

1987-01-01

The STS Meteorological Expert (STSMET) is a long-term project to acquire general Shuttle operational weather forecasting expertise specific to the launch locale, to apply it to Shuttle operational weather forecasting tasks at the Cape Canaveral Forecast Facility, and ultimately to provide an on-line real-time operational aid to the duty forecasters in performing their tasks. Particular attention is given to the development of an approach called scenario-based reasoning, with specific application to summer thunderstorms; this type of reasoning can also be applied to frontal weather phenomena, visibility including fog, and wind shear.

FHWA LTPP Guidelines for Measuring Bridge Approach Transitions Using Inertial Profilers

DOT National Transportation Integrated Search

2016-12-01

The bump at the end of the bridge has long been studied for highways and railways, yet experts from across the transportation industry continue to identify it as one of the most prevalent substructure factors affecting bridge performance. Often, ride...

A middle man approach to knowledge acquisition in expert systems

NASA Technical Reports Server (NTRS)

Jordan, Janice A.; Lin, Min-Jin; Mayer, Richard J.; Sterle, Mark E.

1990-01-01

The Weed Control Advisor (WCA) is a robust expert system that has been successfully implemented on an IBM AT class microcomputer in CLIPS. The goal of the WCA was to demonstrate the feasibility of providing an economical, efficient, user friendly system through which Texas rice producers could obtain expert level knowledge regarding herbicide application for weed control. During the development phase of the WCA, an improved knowledge acquisition method which we call the Middle Man Approach (MMA) was applied to facilitate the communication process between the domain experts and the knowledge engineer. The MMA served to circumvent the problems associated with the more traditional forms of knowledge acquisition by placing the Middle Man, a semi-expert in the problem domain with some computer expertise, at the site of system development. The middle man was able to contribute to system development in two major ways. First, the Middle Man had experience working in rice production and could assume many of the responsibilities normally performed by the domain experts such as explaining the background of the problem domain and determining the important relations. Second, the Middle Man was familiar with computers and worked closely with the system developers to update the rules after the domain experts reviewed the prototype, contribute to the help menus and explanation portions of the expert system, conduct the testing that is required to insure that the expert system gives the expected results answer questions in a timely way, help the knowledge engineer structure the domain knowledge into a useable form, and provide insight into the end user's profile which helped in the development of the simple user friendly interface. The final results were not only that both time expended and costs were greatly reduced by using the MMA, but the quality of the system was improved. This papa will introduce the WCA system and then discuss traditional knowledge acquisition along with some of the problems often associated with it, the MMA methodology, and its application to the WCA development.

The "expert patient" approach for non-communicable disease management in low and middle income settings: When the reality confronts the rhetoric.

PubMed

Xiao, Yue

2015-09-01

This paper seeks to explore the relevance between the Western "expert patient" rhetoric and the reality of non-communicable diseases (NCDs) control and management in low and middle income settings from the health sociological perspective. It firstly sets up a conceptual framework of the "expert patient" or the patient self-management approach, showing the rhetoric of the initiative in the developed countries. Then by examining the situation of NCDs control and management in low income settings, the paper tries to evaluate the possibilities of implementing the "expert patient" approach in these countries. Kober and Van Damme's study on the relevance of the "expert patient" for an HIV/AIDS program in low income settings is critically studied to show the relevance of the developed countries' rhetoric of the "expert patient" approach for the reality of developing countries. In addition, the MoPoTsyo diabetes peer educator program is analyzed to show the challenges faced by the low income countries in implementing patient self-management programs. Finally, applications of the expert patient approach in China are discussed as well, to remind us of the possible difficulties in introducing it into rural settings.

Development of a practical approach to expert elicitation for randomised controlled trials with missing health outcomes: Application to the IMPROVE trial

PubMed Central

Mason, Alexina J; Gomes, Manuel; Grieve, Richard; Ulug, Pinar; Powell, Janet T; Carpenter, James

2017-01-01

Background/aims: The analyses of randomised controlled trials with missing data typically assume that, after conditioning on the observed data, the probability of missing data does not depend on the patient’s outcome, and so the data are ‘missing at random’ . This assumption is usually implausible, for example, because patients in relatively poor health may be more likely to drop out. Methodological guidelines recommend that trials require sensitivity analysis, which is best informed by elicited expert opinion, to assess whether conclusions are robust to alternative assumptions about the missing data. A major barrier to implementing these methods in practice is the lack of relevant practical tools for eliciting expert opinion. We develop a new practical tool for eliciting expert opinion and demonstrate its use for randomised controlled trials with missing data. Methods: We develop and illustrate our approach for eliciting expert opinion with the IMPROVE trial (ISRCTN 48334791), an ongoing multi-centre randomised controlled trial which compares an emergency endovascular strategy versus open repair for patients with ruptured abdominal aortic aneurysm. In the IMPROVE trial at 3 months post-randomisation, 21% of surviving patients did not complete health-related quality of life questionnaires (assessed by EQ-5D-3L). We address this problem by developing a web-based tool that provides a practical approach for eliciting expert opinion about quality of life differences between patients with missing versus complete data. We show how this expert opinion can define informative priors within a fully Bayesian framework to perform sensitivity analyses that allow the missing data to depend upon unobserved patient characteristics. Results: A total of 26 experts, of 46 asked to participate, completed the elicitation exercise. The elicited quality of life scores were lower on average for the patients with missing versus complete data, but there was considerable uncertainty in these elicited values. The missing at random analysis found that patients randomised to the emergency endovascular strategy versus open repair had higher average (95% credible interval) quality of life scores of 0.062 (−0.005 to 0.130). Our sensitivity analysis that used the elicited expert information as pooled priors found that the gain in average quality of life for the emergency endovascular strategy versus open repair was 0.076 (−0.054 to 0.198). Conclusion: We provide and exemplify a practical tool for eliciting the expert opinion required by recommended approaches to the sensitivity analyses of randomised controlled trials. We show how this approach allows the trial analysis to fully recognise the uncertainty that arises from making alternative, plausible assumptions about the reasons for missing data. This tool can be widely used in the design, analysis and interpretation of future trials, and to facilitate this, materials are available for download. PMID:28675302

Methodological approach to moving nutritional science evidence into practice.

PubMed

Crawford, Cindy; Teo, Lynn; Elfenbaum, Pamela; Enslein, Viviane; Deuster, Patricia A; Berry, Kevin

2017-06-01

The Metabolically Optimized Brain study explored nutritional science believed to be ready to place into practice to help improve US service members' cognitive performance and, thereby, optimize mission-readiness. A transparent, step-wise, research approach was used for informing evidence-based decisions among and for various, diverse stakeholders. A steering committee and subject-matter experts convened to devise the protocol and independent systematic reviews were performed to determine the quality of the evidence for nutritional science in 4 areas relevant to military populations: (1) caffeinated foods and beverages; (2) omega-3 polyunsaturated fatty acids; (3) plant-based foods and beverages or their phytochemical constituents; and (4) whole dietary patterns. A research expert panel was asked to then recommend future research directions and solutions likely to benefit warfighters. An implementation expert panel further considered how to apply sound nutritional science in a cost-effective manner. This article summarizes the methodological processes, high-level results, global research recommendations, and priorities for implementation. Specific results of the individual dietary interventions, as well as recommendations for moving this field of research and practice forward, are detailed throughout the current supplement. © The Author(s) 2017. Published by Oxford University Press on behalf of the International Life Sciences Institute. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Public health nurses' supervision of clients in Norway.

PubMed

Tveiten, S; Severinsson, E

2005-09-01

The aim of this study was to explore and describe what public health nurses (PHNs) understand by client supervision and how they perform it. The main principles of the health promotion discourse initiated by the World Health Organization (WHO) over the last 20-30 years are client participation and the view of the client as expert. Supervision is one relevant intervention strategy in the empowerment process, in which these principles play a central role. There is a lack of research pertaining to the intervention models employed by PHNs. Twenty-three transcribed audiotaped dialogues between PHNs and their clients were analysed by means of qualitative content analysis. What the PHNs understand by supervision and how they perform it can be described by three themes: continuity in relationships and reflexivity in the supervision approach, communicating with the client about his/her needs, problems and worries; and the organization of client supervision. The PHNs in this study understand client supervision as communication and relationships with clients on the subject of a healthy lifestyle, child development and coping with everyday life. The PHNs' approach to client supervision seemed to include aspects of empowerment by means of client participation and the view of the client as expert. However, the PHNs themselves had an expert role.

A clinical decision support system for diagnosis of Allergic Rhinitis based on intradermal skin tests.

PubMed

Jabez Christopher, J; Khanna Nehemiah, H; Kannan, A

2015-10-01

Allergic Rhinitis is a universal common disease, especially in populated cities and urban areas. Diagnosis and treatment of Allergic Rhinitis will improve the quality of life of allergic patients. Though skin tests remain the gold standard test for diagnosis of allergic disorders, clinical experts are required for accurate interpretation of test outcomes. This work presents a clinical decision support system (CDSS) to assist junior clinicians in the diagnosis of Allergic Rhinitis. Intradermal Skin tests were performed on patients who had plausible allergic symptoms. Based on patient׳s history, 40 clinically relevant allergens were tested. 872 patients who had allergic symptoms were considered for this study. The rule based classification approach and the clinical test results were used to develop and validate the CDSS. Clinical relevance of the CDSS was compared with the Score for Allergic Rhinitis (SFAR). Tests were conducted for junior clinicians to assess their diagnostic capability in the absence of an expert. The class based Association rule generation approach provides a concise set of rules that is further validated by clinical experts. The interpretations of the experts are considered as the gold standard. The CDSS diagnoses the presence or absence of rhinitis with an accuracy of 88.31%. The allergy specialist and the junior clinicians prefer the rule based approach for its comprehendible knowledge model. The Clinical Decision Support Systems with rule based classification approach assists junior doctors and clinicians in the diagnosis of Allergic Rhinitis to make reliable decisions based on the reports of intradermal skin tests. Copyright © 2015 Elsevier Ltd. All rights reserved.

Selecting appropriate wastewater treatment technologies using a choosing-by-advantages approach.

PubMed

Arroyo, Paz; Molinos-Senante, María

2018-06-01

Selecting the most sustainable wastewater treatment (WWT) technology among possible alternatives is a very complex task because the choice must integrate economic, environmental, and social criteria. Traditionally, several multi-criteria decision-making approaches have been applied, with the most often used being the analytical hierarchical process (AHP). However, AHP allows users to offset poor environmental and/or social performance with low cost. To overcome this limitation, our study examines a choosing-by-advantages (CBA) approach to rank seven WWT technologies for secondary WWT. CBA results were compared with results obtained by using the AHP approach. The rankings of WWT alternatives differed, depending on whether the CBA or AHP approach was used, which highlights the importance of the method used to support decision-making processes, particularly ones that rely on subjective interpretations by experts. This paper uses a holistic perspective to demonstrate the benefits of using the CBA approach to support a decision-making process when a group of experts must come to a consensus in selecting the most suitable WWT technology among several available. Copyright © 2017 Elsevier B.V. All rights reserved.

Expectations, Observations, and the Cognitive Processes That Bind Them: Expert Assessment of Examinee Performance

ERIC Educational Resources Information Center

St-Onge, Christina; Chamberland, Martine; Lévesque, Annie; Varpio, Lara

2016-01-01

Performance-based assessment (PBA) is a valued assessment approach in medical education, be it in a clerkship, residency, or practice context. Raters are intrinsic to PBA and the increased use of PBA has lead to an increased interest in rater cognition. Although several researchers have tackled factors that may influence the variability in rater…

Comparison of climate envelope models developed using expert-selected variables versus statistical selection

USGS Publications Warehouse

Brandt, Laura A.; Benscoter, Allison; Harvey, Rebecca G.; Speroterra, Carolina; Bucklin, David N.; Romañach, Stephanie; Watling, James I.; Mazzotti, Frank J.

2017-01-01

Climate envelope models are widely used to describe potential future distribution of species under different climate change scenarios. It is broadly recognized that there are both strengths and limitations to using climate envelope models and that outcomes are sensitive to initial assumptions, inputs, and modeling methods Selection of predictor variables, a central step in modeling, is one of the areas where different techniques can yield varying results. Selection of climate variables to use as predictors is often done using statistical approaches that develop correlations between occurrences and climate data. These approaches have received criticism in that they rely on the statistical properties of the data rather than directly incorporating biological information about species responses to temperature and precipitation. We evaluated and compared models and prediction maps for 15 threatened or endangered species in Florida based on two variable selection techniques: expert opinion and a statistical method. We compared model performance between these two approaches for contemporary predictions, and the spatial correlation, spatial overlap and area predicted for contemporary and future climate predictions. In general, experts identified more variables as being important than the statistical method and there was low overlap in the variable sets (<40%) between the two methods Despite these differences in variable sets (expert versus statistical), models had high performance metrics (>0.9 for area under the curve (AUC) and >0.7 for true skill statistic (TSS). Spatial overlap, which compares the spatial configuration between maps constructed using the different variable selection techniques, was only moderate overall (about 60%), with a great deal of variability across species. Difference in spatial overlap was even greater under future climate projections, indicating additional divergence of model outputs from different variable selection techniques. Our work is in agreement with other studies which have found that for broad-scale species distribution modeling, using statistical methods of variable selection is a useful first step, especially when there is a need to model a large number of species or expert knowledge of the species is limited. Expert input can then be used to refine models that seem unrealistic or for species that experts believe are particularly sensitive to change. It also emphasizes the importance of using multiple models to reduce uncertainty and improve map outputs for conservation planning. Where outputs overlap or show the same direction of change there is greater certainty in the predictions. Areas of disagreement can be used for learning by asking why the models do not agree, and may highlight areas where additional on-the-ground data collection could improve the models.

Modification of Peyton's four-step approach for small group teaching - a descriptive study.

PubMed

Nikendei, Christoph; Huber, Julia; Stiepak, Jan; Huhn, Daniel; Lauter, Jan; Herzog, Wolfgang; Jünger, Jana; Krautter, Markus

2014-04-02

Skills-lab training as a methodological teaching approach is nowadays part of the training programs of almost all medical faculties. Specific ingredients have been shown to contribute to a successful learning experience in skills-labs. Although it is undoubted that the instructional approach used to introduce novel clinical technical skills to learners has a decisive impact on subsequent skills performance, as yet, little is known about differential effects of varying instructional methods. An instructional approach that is becoming increasingly prevalent in medical education is "Peyton's Four-Step Approach". As Peyton's Four Step Approach was designed for a 1:1 teacher : student ratio, the aim of the present study was to develop and evaluate a modified Peyton's Approach for small group teaching. The modified Peyton's Approach was applied in three skills-lab training sessions on IV catheter insertion, each with three first- or second year medical students (n = 9), delivered by three different skills-lab teachers. The presented descriptive study investigated the practicability and subjective impressions of skills-lab trainees and tutors. Skills-lab sessions were evaluated by trainees' self-assessment, expert ratings, and qualitative analysis of semi-standardized interviews conducted with trainees and tutors. The model was well accepted by trainees, and was rated as easy to realize, resulting in a good flow of teaching and success in attracting trainee's attention when observed by expert raters. Qualitative semi-standardized interviews performed with all of the trainees and tutors revealed that trainees valued repeated observation, instruction of trainees and the opportunity for independent performance, while tutors stressed that trainees were highly concentrated throughout the training and that they perceived repeated observation to be a valuable preparation for their own performance. The modified Peyton's Approach to instruct small groups of students in skills-lab training sessions has revealed to be practicable, well accepted by trainees, and easy for tutors to realize. Further research should address the realization of the model in larger skills-lab training groups.

ENHANCING TEST SENSITIVITY IN TOXICITY TESTING BY USING A STATISTICAL PERFORMANCE STANDARD

EPA Science Inventory

Previous reports have shown that within-test sensitivity can vary markedly among laboratories. Experts have advocated an empirical approach to controlling test variability based on the MSD, control means, and other test acceptability criteria. (The MSD represents the smallest dif...

Concurrent evolution of feature extractors and modular artificial neural networks

NASA Astrophysics Data System (ADS)

Hannak, Victor; Savakis, Andreas; Yang, Shanchieh Jay; Anderson, Peter

2009-05-01

This paper presents a new approach for the design of feature-extracting recognition networks that do not require expert knowledge in the application domain. Feature-Extracting Recognition Networks (FERNs) are composed of interconnected functional nodes (feurons), which serve as feature extractors, and are followed by a subnetwork of traditional neural nodes (neurons) that act as classifiers. A concurrent evolutionary process (CEP) is used to search the space of feature extractors and neural networks in order to obtain an optimal recognition network that simultaneously performs feature extraction and recognition. By constraining the hill-climbing search functionality of the CEP on specific parts of the solution space, i.e., individually limiting the evolution of feature extractors and neural networks, it was demonstrated that concurrent evolution is a necessary component of the system. Application of this approach to a handwritten digit recognition task illustrates that the proposed methodology is capable of producing recognition networks that perform in-line with other methods without the need for expert knowledge in image processing.

Natural and Artificial Intelligence in Neurosurgery: A Systematic Review.

PubMed

Senders, Joeky T; Arnaout, Omar; Karhade, Aditya V; Dasenbrock, Hormuzdiyar H; Gormley, William B; Broekman, Marike L; Smith, Timothy R

2017-09-07

Machine learning (ML) is a domain of artificial intelligence that allows computer algorithms to learn from experience without being explicitly programmed. To summarize neurosurgical applications of ML where it has been compared to clinical expertise, here referred to as "natural intelligence." A systematic search was performed in the PubMed and Embase databases as of August 2016 to review all studies comparing the performance of various ML approaches with that of clinical experts in neurosurgical literature. Twenty-three studies were identified that used ML algorithms for diagnosis, presurgical planning, or outcome prediction in neurosurgical patients. Compared to clinical experts, ML models demonstrated a median absolute improvement in accuracy and area under the receiver operating curve of 13% (interquartile range 4-21%) and 0.14 (interquartile range 0.07-0.21), respectively. In 29 (58%) of the 50 outcome measures for which a P -value was provided or calculated, ML models outperformed clinical experts ( P < .05). In 18 of 50 (36%), no difference was seen between ML and expert performance ( P > .05), while in 3 of 50 (6%) clinical experts outperformed ML models ( P < .05). All 4 studies that compared clinicians assisted by ML models vs clinicians alone demonstrated a better performance in the first group. We conclude that ML models have the potential to augment the decision-making capacity of clinicians in neurosurgical applications; however, significant hurdles remain associated with creating, validating, and deploying ML models in the clinical setting. Shifting from the preconceptions of a human-vs-machine to a human-and-machine paradigm could be essential to overcome these hurdles. Published by Oxford University Press on behalf of Congress of Neurological Surgeons 2017.

A Systematic Approach for Obtaining Performance on Matrix-Like Operations

NASA Astrophysics Data System (ADS)

Veras, Richard Michael

Scientific Computation provides a critical role in the scientific process because it allows us ask complex queries and test predictions that would otherwise be unfeasible to perform experimentally. Because of its power, Scientific Computing has helped drive advances in many fields ranging from Engineering and Physics to Biology and Sociology to Economics and Drug Development and even to Machine Learning and Artificial Intelligence. Common among these domains is the desire for timely computational results, thus a considerable amount of human expert effort is spent towards obtaining performance for these scientific codes. However, this is no easy task because each of these domains present their own unique set of challenges to software developers, such as domain specific operations, structurally complex data and ever-growing datasets. Compounding these problems are the myriads of constantly changing, complex and unique hardware platforms that an expert must target. Unfortunately, an expert is typically forced to reproduce their effort across multiple problem domains and hardware platforms. In this thesis, we demonstrate the automatic generation of expert level high-performance scientific codes for Dense Linear Algebra (DLA), Structured Mesh (Stencil), Sparse Linear Algebra and Graph Analytic. In particular, this thesis seeks to address the issue of obtaining performance on many complex platforms for a certain class of matrix-like operations that span across many scientific, engineering and social fields. We do this by automating a method used for obtaining high performance in DLA and extending it to structured, sparse and scale-free domains. We argue that it is through the use of the underlying structure found in the data from these domains that enables this process. Thus, obtaining performance for most operations does not occur in isolation of the data being operated on, but instead depends significantly on the structure of the data.

Standardizing bimanual vaginal examination using cognitive task analysis.

PubMed

Plumptre, Isabella; Mulki, Omar; Granados, Alejandro; Gayle, Claudine; Ahmed, Shahla; Low-Beer, Naomi; Higham, Jenny; Bello, Fernando

2017-10-01

To create a standardized universal list of procedural steps for bimanual vaginal examination (BVE) for teaching, assessment, and simulator development. This observational study, conducted from June-July 2012 and July-December 2014, collected video data of 10 expert clinicians performing BVE in a nonclinical environment. Video data were analyzed to produce a cognitive task analysis (CTA) of the examination steps performed. The CTA was further refined through structured interviews to make it suitable for teaching or assessment. It was validated through its use as a procedural examination checklist to rate expert clinician performance. BVE was deconstructed into 88 detailed steps outlining the complete examination process. These initial 88 steps were reduced to 35 by focusing on the unseen internal examination, then further refined through interviews with five experts into 30 essential procedural steps, five of which are additional steps if pathology is suspected. Using the CTA as a procedural checklist, the mean number of steps performed and/or verbalized was 21.6 ± 3.12 (72% ± 10.4%; range, 15.9-27.9, 53%-93%). This approach identified 30 essential steps for performing BVE, producing a new technique and standardized tool for teaching, assessment, and simulator development. © 2017 International Federation of Gynecology and Obstetrics.

Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models.

PubMed

Garibaldi, Jonathan M; Zhou, Shang-Ming; Wang, Xiao-Ying; John, Robert I; Ellis, Ian O

2012-06-01

It has been often demonstrated that clinicians exhibit both inter-expert and intra-expert variability when making difficult decisions. In contrast, the vast majority of computerized models that aim to provide automated support for such decisions do not explicitly recognize or replicate this variability. Furthermore, the perfect consistency of computerized models is often presented as a de facto benefit. In this paper, we describe a novel approach to incorporate variability within a fuzzy inference system using non-stationary fuzzy sets in order to replicate human variability. We apply our approach to a decision problem concerning the recommendation of post-operative breast cancer treatment; specifically, whether or not to administer chemotherapy based on assessment of five clinical variables: NPI (the Nottingham Prognostic Index), estrogen receptor status, vascular invasion, age and lymph node status. In doing so, we explore whether such explicit modeling of variability provides any performance advantage over a more conventional fuzzy approach, when tested on a set of 1310 unselected cases collected over a fourteen year period at the Nottingham University Hospitals NHS Trust, UK. The experimental results show that the standard fuzzy inference system (that does not model variability) achieves overall agreement to clinical practice around 84.6% (95% CI: 84.1-84.9%), while the non-stationary fuzzy model can significantly increase performance to around 88.1% (95% CI: 88.0-88.2%), p<0.001. We conclude that non-stationary fuzzy models provide a valuable new approach that may be applied to clinical decision support systems in any application domain. Copyright © 2012 Elsevier Inc. All rights reserved.

Toward a Model of Expert Knowledge Structure and Their Role in Cognitive Task Performance

DTIC Science & Technology

1993-11-01

ONR. I I ’Ibis project addressed the role of knowledge organization in skilled cognitive task performance. In particular, this work focused on three...aspects. I EDETERMKNOWLEDGES STRUCTURE MEASURES Figure 1. Knowledge Structure Domain. The work performed under this contract is divided into three...for knowledge structure development would lead to a more global training approach. As such, this effort attempted to define the scope of further work

Industrial knowledge design: an approach for designing information artifacts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schatz, Sae; Berking, Peter; Raybourn, Elaine M.

In this study, the authors define a new approach that addresses the challenge of efficiently designing informational artefacts for optimal knowledge acquisition, an important issue in cognitive ergonomics. Termed Industrial Knowledge Design (or InK'D), it draws from information-related (e.g. informatics) and neurosciences-related (e.g. neuroergonomics) disciplines. Although it can be used for a broad scope of communication-driven business functions, our focus as learning professionals is on conveying knowledge for purposes of training, education, and performance support. This paper discusses preliminary principles of InK'D practice that can be employed to maximise the quality and quantity of transferred knowledge through interaction design. Themore » paper codifies tacit knowledge into explicit concepts that can be leveraged by expert and non-expert knowledge designers alike.« less

Industrial knowledge design: an approach for designing information artifacts

DOE PAGES

Schatz, Sae; Berking, Peter; Raybourn, Elaine M.

2017-01-19

In this study, the authors define a new approach that addresses the challenge of efficiently designing informational artefacts for optimal knowledge acquisition, an important issue in cognitive ergonomics. Termed Industrial Knowledge Design (or InK'D), it draws from information-related (e.g. informatics) and neurosciences-related (e.g. neuroergonomics) disciplines. Although it can be used for a broad scope of communication-driven business functions, our focus as learning professionals is on conveying knowledge for purposes of training, education, and performance support. This paper discusses preliminary principles of InK'D practice that can be employed to maximise the quality and quantity of transferred knowledge through interaction design. Themore » paper codifies tacit knowledge into explicit concepts that can be leveraged by expert and non-expert knowledge designers alike.« less

The influence of expert opinions on the selection of wastewater treatment alternatives: a group decision-making approach.

PubMed

Kalbar, Pradip P; Karmakar, Subhankar; Asolekar, Shyam R

2013-10-15

The application of multiple-attribute decision-making (MADM) to real life decision problems suggests that avoiding the loss of information through scenario-based approaches and including expert opinions in the decision-making process are two major challenges that require more research efforts. Recently, a wastewater treatment technology selection effort has been made with a 'scenario-based' method of MADM. This paper focuses on a novel approach to incorporate expert opinions into the scenario-based decision-making process, as expert opinions play a major role in the selection of treatment technologies. The sets of criteria and the indicators that are used consist of both qualitative and quantitative criteria. The group decision-making (GDM) approach that is implemented for aggregating expert opinions is based on an analytical hierarchy process (AHP), which is the most widely used MADM method. The pairwise comparison matrices (PCMs) for qualitative criteria are formed based on expert opinions, whereas, a novel approach is proposed for generating PCMs for quantitative criteria. It has been determined that the experts largely prefer natural treatment systems because they are more sustainable in any scenario. However, PCMs based on expert opinions suggest that advanced technologies such as the sequencing batch reactor (SBR) can also be appropriate for a given decision scenario. The proposed GDM approach is a rationalized process that will be more appropriate in realistic scenarios where multiple stakeholders with local and regional societal priorities are involved in the selection of treatment technology. Copyright © 2013 Elsevier Ltd. All rights reserved.

Getting Inside the Expert's Head: An Analysis of Physician Cognitive Processes During Trauma Resuscitations.

PubMed

White, Matthew R; Braund, Heather; Howes, Daniel; Egan, Rylan; Gegenfurtner, Andreas; van Merrienboer, Jeroen J G; Szulewski, Adam

2018-04-23

Crisis resource management skills are integral to leading the resuscitation of a critically ill patient. Despite their importance, crisis resource management skills (and their associated cognitive processes) have traditionally been difficult to study in the real world. The objective of this study was to derive key cognitive processes underpinning expert performance in resuscitation medicine, using a new eye-tracking-based video capture method during clinical cases. During an 18-month period, a sample of 10 trauma resuscitations led by 4 expert trauma team leaders was analyzed. The physician team leaders were outfitted with mobile eye-tracking glasses for each case. After each resuscitation, participants were debriefed with a modified cognitive task analysis, based on a cued-recall protocol, augmented by viewing their own first-person perspective eye-tracking video from the clinical encounter. Eye-tracking technology was successfully applied as a tool to aid in the qualitative analysis of expert performance in a clinical setting. All participants stated that using these methods helped uncover previously unconscious aspects of their cognition. Overall, 5 major themes were derived from the interviews: logistic awareness, managing uncertainty, visual fixation behaviors, selective attendance to information, and anticipatory behaviors. The novel approach of cognitive task analysis augmented by eye tracking allowed the derivation of 5 unique cognitive processes underpinning expert performance in leading a resuscitation. An understanding of these cognitive processes has the potential to enhance educational methods and to create new assessment modalities of these previously tacit aspects of expertise in this field. Copyright © 2018 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.

An intelligent tutoring system for space shuttle diagnosis

NASA Technical Reports Server (NTRS)

Johnson, William B.; Norton, Jeffrey E.; Duncan, Phillip C.

1988-01-01

An Intelligent Tutoring System (ITS) transcends conventional computer-based instruction. An ITS is capable of monitoring and understanding student performance thereby providing feedback, explanation, and remediation. This is accomplished by including models of the student, the instructor, and the expert technician or operator in the domain of interest. The space shuttle fuel cell is the technical domain for the project described below. One system, Microcomputer Intelligence for Technical Training (MITT), demonstrates that ITS's can be developed and delivered, with a reasonable amount of effort and in a short period of time, on a microcomputer. The MITT system capitalizes on the diagnostic training approach called Framework for Aiding the Understanding of Logical Troubleshooting (FAULT) (Johnson, 1987). The system's embedded procedural expert was developed with NASA's C-Language Integrated Production (CLIP) expert system shell (Cubert, 1987).

Agreement, the F-Measure, and Reliability in Information Retrieval

PubMed Central

Hripcsak, George; Rothschild, Adam S.

2005-01-01

Information retrieval studies that involve searching the Internet or marking phrases usually lack a well-defined number of negative cases. This prevents the use of traditional interrater reliability metrics like the κ statistic to assess the quality of expert-generated gold standards. Such studies often quantify system performance as precision, recall, and F-measure, or as agreement. It can be shown that the average F-measure among pairs of experts is numerically identical to the average positive specific agreement among experts and that κ approaches these measures as the number of negative cases grows large. Positive specific agreement—or the equivalent F-measure—may be an appropriate way to quantify interrater reliability and therefore to assess the reliability of a gold standard in these studies. PMID:15684123

[Study on expert system of infrared spectral characteristic of combustible smoke agent].

PubMed

Song, Dong-ming; Guan, Hua; Hou, Wei; Pan, Gong-pei

2009-05-01

The present paper studied the application of expert system in prediction of infrared spectral characteristic of combustible anti-infrared smoke agent. The construction of the expert system was founded, based on the theory of minimum free energy and infrared spectral addition. After the direction of smoke agent was input, the expert system could figure out the final combustion products. Then infrared spectrogram of smoke could also be simulated by adding the spectra of all of the combustion products. Meanwhile, the screening index of smoke was provided in the wave bands of 3-5 im and 8-14 microm. FTIR spectroscope was used to investigate the performance of one kind of HC smoke. The combustion products calculated by the expert system were coincident with the actual data, and the simulant infrared spectrum was also similar to the real one of the smoke. The screening index given by the system was consistent with the known facts. It was showed that a new approach was offered for the fast discrimination of varieties of directions of smoke agent.

Validation of a systems-actuarial computer process for multidimensional classification of child psychopathology.

PubMed

McDermott, P A; Hale, R L

1982-07-01

Tested diagnostic classifications of child psychopathology produced by a computerized technique known as multidimensional actuarial classification (MAC) against the criterion of expert psychological opinion. The MAC program applies series of statistical decision rules to assess the importance of and relationships among several dimensions of classification, i.e., intellectual functioning, academic achievement, adaptive behavior, and social and behavioral adjustment, to perform differential diagnosis of children's mental retardation, specific learning disabilities, behavioral and emotional disturbance, possible communication or perceptual-motor impairment, and academic under- and overachievement in reading and mathematics. Classifications rendered by MAC are compared to those offered by two expert child psychologists for cases of 73 children referred for psychological services. Experts' agreement with MAC was significant for all classification areas, as was MAC's agreement with the experts held as a conjoint reference standard. Whereas the experts' agreement with MAC averaged 86.0% above chance, their agreement with one another averaged 76.5% above chance. Implications of the findings are explored and potential advantages of the systems-actuarial approach are discussed.

Rapid systematic assessment of the detection and attribution of regional anthropogenic climate change

DOE PAGES

Stone, Daithi A.; Hansen, Gerrit

2015-11-21

Despite being a well-established research field, the detection and attribution of observed climate change to anthropogenic forcing is not yet provided as a climate service. One reason for this is the lack of a methodology for performing tailored detection and attribution assessments on a rapid time scale. Here we develop such an approach, based on the translation of quantitative analysis into the “confidence” language employed in recent Assessment Reports of the Intergovernmental Panel on Climate Change. While its systematic nature necessarily ignores some nuances examined in detailed expert assessments, the approach nevertheless goes beyond most detection and attribution studies inmore » considering contributors to building confidence such as errors in observational data products arising from sparse monitoring networks. When compared against recent expert assessments, the results of this approach closely match those of the existing assessments. Where there are small discrepancies, these variously reflect ambiguities in the details of what is being assessed, reveal nuances or limitations of the expert assessments, or indicate limitations of the accuracy of the sort of systematic approach employed here. Deployment of the method on 116 regional assessments of recent temperature and precipitation changes indicates that existing rules of thumb concerning the detectability of climate change ignore the full range of sources of uncertainty, most particularly the importance of adequate observational monitoring.« less

Reliability and performance evaluation of systems containing embedded rule-based expert systems

NASA Technical Reports Server (NTRS)

Beaton, Robert M.; Adams, Milton B.; Harrison, James V. A.

1989-01-01

A method for evaluating the reliability of real-time systems containing embedded rule-based expert systems is proposed and investigated. It is a three stage technique that addresses the impact of knowledge-base uncertainties on the performance of expert systems. In the first stage, a Markov reliability model of the system is developed which identifies the key performance parameters of the expert system. In the second stage, the evaluation method is used to determine the values of the expert system's key performance parameters. The performance parameters can be evaluated directly by using a probabilistic model of uncertainties in the knowledge-base or by using sensitivity analyses. In the third and final state, the performance parameters of the expert system are combined with performance parameters for other system components and subsystems to evaluate the reliability and performance of the complete system. The evaluation method is demonstrated in the context of a simple expert system used to supervise the performances of an FDI algorithm associated with an aircraft longitudinal flight-control system.

Space shuttle onboard navigation console expert/trainer system

NASA Technical Reports Server (NTRS)

Wang, Lui; Bochsler, Dan

1987-01-01

A software system for use in enhancing operational performance as well as training ground controllers in monitoring onboard Space Shuttle navigation sensors is described. The Onboard Navigation (ONAV) development reflects a trend toward following a structured and methodical approach to development. The ONAV system must deal with integrated conventional and expert system software, complex interfaces, and implementation limitations due to the target operational environment. An overview of the onboard navigation sensor monitoring function is presented, along with a description of guidelines driving the development effort, requirements that the system must meet, current progress, and future efforts.

The weighted priors approach for combining expert opinions in logistic regression experiments

DOE PAGES

Quinlan, Kevin R.; Anderson-Cook, Christine M.; Myers, Kary L.

2017-04-24

When modeling the reliability of a system or component, it is not uncommon for more than one expert to provide very different prior estimates of the expected reliability as a function of an explanatory variable such as age or temperature. Our goal in this paper is to incorporate all information from the experts when choosing a design about which units to test. Bayesian design of experiments has been shown to be very successful for generalized linear models, including logistic regression models. We use this approach to develop methodology for the case where there are several potentially non-overlapping priors under consideration.more » While multiple priors have been used for analysis in the past, they have never been used in a design context. The Weighted Priors method performs well for a broad range of true underlying model parameter choices and is more robust when compared to other reasonable design choices. Finally, we illustrate the method through multiple scenarios and a motivating example. Additional figures for this article are available in the online supplementary information.« less

The weighted priors approach for combining expert opinions in logistic regression experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Quinlan, Kevin R.; Anderson-Cook, Christine M.; Myers, Kary L.

When modeling the reliability of a system or component, it is not uncommon for more than one expert to provide very different prior estimates of the expected reliability as a function of an explanatory variable such as age or temperature. Our goal in this paper is to incorporate all information from the experts when choosing a design about which units to test. Bayesian design of experiments has been shown to be very successful for generalized linear models, including logistic regression models. We use this approach to develop methodology for the case where there are several potentially non-overlapping priors under consideration.more » While multiple priors have been used for analysis in the past, they have never been used in a design context. The Weighted Priors method performs well for a broad range of true underlying model parameter choices and is more robust when compared to other reasonable design choices. Finally, we illustrate the method through multiple scenarios and a motivating example. Additional figures for this article are available in the online supplementary information.« less

A mathematical framework for combining decisions of multiple experts toward accurate and remote diagnosis of malaria using tele-microscopy.

PubMed

Mavandadi, Sam; Feng, Steve; Yu, Frank; Dimitrov, Stoyan; Nielsen-Saines, Karin; Prescott, William R; Ozcan, Aydogan

2012-01-01

We propose a methodology for digitally fusing diagnostic decisions made by multiple medical experts in order to improve accuracy of diagnosis. Toward this goal, we report an experimental study involving nine experts, where each one was given more than 8,000 digital microscopic images of individual human red blood cells and asked to identify malaria infected cells. The results of this experiment reveal that even highly trained medical experts are not always self-consistent in their diagnostic decisions and that there exists a fair level of disagreement among experts, even for binary decisions (i.e., infected vs. uninfected). To tackle this general medical diagnosis problem, we propose a probabilistic algorithm to fuse the decisions made by trained medical experts to robustly achieve higher levels of accuracy when compared to individual experts making such decisions. By modelling the decisions of experts as a three component mixture model and solving for the underlying parameters using the Expectation Maximisation algorithm, we demonstrate the efficacy of our approach which significantly improves the overall diagnostic accuracy of malaria infected cells. Additionally, we present a mathematical framework for performing 'slide-level' diagnosis by using individual 'cell-level' diagnosis data, shedding more light on the statistical rules that should govern the routine practice in examination of e.g., thin blood smear samples. This framework could be generalized for various other tele-pathology needs, and can be used by trained experts within an efficient tele-medicine platform.

Development of Type 2 Diabetes Mellitus Phenotyping Framework Using Expert Knowledge and Machine Learning Approach.

PubMed

Kagawa, Rina; Kawazoe, Yoshimasa; Ida, Yusuke; Shinohara, Emiko; Tanaka, Katsuya; Imai, Takeshi; Ohe, Kazuhiko

2017-07-01

Phenotyping is an automated technique that can be used to distinguish patients based on electronic health records. To improve the quality of medical care and advance type 2 diabetes mellitus (T2DM) research, the demand for T2DM phenotyping has been increasing. Some existing phenotyping algorithms are not sufficiently accurate for screening or identifying clinical research subjects. We propose a practical phenotyping framework using both expert knowledge and a machine learning approach to develop 2 phenotyping algorithms: one is for screening; the other is for identifying research subjects. We employ expert knowledge as rules to exclude obvious control patients and machine learning to increase accuracy for complicated patients. We developed phenotyping algorithms on the basis of our framework and performed binary classification to determine whether a patient has T2DM. To facilitate development of practical phenotyping algorithms, this study introduces new evaluation metrics: area under the precision-sensitivity curve (AUPS) with a high sensitivity and AUPS with a high positive predictive value. The proposed phenotyping algorithms based on our framework show higher performance than baseline algorithms. Our proposed framework can be used to develop 2 types of phenotyping algorithms depending on the tuning approach: one for screening, the other for identifying research subjects. We develop a novel phenotyping framework that can be easily implemented on the basis of proper evaluation metrics, which are in accordance with users' objectives. The phenotyping algorithms based on our framework are useful for extraction of T2DM patients in retrospective studies.

Computerized summary scoring: crowdsourcing-based latent semantic analysis.

PubMed

Li, Haiying; Cai, Zhiqiang; Graesser, Arthur C

2017-11-03

In this study we developed and evaluated a crowdsourcing-based latent semantic analysis (LSA) approach to computerized summary scoring (CSS). LSA is a frequently used mathematical component in CSS, where LSA similarity represents the extent to which the to-be-graded target summary is similar to a model summary or a set of exemplar summaries. Researchers have proposed different formulations of the model summary in previous studies, such as pregraded summaries, expert-generated summaries, or source texts. The former two methods, however, require substantial human time, effort, and costs in order to either grade or generate summaries. Using source texts does not require human effort, but it also does not predict human summary scores well. With human summary scores as the gold standard, in this study we evaluated the crowdsourcing LSA method by comparing it with seven other LSA methods that used sets of summaries from different sources (either experts or crowdsourced) of differing quality, along with source texts. Results showed that crowdsourcing LSA predicted human summary scores as well as expert-good and crowdsourcing-good summaries, and better than the other methods. A series of analyses with different numbers of crowdsourcing summaries demonstrated that the number (from 10 to 100) did not significantly affect performance. These findings imply that crowdsourcing LSA is a promising approach to CSS, because it saves human effort in generating the model summary while still yielding comparable performance. This approach to small-scale CSS provides a practical solution for instructors in courses, and also advances research on automated assessments in which student responses are expected to semantically converge on subject matter content.

Breaking new ground in the mind: an initial study of mental brittle transformation and mental rigid rotation in science experts.

PubMed

Resnick, Ilyse; Shipley, Thomas F

2013-05-01

The current study examines the spatial skills employed in different spatial reasoning tasks, by asking how science experts who are practiced in different types of visualizations perform on different spatial tasks. Specifically, the current study examines the varieties of mental transformations. We hypothesize that there may be two broad classes of mental transformations: rigid body mental transformations and non-rigid mental transformations. We focus on the disciplines of geology and organic chemistry because different types of transformations are central to the two disciplines: While geologists and organic chemists may both confront rotation in the practice of their profession, only geologists confront brittle transformations. A new instrument was developed to measure mental brittle transformation (visualizing breaking). Geologists and organic chemists performed similarly on a measure of mental rotation, while geologists outperformed organic chemists on the mental brittle transformation test. The differential pattern of skill on the two tests for the two groups of experts suggests that mental brittle transformation and mental rotation are different spatial skills. The roles of domain general cognitive resources (attentional control, spatial working memory, and perceptual filling in) and strategy in completing mental brittle transformation are discussed. The current study illustrates how ecological and interdisciplinary approaches complement traditional cognitive science to offer a comprehensive approach to understanding the nature of spatial thinking.

Detecting ‘Wrong Blood in Tube’ Errors: Evaluation of a Bayesian Network Approach

PubMed Central

Doctor, Jason N.; Strylewicz, Greg

2010-01-01

Objective In an effort to address the problem of laboratory errors, we develop and evaluate a method to detect mismatched specimens from nationally collected blood laboratory data in two experiments. Methods In Experiment 1 and 2 using blood labs from National Health and Nutrition Examination Survey (NHANES) and values derived from the Diabetes Prevention Program (DPP) respectively, a proportion of glucose and HbA1c specimens were randomly mismatched. A Bayesian network that encoded probabilistic relationships among analytes was used to predict mismatches. In Experiment 1 the performance of the network was compared against existing error detection software. In Experiment 2 the network was compared against 11 human experts recruited from the American Academy of Clinical Chemists. Results were compared via area under the receiver-operating characteristics curves (AUCs) and with agreement statistics. Results In Experiment 1 the network was most predictive of mismatches that produced clinically significant discrepancies between true and mismatched scores ((AUC of 0.87 (±0.04) for HbA1c and 0.83 (±0.02) for glucose), performed well in identifying errors among those self-reporting diabetes (N = 329) (AUC = 0.79 (± 0.02)) and performed significantly better than the established approach it was tested against (in all cases p < .0.05). In Experiment 2 it performed better (and in no case worse) than 7 of the 11 human experts. Average percent agreement was 0.79. and Kappa (κ) was 0.59, between experts and the Bayesian network. Conclusions Bayesian network can accurately identify mismatched specimens. The algorithm is best at identifying mismatches that result in a clinically significant magnitude of error. PMID:20566275

Thermal Expert System (TEXSYS): Systems autonomy demonstration project, volume 2. Results

NASA Technical Reports Server (NTRS)

Glass, B. J. (Editor)

1992-01-01

The Systems Autonomy Demonstration Project (SADP) produced a knowledge-based real-time control system for control and fault detection, isolation, and recovery (FDIR) of a prototype two-phase Space Station Freedom external active thermal control system (EATCS). The Thermal Expert System (TEXSYS) was demonstrated in recent tests to be capable of reliable fault anticipation and detection, as well as ordinary control of the thermal bus. Performance requirements were addressed by adopting a hierarchical symbolic control approach-layering model-based expert system software on a conventional, numerical data acquisition and control system. The model-based reasoning capabilities of TEXSYS were shown to be advantageous over typical rule-based expert systems, particularly for detection of unforeseen faults and sensor failures. Volume 1 gives a project overview and testing highlights. Volume 2 provides detail on the EATCS testbed, test operations, and online test results. Appendix A is a test archive, while Appendix B is a compendium of design and user manuals for the TEXSYS software.

Thermal Expert System (TEXSYS): Systems automony demonstration project, volume 1. Overview

NASA Technical Reports Server (NTRS)

Glass, B. J. (Editor)

1992-01-01

The Systems Autonomy Demonstration Project (SADP) produced a knowledge-based real-time control system for control and fault detection, isolation, and recovery (FDIR) of a prototype two-phase Space Station Freedom external active thermal control system (EATCS). The Thermal Expert System (TEXSYS) was demonstrated in recent tests to be capable of reliable fault anticipation and detection, as well as ordinary control of the thermal bus. Performance requirements were addressed by adopting a hierarchical symbolic control approach-layering model-based expert system software on a conventional, numerical data acquisition and control system. The model-based reasoning capabilities of TEXSYS were shown to be advantageous over typical rule-based expert systems, particularly for detection of unforeseen faults and sensor failures. Volume 1 gives a project overview and testing highlights. Volume 2 provides detail on the EATCS test bed, test operations, and online test results. Appendix A is a test archive, while Appendix B is a compendium of design and user manuals for the TEXSYS software.

Development of a multimedia tutorial to educate how to assess the critical view of safety in laparoscopic cholecystectomy using expert review and crowd-sourcing.

PubMed

Deal, Shanley B; Stefanidis, Dimitrios; Brunt, L Michael; Alseidi, Adnan

2017-05-01

We sought to determine the feasibility of developing a multimedia educational tutorial to teach learners to assess the critical view of safety using input from expert surgeons, non-surgeons and crowd-sourcing. We intended to develop a tutorial that would teach learners how to identify the basic anatomy and physiology of the gallbladder, identify the components of the critical view of safety criteria, and understand its significance for performing a safe gallbladder removal. Using rounds of assessment with experts, laypersons and crowd-workers we developed an educational video with improving comprehension after each round of revision. We demonstrate that the development of a multimedia educational tool to educate learners of various backgrounds is feasible using an iterative review process that incorporates the input of experts and crowd sourcing. When planning the development of an educational tutorial, a step-wise approach as described herein should be considered. Copyright © 2017 Elsevier Inc. All rights reserved.

Thermal Expert System (TEXSYS): Systems autonomy demonstration project, volume 2. Results

NASA Astrophysics Data System (ADS)

Glass, B. J.

1992-10-01

The Systems Autonomy Demonstration Project (SADP) produced a knowledge-based real-time control system for control and fault detection, isolation, and recovery (FDIR) of a prototype two-phase Space Station Freedom external active thermal control system (EATCS). The Thermal Expert System (TEXSYS) was demonstrated in recent tests to be capable of reliable fault anticipation and detection, as well as ordinary control of the thermal bus. Performance requirements were addressed by adopting a hierarchical symbolic control approach-layering model-based expert system software on a conventional, numerical data acquisition and control system. The model-based reasoning capabilities of TEXSYS were shown to be advantageous over typical rule-based expert systems, particularly for detection of unforeseen faults and sensor failures. Volume 1 gives a project overview and testing highlights. Volume 2 provides detail on the EATCS testbed, test operations, and online test results. Appendix A is a test archive, while Appendix B is a compendium of design and user manuals for the TEXSYS software.

A knowledge-based approach to configuration layout, justification, and documentation

NASA Technical Reports Server (NTRS)

Craig, F. G.; Cutts, D. E.; Fennel, T. R.; Case, C.; Palmer, J. R.

1990-01-01

The design, development, and implementation is described of a prototype expert system which could aid designers and system engineers in the placement of racks aboard modules on Space Station Freedom. This type of problem is relevant to any program with multiple constraints and requirements demanding solutions which minimize usage of limited resources. This process is generally performed by a single, highly experienced engineer who integrates all the diverse mission requirements and limitations, and develops an overall technical solution which meets program and system requirements with minimal cost, weight, volume, power, etc. This system architect performs an intellectual integration process in which the underlying design rationale is often not fully documented. This is a situation which lends itself to an expert system solution for enhanced consistency, thoroughness, documentation, and change assessment capabilities.

A Knowledge-Based Approach to Configuration Layout, Justification, and Documentation

NASA Technical Reports Server (NTRS)

Craig, F. G.; Cutts, D. E.; Fennel, T. R.; Case, C. M.; Palmer, J. R.

1991-01-01

The design, development, and implementation of a prototype expert system which could aid designers and system engineers in the placement of racks aboard modules on the Space Station Freedom are described. This type of problem is relevant to any program with multiple constraints and requirements demanding solutions which minimize usage of limited resources. This process is generally performed by a single, highly experienced engineer who integrates all the diverse mission requirements and limitations, and develops an overall technical solution which meets program and system requirements with minimal cost, weight, volume, power, etc. This system architect performs an intellectual integration process in which the underlying design rationale is often not fully documented. This is a situation which lends itself to an expert system solution for enhanced consistency, thoroughness, documentation, and change assessment capabilities.

Adaptive neuro-fuzzy and expert systems for power quality analysis and prediction of abnormal operation

NASA Astrophysics Data System (ADS)

Ibrahim, Wael Refaat Anis

The present research involves the development of several fuzzy expert systems for power quality analysis and diagnosis. Intelligent systems for the prediction of abnormal system operation were also developed. The performance of all intelligent modules developed was either enhanced or completely produced through adaptive fuzzy learning techniques. Neuro-fuzzy learning is the main adaptive technique utilized. The work presents a novel approach to the interpretation of power quality from the perspective of the continuous operation of a single system. The research includes an extensive literature review pertaining to the applications of intelligent systems to power quality analysis. Basic definitions and signature events related to power quality are introduced. In addition, detailed discussions of various artificial intelligence paradigms as well as wavelet theory are included. A fuzzy-based intelligent system capable of identifying normal from abnormal operation for a given system was developed. Adaptive neuro-fuzzy learning was applied to enhance its performance. A group of fuzzy expert systems that could perform full operational diagnosis were also developed successfully. The developed systems were applied to the operational diagnosis of 3-phase induction motors and rectifier bridges. A novel approach for learning power quality waveforms and trends was developed. The technique, which is adaptive neuro fuzzy-based, learned, compressed, and stored the waveform data. The new technique was successfully tested using a wide variety of power quality signature waveforms, and using real site data. The trend-learning technique was incorporated into a fuzzy expert system that was designed to predict abnormal operation of a monitored system. The intelligent system learns and stores, in compressed format, trends leading to abnormal operation. The system then compares incoming data to the retained trends continuously. If the incoming data matches any of the learned trends, an alarm is instigated predicting the advent of system abnormal operation. The incoming data could be compared to previous trends as well as matched to trends developed through computer simulations and stored using fuzzy learning.

CLEAR: Communications Link Expert Assistance Resource

NASA Technical Reports Server (NTRS)

Hull, Larry G.; Hughes, Peter M.

1987-01-01

Communications Link Expert Assistance Resource (CLEAR) is a real time, fault diagnosis expert system for the Cosmic Background Explorer (COBE) Mission Operations Room (MOR). The CLEAR expert system is an operational prototype which assists the MOR operator/analyst by isolating and diagnosing faults in the spacecraft communication link with the Tracking and Data Relay Satellite (TDRS) during periods of realtime data acquisition. The mission domain, user requirements, hardware configuration, expert system concept, tool selection, development approach, and system design were discussed. Development approach and system implementation are emphasized. Also discussed are system architecture, tool selection, operation, and future plans.

Classifying and profiling Social Networking Site users: a latent segmentation approach.

PubMed

Alarcón-del-Amo, María-del-Carmen; Lorenzo-Romero, Carlota; Gómez-Borja, Miguel-Ángel

2011-09-01

Social Networking Sites (SNSs) have showed an exponential growth in the last years. The first step for an efficient use of SNSs stems from an understanding of the individuals' behaviors within these sites. In this research, we have obtained a typology of SNS users through a latent segmentation approach, based on the frequency by which users perform different activities within the SNSs, sociodemographic variables, experience in SNSs, and dimensions related to their interaction patterns. Four different segments have been obtained. The "introvert" and "novel" users are the more occasional. They utilize SNSs mainly to communicate with friends, although "introverts" are more passive users. The "versatile" user performs different activities, although occasionally. Finally, the "expert-communicator" performs a greater variety of activities with a higher frequency. They tend to perform some marketing-related activities such as commenting on ads or gathering information about products and brands as well as commenting ads. The companies can take advantage of these segmentation schemes in different ways: first, by tracking and monitoring information interchange between users regarding their products and brands. Second, they should match the SNS users' profiles with their market targets to use SNSs as marketing tools. Finally, for most business, the expert users could be interesting opinion leaders and potential brand influencers.

Exercise prescription for overhead athletes with shoulder pathology: a systematic review with best evidence synthesis.

PubMed

Wright, Alexis A; Hegedus, Eric J; Tarara, Daniel T; Ray, Samantha C; Dischiavi, Steven L

2018-02-01

To produce a best evidence synthesis of exercise prescription used when treating shoulder pathology in the overhead athlete. A systematic review of exercises used in overhead athletes including case studies and clinical commentaries. MEDLINE, PubMed, SPORTDiscus and CINAHL from database inception through July 8, 2016. We examined data from randomised controlled trials and prospective cohort (level I-IV evidence) studies that addressed exercise intervention in the rehabilitation of the overhead athlete with shoulder pathology. Case studies and clinical commentaries (level V evidence) were examined to account for expert opinion-based research. Data were combined using best evidence synthesis and graded (A-F) recommendations (Centre for Evidence-Based Medicine). There were 33 unique exercises in six level I-IV studies that met our inclusion criteria. Most exercises were single-plane, upper extremity exercises performed below 90 o of elevation. There were 102 unique exercises in 33 level V studies that met our inclusion criteria. These exercises emphasised plyometrics, kinetic chain and sport-specific training. Overall, evidence for exercise interventions in overhead athletes with shoulder pathology is dominated by expert opinion (grade D). There is great variability between exercise approaches suggested by experts and those investigated in research studies and the overall level of evidence is low. The strongest available evidence (level B) supports the use of single-plane, open chain upper extremity exercises performed below 90° of elevation and closed chain upper extremity exercises. Clinical expert pieces support a more advanced, global treatment approach consistent with the complex, multidimensional nature of sport. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Outlier experienced surgeon's performances impact on benchmark for technical surgical skills training.

PubMed

Gallagher, Anthony G; Henn, Patrick J; Neary, Paul C; Senagore, Anthony J; Marcello, Peter W; Bunting, Brendan P; Seymour, Neal E; Satava, Richard M

2018-05-01

Training in medicine must move to an outcome-based approach. A proficiency-based progression outcome approach to training relies on a quantitative estimation of experienced operator performance. We aimed to develop a method for dealing with atypical expert performances in the quantitative definition of surgical proficiency. In study one, 100 experienced laparoscopic surgeons' performances on virtual reality and box-trainer simulators were assessed for two similar laparoscopic tasks. In study two, 15 experienced surgeons and 16 trainee colorectal surgeons performed one simulated hand-assisted laparoscopic colorectal procedure. Performance scores of experienced surgeons in both studies were standardized (i.e. Z-scores) using the mean and standard deviations (SDs). Performances >1.96 SDs from the mean were excluded in proficiency definitions. In study one, 1-5% of surgeons' performances were excluded having performed significantly below their colleagues. Excluded surgeons made significantly fewer correct incisions (mean = 7 (SD = 2) versus 19.42 (SD = 4.6), P < 0.0001) and a greater proportion of incorrect incisions (mean = 45.71 (SD = 10.48) versus 5.25 (SD = 6.6), P < 0.0001). In study two, one experienced colorectal surgeon performance was >4 SDs for time to complete the procedure and >6 SDs for path length. After their exclusions, experienced surgeons' performances were significantly better than trainees for path length: P = 0.031 and for time: P = 0.002. Objectively assessed atypical expert performances were few. Z-score standardization identified them and produced a more robust quantitative definition of proficiency. © 2018 Royal Australasian College of Surgeons.

Problem-solving strategies in psychiatry: differences between experts and novices in diagnostic accuracy and reasoning.

PubMed

Gabriel, Adel; Violato, Claudio

2013-01-01

The purpose of this study was to examine and compare diagnostic success and its relationship with the diagnostic reasoning process between novices and experts in psychiatry. Nine volunteers, comprising five expert psychiatrists and four clinical clerks, completed a think-aloud protocol while attempting to make a DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition) diagnosis of a selected case with both Axis I and Axis III diagnoses. Expert psychiatrists made significantly more successful diagnoses for both the primary psychiatric and medical diagnoses than clinical clerks. Expert psychiatrists also gave fewer differential options. Analyzing the think-aloud protocols, expert psychiatrists were much more organized, made fewer mistakes, and utilized significantly less time to access their knowledge than clinical clerks. Both novices and experts seemed to use the hypothetic-deductive and scheme-inductive approaches to diagnosis. However, experts utilized hypothetic-deductive approaches significantly more often than novices. The hypothetic-deductive diagnostic strategy was utilized more than the scheme-inductive approach by both expert psychiatrists and clinical clerks. However, a specific relationship between diagnostic reasoning and diagnostic success could not be identified in this small pilot study. The author recommends a larger study that would include a detailed analysis of the think-aloud protocols.

Do technical skills correlate with non-technical skills in crisis resource management: a simulation study.

PubMed

Riem, N; Boet, S; Bould, M D; Tavares, W; Naik, V N

2012-11-01

Both technical skills (TS) and non-technical skills (NTS) are key to ensuring patient safety in acute care practice and effective crisis management. These skills are often taught and assessed separately. We hypothesized that TS and NTS are not independent of each other, and we aimed to evaluate the relationship between TS and NTS during a simulated intraoperative crisis scenario. This study was a retrospective analysis of performances from a previously published work. After institutional ethics approval, 50 anaesthesiology residents managed a simulated crisis scenario of an intraoperative cardiac arrest secondary to a malignant arrhythmia. We used a modified Delphi approach to design a TS checklist, specific for the management of a malignant arrhythmia requiring defibrillation. All scenarios were recorded. Each performance was analysed by four independent experts. For each performance, two experts independently rated the technical performance using the TS checklist, and two other experts independently rated NTS using the Anaesthetists' Non-Technical Skills score. TS and NTS were significantly correlated to each other (r=0.45, P<0.05). During a simulated 5 min resuscitation requiring crisis resource management, our results indicate that TS and NTS are related to one another. This research provides the basis for future studies evaluating the nature of this relationship, the influence of NTS training on the performance of TS, and to determine whether NTS are generic and transferrable between crises that require different TS.

A Qualitative Approach to the Evaluation of Expert Systems Shells.

ERIC Educational Resources Information Center

Slawson, Dean A.; And Others

This study explores an approach to the evaluation of expert system shells using case studies. The methodology and some of the results of an evaluation of the prototype development of an expert system using the shell "M1" are detailed, including a description of the participants and the project, the data collection process and materials,…

Biomechanical differences between expert and novice workers in a manual material handling task.

PubMed

Plamondon, Andre; Denis, Denys; Delisle, Alain; Lariviere, Christian; Salazar, Erik

2010-10-01

The objective was to verify whether the methods were safer and more efficient when used by expert handlers than by novice handlers. Altogether, 15 expert and 15 novice handlers were recruited. Their task was to transfer four boxes from a conveyor to a hand trolley. Different characteristics of the load and lifting heights were modified to achieve a larger variety of methods by the participants. The results show that the net moments at the L5/S1 joint were not significantly different (p > 0.05) for the two groups. However, compared with the novices, the experts bent their lumbar region less (experts 54° (SD 11°); novices 66° (SD 15°)) but bent their knees more (experts approx. 72° (SD approx. 30°); novices approx. 53° (SD approx. 33°), which brought them closer to the box. The handler's posture therefore seems to be a major aspect that should be paid specific attention, mainly when there is maximum back loading. STATEMENT OF RELEVANCE: The findings of this research will be useful for improving manual material handling training programmes. Most biomechanical research is based on novice workers and adding information about the approach used by expert handlers in performing their tasks will help provide new avenues for reducing the risk of injury caused by this demanding physical task.

Applications of artificial intelligence to digital photogrammetry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kretsch, J.L.

1988-01-01

The aim of this research was to explore the application of expert systems to digital photogrammetry, specifically to photogrammetric triangulation, feature extraction, and photogrammetric problem solving. In 1987, prototype expert systems were developed for doing system startup, interior orientation, and relative orientation in the mensuration stage. The system explored means of performing diagnostics during the process. In the area of feature extraction, the relationship of metric uncertainty to symbolic uncertainty was the topic of research. Error propagation through the Dempster-Shafer formalism for representing evidence was performed in order to find the variance in the calculated belief values due to errorsmore » in measurements made together the initial evidence needed to being labeling of observed image features with features in an object model. In photogrammetric problem solving, an expert system is under continuous development which seeks to solve photogrammetric problems using mathematical reasoning. The key to the approach used is the representation of knowledge directly in the form of equations, rather than in the form of if-then rules. Then each variable in the equations is treated as a goal to be solved.« less

The Interplay between Methodologies, Tasks and Visualisation Formats in the Study of Visual Expertise

ERIC Educational Resources Information Center

Boucheix, Jean-Michel

2017-01-01

This article introduces this special issue of "Frontline Learning Research." The first paper offers a methodological guide using Ericsson & Smith's (1991) "expert performance approach." This is followed by three papers that analyze the use of eye tracking in visual expertise models, and a paper reviewing the use of methods…

Using cognitive task analysis to develop simulation-based training for medical tasks.

PubMed

Cannon-Bowers, Jan; Bowers, Clint; Stout, Renee; Ricci, Katrina; Hildabrand, Annette

2013-10-01

Pressures to increase the efficacy and effectiveness of medical training are causing the Department of Defense to investigate the use of simulation technologies. This article describes a comprehensive cognitive task analysis technique that can be used to simultaneously generate training requirements, performance metrics, scenario requirements, and simulator/simulation requirements for medical tasks. On the basis of a variety of existing techniques, we developed a scenario-based approach that asks experts to perform the targeted task multiple times, with each pass probing a different dimension of the training development process. In contrast to many cognitive task analysis approaches, we argue that our technique can be highly cost effective because it is designed to accomplish multiple goals. The technique was pilot tested with expert instructors from a large military medical training command. These instructors were employed to generate requirements for two selected combat casualty care tasks-cricothyroidotomy and hemorrhage control. Results indicated that the technique is feasible to use and generates usable data to inform simulation-based training system design. Reprint & Copyright © 2013 Association of Military Surgeons of the U.S.

R and D Evaluation Workshop report, U.S. Department of Energy, Office of Energy Research, September 7--8, 1995

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jordan, G.

1995-10-30

The objective of the workshop was to promote discussions between experts and research managers on developing approaches for assessing the impact of DOE`s basic energy research upon the energy mission, applied research, technology transfer, the economy, and society. The purpose of this impact assessment is to demonstrate results and improve ER research programs in this era when basic research is expected to meet changing national economic and social goals. The questions addressed were: (1) By what criteria and metrics does Energy Research measure performance and evaluate its impact on the DOE mission and society while maintaining an environment that fostersmore » basic research? (2) What combination of evaluation methods best applies to assessing the performance and impact of OBES basic research? The focus will be upon the following methods: Case studies, User surveys, Citation analysis, TRACES approach, Return on DOE investment (ROI)/Econometrics, and Expert panels. (3) What combination of methods and specific rules of thumb can be applied to capture impacts along the spectrum from basic research to products and societal impacts?« less

Expert music performance: cognitive, neural, and developmental bases.

PubMed

Brown, Rachel M; Zatorre, Robert J; Penhune, Virginia B

2015-01-01

In this chapter, we explore what happens in the brain of an expert musician during performance. Understanding expert music performance is interesting to cognitive neuroscientists not only because it tests the limits of human memory and movement, but also because studying expert musicianship can help us understand skilled human behavior in general. In this chapter, we outline important facets of our current understanding of the cognitive and neural basis for music performance, and developmental factors that may underlie musical ability. We address three main questions. (1) What is expert performance? (2) How do musicians achieve expert-level performance? (3) How does expert performance come about? We address the first question by describing musicians' ability to remember, plan, execute, and monitor their performances in order to perform music accurately and expressively. We address the second question by reviewing evidence for possible cognitive and neural mechanisms that may underlie or contribute to expert music performance, including the integration of sound and movement, feedforward and feedback motor control processes, expectancy, and imagery. We further discuss how neural circuits in auditory, motor, parietal, subcortical, and frontal cortex all contribute to different facets of musical expertise. Finally, we address the third question by reviewing evidence for the heritability of musical expertise and for how expertise develops through training and practice. We end by discussing outlooks for future work. © 2015 Elsevier B.V. All rights reserved.

An international consensus algorithm for management of chronic postoperative inguinal pain.

PubMed

Lange, J F M; Kaufmann, R; Wijsmuller, A R; Pierie, J P E N; Ploeg, R J; Chen, D C; Amid, P K

2015-02-01

Tension-free mesh repair of inguinal hernia has led to uniformly low recurrence rates. Morbidity associated with this operation is mainly related to chronic pain. No consensus guidelines exist for the management of this condition. The goal of this study is to design an expert-based algorithm for diagnostic and therapeutic management of chronic inguinal postoperative pain (CPIP). A group of surgeons considered experts on inguinal hernia surgery was solicited to develop the algorithm. Consensus regarding each step of an algorithm proposed by the authors was sought by means of the Delphi method leading to a revised expert-based algorithm. With the input of 28 international experts, an algorithm for a stepwise approach for management of CPIP was created. 26 participants accepted the final algorithm as a consensus model. One participant could not agree with the final concept. One expert did not respond during the final phase. There is a need for guidelines with regard to management of CPIP. This algorithm can serve as a guide with regard to the diagnosis, management, and treatment of these patients and improve clinical outcomes. If an expectative phase of a few months has passed without any amelioration of CPIP, a multidisciplinary approach is indicated and a pain management team should be consulted. Pharmacologic, behavioral, and interventional modalities including nerve blocks are essential. If conservative measures fail and surgery is considered, triple neurectomy, correction for recurrence with or without neurectomy, and meshoma removal if indicated should be performed. Surgeons less experienced with remedial operations for CPIP should not hesitate to refer their patients to dedicated hernia surgeons.

A genetic algorithms approach for altering the membership functions in fuzzy logic controllers

NASA Technical Reports Server (NTRS)

Shehadeh, Hana; Lea, Robert N.

1992-01-01

Through previous work, a fuzzy control system was developed to perform translational and rotational control of a space vehicle. This problem was then re-examined to determine the effectiveness of genetic algorithms on fine tuning the controller. This paper explains the problems associated with the design of this fuzzy controller and offers a technique for tuning fuzzy logic controllers. A fuzzy logic controller is a rule-based system that uses fuzzy linguistic variables to model human rule-of-thumb approaches to control actions within a given system. This 'fuzzy expert system' features rules that direct the decision process and membership functions that convert the linguistic variables into the precise numeric values used for system control. Defining the fuzzy membership functions is the most time consuming aspect of the controller design. One single change in the membership functions could significantly alter the performance of the controller. This membership function definition can be accomplished by using a trial and error technique to alter the membership functions creating a highly tuned controller. This approach can be time consuming and requires a great deal of knowledge from human experts. In order to shorten development time, an iterative procedure for altering the membership functions to create a tuned set that used a minimal amount of fuel for velocity vector approach and station-keep maneuvers was developed. Genetic algorithms, search techniques used for optimization, were utilized to solve this problem.

Evidence flow graph methods for validation and verification of expert systems

NASA Technical Reports Server (NTRS)

Becker, Lee A.; Green, Peter G.; Bhatnagar, Jayant

1988-01-01

This final report describes the results of an investigation into the use of evidence flow graph techniques for performing validation and verification of expert systems. This was approached by developing a translator to convert horn-clause rule bases into evidence flow graphs, a simulation program, and methods of analysis. These tools were then applied to a simple rule base which contained errors. It was found that the method was capable of identifying a variety of problems, for example that the order of presentation of input data or small changes in critical parameters could effect the output from a set of rules.

The load shedding advisor: An example of a crisis-response expert system

NASA Technical Reports Server (NTRS)

Bollinger, Terry B.; Lightner, Eric; Laverty, John; Ambrose, Edward

1987-01-01

A Prolog-based prototype expert system is described that was implemented by the Network Operations Branch of the NASA Goddard Space Flight Center. The purpose of the prototype was to test whether a small, inexpensive computer system could be used to host a load shedding advisor, a system which would monitor major physical environment parameters in a computer facility, then recommend appropriate operator reponses whenever a serious condition was detected. The resulting prototype performed significantly to efficiency gains achieved by replacing a purely rule-based design methodology with a hybrid approach that combined procedural, entity-relationship, and rule-based methods.

Extracting BI-RADS Features from Portuguese Clinical Texts.

PubMed

Nassif, Houssam; Cunha, Filipe; Moreira, Inês C; Cruz-Correia, Ricardo; Sousa, Eliana; Page, David; Burnside, Elizabeth; Dutra, Inês

2012-01-01

In this work we build the first BI-RADS parser for Portuguese free texts, modeled after existing approaches to extract BI-RADS features from English medical records. Our concept finder uses a semantic grammar based on the BIRADS lexicon and on iterative transferred expert knowledge. We compare the performance of our algorithm to manual annotation by a specialist in mammography. Our results show that our parser's performance is comparable to the manual method.

Man vs. Machine: An interactive poll to evaluate hydrological model performance of a manual and an automatic calibration

NASA Astrophysics Data System (ADS)

Wesemann, Johannes; Burgholzer, Reinhard; Herrnegger, Mathew; Schulz, Karsten

2017-04-01

In recent years, a lot of research in hydrological modelling has been invested to improve the automatic calibration of rainfall-runoff models. This includes for example (1) the implementation of new optimisation methods, (2) the incorporation of new and different objective criteria and signatures in the optimisation and (3) the usage of auxiliary data sets apart from runoff. Nevertheless, in many applications manual calibration is still justifiable and frequently applied. The hydrologist performing the manual calibration, with his expert knowledge, is able to judge the hydrographs simultaneously concerning details but also in a holistic view. This integrated eye-ball verification procedure available to man can be difficult to formulate in objective criteria, even when using a multi-criteria approach. Comparing the results of automatic and manual calibration is not straightforward. Automatic calibration often solely involves objective criteria such as Nash-Sutcliffe Efficiency Coefficient or the Kling-Gupta-Efficiency as a benchmark during the calibration. Consequently, a comparison based on such measures is intrinsically biased towards automatic calibration. Additionally, objective criteria do not cover all aspects of a hydrograph leaving questions concerning the quality of a simulation open. This contribution therefore seeks to examine the quality of manually and automatically calibrated hydrographs by interactively involving expert knowledge in the evaluation. Simulations have been performed for the Mur catchment in Austria with the rainfall-runoff model COSERO using two parameter sets evolved from a manual and an automatic calibration. A subset of resulting hydrographs for observation and simulation, representing the typical flow conditions and events, will be evaluated in this study. In an interactive crowdsourcing approach experts attending the session can vote for their preferred simulated hydrograph without having information on the calibration method that produced the respective hydrograph. Therefore, the result of the poll can be seen as an additional quality criterion for the comparison of the two different approaches and help in the evaluation of the automatic calibration method.

Work domain constraints for modelling surgical performance.

PubMed

Morineau, Thierry; Riffaud, Laurent; Morandi, Xavier; Villain, Jonathan; Jannin, Pierre

2015-10-01

Three main approaches can be identified for modelling surgical performance: a competency-based approach, a task-based approach, both largely explored in the literature, and a less known work domain-based approach. The work domain-based approach first describes the work domain properties that constrain the agent's actions and shape the performance. This paper presents a work domain-based approach for modelling performance during cervical spine surgery, based on the idea that anatomical structures delineate the surgical performance. This model was evaluated through an analysis of junior and senior surgeons' actions. Twenty-four cervical spine surgeries performed by two junior and two senior surgeons were recorded in real time by an expert surgeon. According to a work domain-based model describing an optimal progression through anatomical structures, the degree of adjustment of each surgical procedure to a statistical polynomial function was assessed. Each surgical procedure showed a significant suitability with the model and regression coefficient values around 0.9. However, the surgeries performed by senior surgeons fitted this model significantly better than those performed by junior surgeons. Analysis of the relative frequencies of actions on anatomical structures showed that some specific anatomical structures discriminate senior from junior performances. The work domain-based modelling approach can provide an overall statistical indicator of surgical performance, but in particular, it can highlight specific points of interest among anatomical structures that the surgeons dwelled on according to their level of expertise.

Learning to Rank Figures within a Biomedical Article

PubMed Central

Liu, Feifan; Yu, Hong

2014-01-01

Hundreds of millions of figures are available in biomedical literature, representing important biomedical experimental evidence. This ever-increasing sheer volume has made it difficult for scientists to effectively and accurately access figures of their interest, the process of which is crucial for validating research facts and for formulating or testing novel research hypotheses. Current figure search applications can't fully meet this challenge as the “bag of figures” assumption doesn't take into account the relationship among figures. In our previous study, hundreds of biomedical researchers have annotated articles in which they serve as corresponding authors. They ranked each figure in their paper based on a figure's importance at their discretion, referred to as “figure ranking”. Using this collection of annotated data, we investigated computational approaches to automatically rank figures. We exploited and extended the state-of-the-art listwise learning-to-rank algorithms and developed a new supervised-learning model BioFigRank. The cross-validation results show that BioFigRank yielded the best performance compared with other state-of-the-art computational models, and the greedy feature selection can further boost the ranking performance significantly. Furthermore, we carry out the evaluation by comparing BioFigRank with three-level competitive domain-specific human experts: (1) First Author, (2) Non-Author-In-Domain-Expert who is not the author nor co-author of an article but who works in the same field of the corresponding author of the article, and (3) Non-Author-Out-Domain-Expert who is not the author nor co-author of an article and who may or may not work in the same field of the corresponding author of an article. Our results show that BioFigRank outperforms Non-Author-Out-Domain-Expert and performs as well as Non-Author-In-Domain-Expert. Although BioFigRank underperforms First Author, since most biomedical researchers are either in- or out-domain-experts for an article, we conclude that BioFigRank represents an artificial intelligence system that offers expert-level intelligence to help biomedical researchers to navigate increasingly proliferated big data efficiently. PMID:24625719

Learning to rank figures within a biomedical article.

PubMed

Liu, Feifan; Yu, Hong

2014-01-01

Hundreds of millions of figures are available in biomedical literature, representing important biomedical experimental evidence. This ever-increasing sheer volume has made it difficult for scientists to effectively and accurately access figures of their interest, the process of which is crucial for validating research facts and for formulating or testing novel research hypotheses. Current figure search applications can't fully meet this challenge as the "bag of figures" assumption doesn't take into account the relationship among figures. In our previous study, hundreds of biomedical researchers have annotated articles in which they serve as corresponding authors. They ranked each figure in their paper based on a figure's importance at their discretion, referred to as "figure ranking". Using this collection of annotated data, we investigated computational approaches to automatically rank figures. We exploited and extended the state-of-the-art listwise learning-to-rank algorithms and developed a new supervised-learning model BioFigRank. The cross-validation results show that BioFigRank yielded the best performance compared with other state-of-the-art computational models, and the greedy feature selection can further boost the ranking performance significantly. Furthermore, we carry out the evaluation by comparing BioFigRank with three-level competitive domain-specific human experts: (1) First Author, (2) Non-Author-In-Domain-Expert who is not the author nor co-author of an article but who works in the same field of the corresponding author of the article, and (3) Non-Author-Out-Domain-Expert who is not the author nor co-author of an article and who may or may not work in the same field of the corresponding author of an article. Our results show that BioFigRank outperforms Non-Author-Out-Domain-Expert and performs as well as Non-Author-In-Domain-Expert. Although BioFigRank underperforms First Author, since most biomedical researchers are either in- or out-domain-experts for an article, we conclude that BioFigRank represents an artificial intelligence system that offers expert-level intelligence to help biomedical researchers to navigate increasingly proliferated big data efficiently.

Expert system decision support for low-cost launch vehicle operations

NASA Technical Reports Server (NTRS)

Szatkowski, G. P.; Levin, Barry E.

1991-01-01

Progress in assessing the feasibility, benefits, and risks associated with AI expert systems applied to low cost expendable launch vehicle systems is described. Part one identified potential application areas in vehicle operations and on-board functions, assessed measures of cost benefit, and identified key technologies to aid in the implementation of decision support systems in this environment. Part two of the program began the development of prototypes to demonstrate real-time vehicle checkout with controller and diagnostic/analysis intelligent systems and to gather true measures of cost savings vs. conventional software, verification and validation requirements, and maintainability improvement. The main objective of the expert advanced development projects was to provide a robust intelligent system for control/analysis that must be performed within a specified real-time window in order to meet the demands of the given application. The efforts to develop the two prototypes are described. Prime emphasis was on a controller expert system to show real-time performance in a cryogenic propellant loading application and safety validation implementation of this system experimentally, using commercial-off-the-shelf software tools and object oriented programming techniques. This smart ground support equipment prototype is based in C with imbedded expert system rules written in the CLIPS protocol. The relational database, ORACLE, provides non-real-time data support. The second demonstration develops the vehicle/ground intelligent automation concept, from phase one, to show cooperation between multiple expert systems. This automated test conductor (ATC) prototype utilizes a knowledge-bus approach for intelligent information processing by use of virtual sensors and blackboards to solve complex problems. It incorporates distributed processing of real-time data and object-oriented techniques for command, configuration control, and auto-code generation.

A condition metric for Eucalyptus woodland derived from expert evaluations.

PubMed

Sinclair, Steve J; Bruce, Matthew J; Griffioen, Peter; Dodd, Amanda; White, Matthew D

2018-02-01

The evaluation of ecosystem quality is important for land-management and land-use planning. Evaluation is unavoidably subjective, and robust metrics must be based on consensus and the structured use of observations. We devised a transparent and repeatable process for building and testing ecosystem metrics based on expert data. We gathered quantitative evaluation data on the quality of hypothetical grassy woodland sites from experts. We used these data to train a model (an ensemble of 30 bagged regression trees) capable of predicting the perceived quality of similar hypothetical woodlands based on a set of 13 site variables as inputs (e.g., cover of shrubs, richness of native forbs). These variables can be measured at any site and the model implemented in a spreadsheet as a metric of woodland quality. We also investigated the number of experts required to produce an opinion data set sufficient for the construction of a metric. The model produced evaluations similar to those provided by experts, as shown by assessing the model's quality scores of expert-evaluated test sites not used to train the model. We applied the metric to 13 woodland conservation reserves and asked managers of these sites to independently evaluate their quality. To assess metric performance, we compared the model's evaluation of site quality with the managers' evaluations through multidimensional scaling. The metric performed relatively well, plotting close to the center of the space defined by the evaluators. Given the method provides data-driven consensus and repeatability, which no single human evaluator can provide, we suggest it is a valuable tool for evaluating ecosystem quality in real-world contexts. We believe our approach is applicable to any ecosystem. © 2017 State of Victoria.

Optimized classification of 18F-Florbetaben PET scans as positive and negative using an SUVR quantitative approach and comparison to visual assessment.

PubMed

Bullich, Santiago; Seibyl, John; Catafau, Ana M; Jovalekic, Aleksandar; Koglin, Norman; Barthel, Henryk; Sabri, Osama; De Santi, Susan

2017-01-01

Standardized uptake value ratios (SUVRs) calculated from cerebral cortical areas can be used to categorize 18 F-Florbetaben (FBB) PET scans by applying appropriate cutoffs. The objective of this work was first to generate FBB SUVR cutoffs using visual assessment (VA) as standard of truth (SoT) for a number of reference regions (RR) (cerebellar gray matter (GCER), whole cerebellum (WCER), pons (PONS), and subcortical white matter (SWM)). Secondly, to validate the FBB PET scan categorization performed by SUVR cutoffs against the categorization made by post-mortem histopathological confirmation of the Aβ presence. Finally, to evaluate the added value of SUVR cutoff categorization to VA. SUVR cutoffs were generated for each RR using FBB scans from 143 subjects who were visually assessed by 3 readers. SUVR cutoffs were validated in 78 end-of life subjects using VA from 8 independent blinded readers (3 expert readers and 5 non-expert readers) and histopathological confirmation of the presence of neuritic beta-amyloid plaques as SoT. Finally, the number of correctly or incorrectly classified scans according to pathology results using VA and SUVR cutoffs was compared. Composite SUVR cutoffs generated were 1.43 (GCER), 0.96 (WCER), 0.78 (PONS) and 0.71 (SWM). Accuracy values were high and consistent across RR (range 83-94% for histopathology, and 85-94% for VA). SUVR cutoff performed similarly as VA but did not improve VA classification of FBB scans read either by expert readers or the majority read but provided higher accuracy than some non-expert readers. The accurate scan classification obtained in this study supports the use of VA as SoT to generate site-specific SUVR cutoffs. For an elderly end of life population, VA and SUVR cutoff categorization perform similarly in classifying FBB scans as Aβ-positive or Aβ-negative. These results emphasize the additional contribution that SUVR cutoff classification may have compared with VA performed by non-expert readers.

Evaluation of a Performance-Based Expert Elicitation: WHO Global Attribution of Foodborne Diseases.

PubMed

Aspinall, W P; Cooke, R M; Havelaar, A H; Hoffmann, S; Hald, T

2016-01-01

For many societally important science-based decisions, data are inadequate, unreliable or non-existent, and expert advice is sought. In such cases, procedures for eliciting structured expert judgments (SEJ) are increasingly used. This raises questions regarding validity and reproducibility. This paper presents new findings from a large-scale international SEJ study intended to estimate the global burden of foodborne disease on behalf of WHO. The study involved 72 experts distributed over 134 expert panels, with panels comprising thirteen experts on average. Elicitations were conducted in five languages. Performance-based weighted solutions for target questions of interest were formed for each panel. These weights were based on individual expert's statistical accuracy and informativeness, determined using between ten and fifteen calibration variables from the experts' field with known values. Equal weights combinations were also calculated. The main conclusions on expert performance are: (1) SEJ does provide a science-based method for attribution of the global burden of foodborne diseases; (2) equal weighting of experts per panel increased statistical accuracy to acceptable levels, but at the cost of informativeness; (3) performance-based weighting increased informativeness, while retaining accuracy; (4) due to study constraints individual experts' accuracies were generally lower than in other SEJ studies, and (5) there was a negative correlation between experts' informativeness and statistical accuracy which attenuated as accuracy improved, revealing that the least accurate experts drive the negative correlation. It is shown, however, that performance-based weighting has the ability to yield statistically accurate and informative combinations of experts' judgments, thereby offsetting this contrary influence. The present findings suggest that application of SEJ on a large scale is feasible, and motivate the development of enhanced training and tools for remote elicitation of multiple, internationally-dispersed panels.

A Generalized Mixture Framework for Multi-label Classification

PubMed Central

Hong, Charmgil; Batal, Iyad; Hauskrecht, Milos

2015-01-01

We develop a novel probabilistic ensemble framework for multi-label classification that is based on the mixtures-of-experts architecture. In this framework, we combine multi-label classification models in the classifier chains family that decompose the class posterior distribution P(Y1, …, Yd|X) using a product of posterior distributions over components of the output space. Our approach captures different input–output and output–output relations that tend to change across data. As a result, we can recover a rich set of dependency relations among inputs and outputs that a single multi-label classification model cannot capture due to its modeling simplifications. We develop and present algorithms for learning the mixtures-of-experts models from data and for performing multi-label predictions on unseen data instances. Experiments on multiple benchmark datasets demonstrate that our approach achieves highly competitive results and outperforms the existing state-of-the-art multi-label classification methods. PMID:26613069

Toward a theory of distributed word expert natural language parsing

NASA Technical Reports Server (NTRS)

Rieger, C.; Small, S.

1981-01-01

An approach to natural language meaning-based parsing in which the unit of linguistic knowledge is the word rather than the rewrite rule is described. In the word expert parser, knowledge about language is distributed across a population of procedural experts, each representing a word of the language, and each an expert at diagnosing that word's intended usage in context. The parser is structured around a coroutine control environment in which the generator-like word experts ask questions and exchange information in coming to collective agreement on sentence meaning. The word expert theory is advanced as a better cognitive model of human language expertise than the traditional rule-based approach. The technical discussion is organized around examples taken from the prototype LISP system which implements parts of the theory.

A unified approach for debugging is-a structure and mappings in networked taxonomies

PubMed Central

2013-01-01

Background With the increased use of ontologies and ontology mappings in semantically-enabled applications such as ontology-based search and data integration, the issue of detecting and repairing defects in ontologies and ontology mappings has become increasingly important. These defects can lead to wrong or incomplete results for the applications. Results We propose a unified framework for debugging the is-a structure of and mappings between taxonomies, the most used kind of ontologies. We present theory and algorithms as well as an implemented system RepOSE, that supports a domain expert in detecting and repairing missing and wrong is-a relations and mappings. We also discuss two experiments performed by domain experts: an experiment on the Anatomy ontologies from the Ontology Alignment Evaluation Initiative, and a debugging session for the Swedish National Food Agency. Conclusions Semantically-enabled applications need high quality ontologies and ontology mappings. One key aspect is the detection and removal of defects in the ontologies and ontology mappings. Our system RepOSE provides an environment that supports domain experts to deal with this issue. We have shown the usefulness of the approach in two experiments by detecting and repairing circa 200 and 30 defects, respectively. PMID:23548155

Network approaches for expert decisions in sports.

PubMed

Glöckner, Andreas; Heinen, Thomas; Johnson, Joseph G; Raab, Markus

2012-04-01

This paper focuses on a model comparison to explain choices based on gaze behavior via simulation procedures. We tested two classes of models, a parallel constraint satisfaction (PCS) artificial neuronal network model and an accumulator model in a handball decision-making task from a lab experiment. Both models predict action in an option-generation task in which options can be chosen from the perspective of a playmaker in handball (i.e., passing to another player or shooting at the goal). Model simulations are based on a dataset of generated options together with gaze behavior measurements from 74 expert handball players for 22 pieces of video footage. We implemented both classes of models as deterministic vs. probabilistic models including and excluding fitted parameters. Results indicated that both classes of models can fit and predict participants' initially generated options based on gaze behavior data, and that overall, the classes of models performed about equally well. Early fixations were thereby particularly predictive for choices. We conclude that the analyses of complex environments via network approaches can be successfully applied to the field of experts' decision making in sports and provide perspectives for further theoretical developments. Copyright © 2011 Elsevier B.V. All rights reserved.

A Knowledge Navigation Method for the Domain of Customers' Services of Mobile Communication Corporations in China

NASA Astrophysics Data System (ADS)

Wu, Jiangning; Wang, Xiaohuan

Rapidly increasing amount of mobile phone users and types of services leads to a great accumulation of complaining information. How to use this information to enhance the quality of customers' services is a big issue at present. To handle this kind of problem, the paper presents an approach to construct a domain knowledge map for navigating the explicit and tacit knowledge in two ways: building the Topic Map-based explicit knowledge navigation model, which includes domain TM construction, a semantic topic expansion algorithm and VSM-based similarity calculation; building Social Network Analysis-based tacit knowledge navigation model, which includes a multi-relational expert navigation algorithm and the criterions to evaluate the performance of expert networks. In doing so, both the customer managers and operators in call centers can find the appropriate knowledge and experts quickly and exactly. The experimental results show that the above method is very powerful for knowledge navigation.

Diagnostics in the Extendable Integrated Support Environment (EISE)

NASA Technical Reports Server (NTRS)

Brink, James R.; Storey, Paul

1988-01-01

Extendable Integrated Support Environment (EISE) is a real-time computer network consisting of commercially available hardware and software components to support systems level integration, modifications, and enhancement to weapons systems. The EISE approach offers substantial potential savings by eliminating unique support environments in favor of sharing common modules for the support of operational weapon systems. An expert system is being developed that will help support diagnosing faults in this network. This is a multi-level, multi-expert diagnostic system that uses experiential knowledge relating symptoms to faults and also reasons from structural and functional models of the underlying physical model when experiential reasoning is inadequate. The individual expert systems are orchestrated by a supervisory reasoning controller, a meta-level reasoner which plans the sequence of reasoning steps to solve the given specific problem. The overall system, termed the Diagnostic Executive, accesses systems level performance checks and error reports, and issues remote test procedures to formulate and confirm fault hypotheses.

Comparing Habitat Suitability and Connectivity Modeling Methods for Conserving Pronghorn Migrations

PubMed Central

Poor, Erin E.; Loucks, Colby; Jakes, Andrew; Urban, Dean L.

2012-01-01

Terrestrial long-distance migrations are declining globally: in North America, nearly 75% have been lost. Yet there has been limited research comparing habitat suitability and connectivity models to identify migration corridors across increasingly fragmented landscapes. Here we use pronghorn (Antilocapra americana) migrations in prairie habitat to compare two types of models that identify habitat suitability: maximum entropy (Maxent) and expert-based (Analytic Hierarchy Process). We used distance to wells, distance to water, NDVI, land cover, distance to roads, terrain shape and fence presence to parameterize the models. We then used the output of these models as cost surfaces to compare two common connectivity models, least-cost modeling (LCM) and circuit theory. Using pronghorn movement data from spring and fall migrations, we identified potential migration corridors by combining each habitat suitability model with each connectivity model. The best performing model combination was Maxent with LCM corridors across both seasons. Maxent out-performed expert-based habitat suitability models for both spring and fall migrations. However, expert-based corridors can perform relatively well and are a cost-effective alternative if species location data are unavailable. Corridors created using LCM out-performed circuit theory, as measured by the number of pronghorn GPS locations present within the corridors. We suggest the use of a tiered approach using different corridor widths for prioritizing conservation and mitigation actions, such as fence removal or conservation easements. PMID:23166656

Comparing habitat suitability and connectivity modeling methods for conserving pronghorn migrations.

PubMed

Poor, Erin E; Loucks, Colby; Jakes, Andrew; Urban, Dean L

2012-01-01

Terrestrial long-distance migrations are declining globally: in North America, nearly 75% have been lost. Yet there has been limited research comparing habitat suitability and connectivity models to identify migration corridors across increasingly fragmented landscapes. Here we use pronghorn (Antilocapra americana) migrations in prairie habitat to compare two types of models that identify habitat suitability: maximum entropy (Maxent) and expert-based (Analytic Hierarchy Process). We used distance to wells, distance to water, NDVI, land cover, distance to roads, terrain shape and fence presence to parameterize the models. We then used the output of these models as cost surfaces to compare two common connectivity models, least-cost modeling (LCM) and circuit theory. Using pronghorn movement data from spring and fall migrations, we identified potential migration corridors by combining each habitat suitability model with each connectivity model. The best performing model combination was Maxent with LCM corridors across both seasons. Maxent out-performed expert-based habitat suitability models for both spring and fall migrations. However, expert-based corridors can perform relatively well and are a cost-effective alternative if species location data are unavailable. Corridors created using LCM out-performed circuit theory, as measured by the number of pronghorn GPS locations present within the corridors. We suggest the use of a tiered approach using different corridor widths for prioritizing conservation and mitigation actions, such as fence removal or conservation easements.

Reference standards, judges, and comparison subjects: roles for experts in evaluating system performance.

PubMed

Hripcsak, George; Wilcox, Adam

2002-01-01

Medical informatics systems are often designed to perform at the level of human experts. Evaluation of the performance of these systems is often constrained by lack of reference standards, either because the appropriate response is not known or because no simple appropriate response exists. Even when performance can be assessed, it is not always clear whether the performance is sufficient or reasonable. These challenges can be addressed if an evaluator enlists the help of clinical domain experts. 1) The experts can carry out the same tasks as the system, and then their responses can be combined to generate a reference standard. 2)The experts can judge the appropriateness of system output directly. 3) The experts can serve as comparison subjects with which the system can be compared. These are separate roles that have different implications for study design, metrics, and issues of reliability and validity. Diagrams help delineate the roles of experts in complex study designs.

Supporting community annotation and user collaboration in the integrated microbial genomes (IMG) system.

PubMed

Chen, I-Min A; Markowitz, Victor M; Palaniappan, Krishna; Szeto, Ernest; Chu, Ken; Huang, Jinghua; Ratner, Anna; Pillay, Manoj; Hadjithomas, Michalis; Huntemann, Marcel; Mikhailova, Natalia; Ovchinnikova, Galina; Ivanova, Natalia N; Kyrpides, Nikos C

2016-04-26

The exponential growth of genomic data from next generation technologies renders traditional manual expert curation effort unsustainable. Many genomic systems have included community annotation tools to address the problem. Most of these systems adopted a "Wiki-based" approach to take advantage of existing wiki technologies, but encountered obstacles in issues such as usability, authorship recognition, information reliability and incentive for community participation. Here, we present a different approach, relying on tightly integrated method rather than "Wiki-based" method, to support community annotation and user collaboration in the Integrated Microbial Genomes (IMG) system. The IMG approach allows users to use existing IMG data warehouse and analysis tools to add gene, pathway and biosynthetic cluster annotations, to analyze/reorganize contigs, genes and functions using workspace datasets, and to share private user annotations and workspace datasets with collaborators. We show that the annotation effort using IMG can be part of the research process to overcome the user incentive and authorship recognition problems thus fostering collaboration among domain experts. The usability and reliability issues are addressed by the integration of curated information and analysis tools in IMG, together with DOE Joint Genome Institute (JGI) expert review. By incorporating annotation operations into IMG, we provide an integrated environment for users to perform deeper and extended data analysis and annotation in a single system that can lead to publications and community knowledge sharing as shown in the case studies.

The Usability Analysis of an E-Learning Environment

ERIC Educational Resources Information Center

Torun, Fulya; Tekedere, Hakan

2015-01-01

In this research, an E-learning environment is developed for the teacher candidates taking the course on Scientific Research Methods. The course contents were adapted to one of the constructivist approach models referred to as 5E, and an expert opinion was received for the compliance of this model. An usability analysis was also performed to…

Group prioritisation with unknown expert weights in incomplete linguistic context

NASA Astrophysics Data System (ADS)

Cheng, Dong; Cheng, Faxin; Zhou, Zhili; Wang, Juan

2017-09-01

In this paper, we study a group prioritisation problem in situations when the expert weights are completely unknown and their judgement preferences are linguistic and incomplete. Starting from the theory of relative entropy (RE) and multiplicative consistency, an optimisation model is provided for deriving an individual priority vector without estimating the missing value(s) of an incomplete linguistic preference relation. In order to address the unknown expert weights in the group aggregating process, we define two new kinds of expert weight indicators based on RE: proximity entropy weight and similarity entropy weight. Furthermore, a dynamic-adjusting algorithm (DAA) is proposed to obtain an objective expert weight vector and capture the dynamic properties involved in it. Unlike the extant literature of group prioritisation, the proposed RE approach does not require pre-allocation of expert weights and can solve incomplete preference relations. An interesting finding is that once all the experts express their preference relations, the final expert weight vector derived from the DAA is fixed irrespective of the initial settings of expert weights. Finally, an application example is conducted to validate the effectiveness and robustness of the RE approach.

Assigning clinical codes with data-driven concept representation on Dutch clinical free text.

PubMed

Scheurwegs, Elyne; Luyckx, Kim; Luyten, Léon; Goethals, Bart; Daelemans, Walter

2017-05-01

Clinical codes are used for public reporting purposes, are fundamental to determining public financing for hospitals, and form the basis for reimbursement claims to insurance providers. They are assigned to a patient stay to reflect the diagnosis and performed procedures during that stay. This paper aims to enrich algorithms for automated clinical coding by taking a data-driven approach and by using unsupervised and semi-supervised techniques for the extraction of multi-word expressions that convey a generalisable medical meaning (referred to as concepts). Several methods for extracting concepts from text are compared, two of which are constructed from a large unannotated corpus of clinical free text. A distributional semantic model (i.c. the word2vec skip-gram model) is used to generalize over concepts and retrieve relations between them. These methods are validated on three sets of patient stay data, in the disease areas of urology, cardiology, and gastroenterology. The datasets are in Dutch, which introduces a limitation on available concept definitions from expert-based ontologies (e.g. UMLS). The results show that when expert-based knowledge in ontologies is unavailable, concepts derived from raw clinical texts are a reliable alternative. Both concepts derived from raw clinical texts perform and concepts derived from expert-created dictionaries outperform a bag-of-words approach in clinical code assignment. Adding features based on tokens that appear in a semantically similar context has a positive influence for predicting diagnostic codes. Furthermore, the experiments indicate that a distributional semantics model can find relations between semantically related concepts in texts but also introduces erroneous and redundant relations, which can undermine clinical coding performance. Copyright © 2017. Published by Elsevier Inc.

AutoNR: an automated system that measures ECAP thresholds with the Nucleus Freedom cochlear implant via machine intelligence.

PubMed

Botros, Andrew; van Dijk, Bas; Killian, Matthijs

2007-05-01

AutoNRT is an automated system that measures electrically evoked compound action potential (ECAP) thresholds from the auditory nerve with the Nucleus Freedom cochlear implant. ECAP thresholds along the electrode array are useful in objectively fitting cochlear implant systems for individual use. This paper provides the first detailed description of the AutoNRT algorithm and its expert systems, and reports the clinical success of AutoNRT to date. AutoNRT determines thresholds by visual detection, using two decision tree expert systems that automatically recognise ECAPs. The expert systems are guided by a dataset of 5393 neural response measurements. The algorithm approaches threshold from lower stimulus levels, ensuring recipient safety during postoperative measurements. Intraoperative measurements use the same algorithm but proceed faster by beginning at stimulus levels much closer to threshold. When searching for ECAPs, AutoNRT uses a highly specific expert system (specificity of 99% during training, 96% during testing; sensitivity of 91% during training, 89% during testing). Once ECAPs are established, AutoNRT uses an unbiased expert system to determine an accurate threshold. Throughout the execution of the algorithm, recording parameters (such as implant amplifier gain) are automatically optimised when needed. In a study that included 29 intraoperative and 29 postoperative subjects (a total of 418 electrodes), AutoNRT determined a threshold in 93% of cases where a human expert also determined a threshold. When compared to the median threshold of multiple human observers on 77 randomly selected electrodes, AutoNRT performed as accurately as the 'average' clinician. AutoNRT has demonstrated a high success rate and a level of performance that is comparable with human experts. It has been used in many clinics worldwide throughout the clinical trial and commercial launch of Nucleus Custom Sound Suite, significantly streamlining the clinical procedures associated with cochlear implant use.

Identifying Key Performance Indicators for Holistic Hospital Management with a Modified DEMATEL Approach.

PubMed

Si, Sheng-Li; You, Xiao-Yue; Liu, Hu-Chen; Huang, Jia

2017-08-19

Performance analysis is an important way for hospitals to achieve higher efficiency and effectiveness in providing services to their customers. The performance of the healthcare system can be measured by many indicators, but it is difficult to improve them simultaneously due to the limited resources. A feasible way is to identify the central and influential indicators to improve healthcare performance in a stepwise manner. In this paper, we propose a hybrid multiple criteria decision making (MCDM) approach to identify key performance indicators (KPIs) for holistic hospital management. First, through integrating evidential reasoning approach and interval 2-tuple linguistic variables, various assessments of performance indicators provided by healthcare experts are modeled. Then, the decision making trial and evaluation laboratory (DEMATEL) technique is adopted to build an interactive network and visualize the causal relationships between the performance indicators. Finally, an empirical case study is provided to demonstrate the proposed approach for improving the efficiency of healthcare management. The results show that "accidents/adverse events", "nosocomial infection", ''incidents/errors", "number of operations/procedures" are significant influential indicators. Also, the indicators of "length of stay", "bed occupancy" and "financial measures" play important roles in performance evaluation of the healthcare organization. The proposed decision making approach could be considered as a reference for healthcare administrators to enhance the performance of their healthcare institutions.

The Pacor 2 expert system: A case-based reasoning approach to troubleshooting

NASA Technical Reports Server (NTRS)

Sary, Charisse

1994-01-01

The Packet Processor 2 (Pacor 2) Data Capture Facility (DCF) acquires, captures, and performs level-zero processing of packet telemetry for spaceflight missions that adhere to communication services recommendations established by the Consultative Committee for Space Data Systems (CCSDS). A major goal of this project is to reduce life-cycle costs. One way to achieve this goal is to increase automation. Through automation, using expert systems, and other technologies, staffing requirements will remain static, which will enable the same number of analysts to support more missions. Analysts provide packet telemetry data evaluation and analysis services for all data received. Data that passes this evaluation is forwarded to the Data Distribution Facility (DDF) and released to scientists. Through troubleshooting, data that fails this evaluation is dumped and analyzed to determine if its quality can be improved before it is released. This paper describes a proof-of-concept prototype that troubleshoots data quality problems. The Pacor 2 expert system prototype uses the case-based reasoning (CBR) approach to development, an alternative to a rule-based approach. Because Pacor 2 is not operational, the prototype has been developed using cases that describe existing troubleshooting experience from currently operating missions. Through CBR, this experience will be available to analysts when Pacor 2 becomes operational. As Pacor 2 unique experience is gained, analysts will update the case base. In essence, analysts are training the system as they learn. Once the system has learned the cases most likely to recur, it can serve as an aide to inexperienced analysts, a refresher to experienced analysts for infrequently occurring problems, or a training tool for new analysts. The Expert System Development Methodology (ESDM) is being used to guide development.

Key attributes of expert NRL referees.

PubMed

Morris, Gavin; O'Connor, Donna

2017-05-01

Experiential knowledge of elite National Rugby League (NRL) referees was investigated to determine the key attributes contributing to expert officiating performance. Fourteen current first-grade NRL referees were asked to identify the key attributes they believed contributed to their expert refereeing performance. The modified Delphi method involved a 3-round process of an initial semi-structured interview followed by 2 questionnaires to reach consensus of opinion. The data revealed 25 attributes that were rated as most important that underpin expert NRL refereeing performance. Results illustrate the significance of the cognitive category, with the top 6 ranked attributes all cognitive skills. Of these, the referees ranked decision-making accuracy as the most important attribute, followed by reading the game, communication, game understanding, game management and knowledge of the rules. Player rapport, positioning and teamwork were the top ranked game skill attributes underpinning performance excellence. Expert referees also highlighted a number of psychological attributes (e.g., concentration, composure and mental toughness) that were significant to performance. There were only 2 physiological attributes (fitness, aerobic endurance) that were identified as significant to elite officiating performance. In summary, expert consensus was attained which successfully provided a hierarchy of the most significant attributes of expert NRL refereeing performance.

Extracting BI-RADS Features from Portuguese Clinical Texts

PubMed Central

Nassif, Houssam; Cunha, Filipe; Moreira, Inês C.; Cruz-Correia, Ricardo; Sousa, Eliana; Page, David; Burnside, Elizabeth; Dutra, Inês

2013-01-01

In this work we build the first BI-RADS parser for Portuguese free texts, modeled after existing approaches to extract BI-RADS features from English medical records. Our concept finder uses a semantic grammar based on the BIRADS lexicon and on iterative transferred expert knowledge. We compare the performance of our algorithm to manual annotation by a specialist in mammography. Our results show that our parser’s performance is comparable to the manual method. PMID:23797461

Intraosseous access can be taught to medical students using the four-step approach.

PubMed

Afzali, Monika; Kvisselgaard, Ask Daffy; Lyngeraa, Tobias Stenbjerg; Viggers, Sandra

2017-03-02

The intraosseous (IO) access is an alternative route for vascular access when peripheral intravascular catheterization cannot be obtained. In Denmark the IO access is reported as infrequently trained and used. The aim of this pilot study was to investigate if medical students can obtain competencies in IO access when taught by a modified Walker and Peyton's four-step approach. Nineteen students attended a human cadaver course in emergency procedures. A lecture was followed by a workshop. Fifteen students were presented with a case where IO access was indicated and their performance was evaluated by an objective structured clinical examination (OSCE) and rated using a weighted checklist. To evaluate the validity of the checklist, three raters rated performance and Cohen's kappa was performed to assess inter-rater reliability (IRR). To examine the strength of the overall IRR, Randolph's free-marginal multi rater kappa was used. A maximum score of 15 points was obtained by nine (60%) of the participants and two participants (13%) scored 13 points with all three raters. Only one participant failed more than one item on the checklist. The expert rater rated lower with a mean score of 14.2 versus the non-expert raters with mean 14.6 and 14.3. The overall IRR calculated with Randolph's free-marginal multi rater kappa was 0.71. The essentials of the IO access procedure can be taught to medical students using a modified version of the Walker and Peyton's four-step approach and the checklist used was found reliable.

Assessing Residents' Readiness for OR Autonomy: A Qualitative Descriptive Study of Expert Surgical Teachers' Best Practices.

PubMed

Chen, Xiaodong Phoenix; Sullivan, Amy M; Alseidi, Adnan; Kwakye, Gifty; Smink, Douglas S

Providing resident autonomy in the operating room (OR) is one of the major challenges for surgical educators today. The purpose of this study was to explore what approaches expert surgical teachers use to assess residents' readiness for autonomy in the OR. We particularly focused on the assessments that experts make prior to conducting the surgical time-out. We conducted semistructured in-depth interviews with expert surgical teachers from March 2016 to September 2016. Purposeful sampling and snowball sampling were applied to identify and recruit expert surgical teachers from general surgery residency programs across the United States to represent a range of clinical subspecialties. All interviews were audio-recorded, deidentified, and transcribed. We applied the Framework Method of content analysis, discussed and reached final consensus on the themes. We interviewed 15 expert teachers from 9 institutions. The majority (13/15) were Program or Associate Program Directors; 47% (7/15) primarily performed complex surgical operations (e.g., endocrine surgery). Five themes regarding how expert surgical teachers determine residents' readiness for OR autonomy before the surgical time-out emerged. These included 3 domains of evidence elicited about the resident (resident characteristics, medical knowledge, and beyond the current OR case), 1 variable relating to attending characteristics, and 1 variable composed of contextual factors. Experts obtained one or more examples of evidence, and adjusted residents' initial autonomy using factors from the attending variable and the context variable. Expert surgical teachers' assessments of residents' readiness for OR autonomy included 5 key components. Better understanding these inputs can contribute to both faculty and resident development, enabling increased resident autonomy and preparation for independent practice. Copyright © 2017 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

The Development of Expert Learners in the Classroom

ERIC Educational Resources Information Center

Rahman, Saemah; Mahmud, Zuria; Yassin, Siti Fatimah Mohd; Amir, Ruslin; Ilias, Khadijah Wan

2010-01-01

The term "expert learner" refers to students who are actively engaged with the materials learned and take responsibility for their own learning. Literature reviews suggested the use of metacognitive approach to help develop students to become expert learners. Research on development of expert learners can be traced from movements that…

Ultrasound Fracture Diagnosis in Space

NASA Technical Reports Server (NTRS)

Dulchavsky, Scott A.; Amponsah, David; Sargsyan, Ashot E.; Garcia, Kathleen M.; Hamilton, Douglas R.; vanHolsbeeck, Marnix

2010-01-01

Introduction: This ground-based investigation accumulated high-level clinical evidence on the sensitivity and specificity of point of care ultrasound performed by expert and novice users for the rapid diagnosis of musculoskeletal (MSK) injuries. We developed preliminary educational methodologies to provide just-in-time training of novice users by creating multi-media training tools and imaging procedures for non expert operators and evaluated the sensitivity and specificity of non-expert performed musculoskeletal ultrasound to diagnose acute injuries in a Level 1 Trauma Center. Methods: Patients with potential MSK injuries were identified in the emergency room. A focused MSK ultrasound was performed by expert operators and compared to standard radiographs. A repeat examination was performed by non-expert operators who received a short, just-in-time multimedia education aid. The sensitivity and specificity of the expert and novice ultrasound examinations were compared to gold standard radiography. Results: Over 800 patients were enrolled in this study. The sensitivity and specificity of expert performed ultrasound exceeded 98% for MSK injuries. Novice operators achieved 97% sensitivity and 99% specificity for targeted examinations with the greatest error in fractures involving the hand and foot. Conclusion: Point of care ultrasound is a sensitive and specific diagnostic test for MSK injury when performed by experts and just-in-time trained novice operators.

Assessment of competence in video-assisted thoracoscopic surgery lobectomy: A Danish nationwide study.

PubMed

Petersen, René Horsleben; Gjeraa, Kirsten; Jensen, Katrine; Møller, Lars Borgbjerg; Hansen, Henrik Jessen; Konge, Lars

2018-04-18

Competence in video-assisted thoracoscopic surgery lobectomy has previously been established on the basis of numbers of procedures performed, but this approach does not ensure competence. Specific assessment tools, such as the newly developed video-assisted thoracoscopic surgery lobectomy assessment tool, allow for structured and objective assessment of competence. Our aim was to provide validity evidence for the video-assisted thoracoscopic surgery lobectomy assessment tool. Video recordings of 60 video-assisted thoracoscopic surgery lobectomies performed by 18 thoracic surgeons were rated using the video-assisted thoracoscopic surgery lobectomy assessment tool. All 4 centers of thoracic surgery in Denmark participated in the study. Two video-assisted thoracoscopic surgery experts rated the videos. They were blinded to surgeon and center. The total internal consistency reliability Cronbach's alpha was 0.93. Inter-rater reliability between the 2 raters was Pearson's r = 0.71 (P < .001). The mean video-assisted thoracoscopic surgery lobectomy assessment tool scores for the 10 procedures performed by beginners were 22.1 (standard deviation [SD], 8.6) for the 28 procedures performed by the intermediate surgeons, 31.2 (SD, 4.4), and for the 20 procedures performed by experts 35.9 (SD, 2.9) (P < .001). Bonferroni post hoc tests showed that experts were significantly better than intermediates (P < .008) and beginners (P < .001). Intermediates' mean scores were significantly better than beginners (P < .001). The pass/fail standard calculated using the contrasting group's method was 31 points. One of the beginners passed, and 2 experts failed the test. Validity evidence was provided for a newly developed assessment tool for video-assisted thoracoscopic surgery lobectomy (video-assisted thoracoscopic surgery lobectomy assessment tool) in a clinical setting. The discriminatory ability among expert surgeons, intermediate surgeons, and beginners proved highly significant. The video-assisted thoracoscopic surgery lobectomy assessment tool could be an important aid in the future training and certification of thoracic surgeons. Copyright © 2018 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

Implementation of a near-zero fluoroscopy approach in interventional electrophysiology: impact of operator experience.

PubMed

Wannagat, Severin; Loehr, Lena; Lask, Sebastian; Völk, Katharina; Karaköse, Tamer; Özcelik, Cemil; Mügge, Andreas; Wutzler, Alexander

2018-04-01

Catheter ablation is performed under fluoroscopic guidance. Reduction of radiation dose for patients and staff is emphasized by current recommendations. Previous studies have shown that lower operator experience leads to increased radiation dose. On the other hand, less experienced operators may depend even more on fluoroscopic guidance. Our study aimed to evaluate feasibility and efficacy of a non-fluoroscopic approach in different training levels. From January 2017, a near-zero fluoroscopy approach was established in two centers. Four operators (beginner, 1st year fellow, 2nd year fellow, expert) were instructed to perform the complete procedure with the use of a 3-D mapping system without fluoroscopy. A historical cohort that underwent procedures with fluoroscopy use served as control group. Dose area product (DPA), procedure duration, acute procedural success, and complications were compared between the groups and for each operator. Procedures were performed in 157 patients. The first 100 patients underwent procedures with fluoroscopic guidance, the following 57 procedures were performed with the near-zero fluoroscopy approach. The results show a significant reduction in DPA for all operators immediately after implementation of the near-zero fluoroscopy protocol (control 637 ± 611 μGy/m 2 ; beginner 44.1 ± 79.5 μGy/m 2 , p = 0.002; 1st year fellow 24.3 ± 46.4.5 μGy/m 2 , p = 0.001; 2nd year fellow 130.3 ± 233.3 μGy/m 2 , p = 0.003; expert 9.3 ± 37.4 μGy/m 2 , P < 0.001). Procedure duration, acute success, and complications were not significantly different between the groups. Our results show a 90% reduction of DPA shortly after implementation of a near-zero fluoroscopy approach in interventional electrophysiology even in operators in training.

Relational machine learning for electronic health record-driven phenotyping.

PubMed

Peissig, Peggy L; Santos Costa, Vitor; Caldwell, Michael D; Rottscheit, Carla; Berg, Richard L; Mendonca, Eneida A; Page, David

2014-12-01

Electronic health records (EHR) offer medical and pharmacogenomics research unprecedented opportunities to identify and classify patients at risk. EHRs are collections of highly inter-dependent records that include biological, anatomical, physiological, and behavioral observations. They comprise a patient's clinical phenome, where each patient has thousands of date-stamped records distributed across many relational tables. Development of EHR computer-based phenotyping algorithms require time and medical insight from clinical experts, who most often can only review a small patient subset representative of the total EHR records, to identify phenotype features. In this research we evaluate whether relational machine learning (ML) using inductive logic programming (ILP) can contribute to addressing these issues as a viable approach for EHR-based phenotyping. Two relational learning ILP approaches and three well-known WEKA (Waikato Environment for Knowledge Analysis) implementations of non-relational approaches (PART, J48, and JRIP) were used to develop models for nine phenotypes. International Classification of Diseases, Ninth Revision (ICD-9) coded EHR data were used to select training cohorts for the development of each phenotypic model. Accuracy, precision, recall, F-Measure, and Area Under the Receiver Operating Characteristic (AUROC) curve statistics were measured for each phenotypic model based on independent manually verified test cohorts. A two-sided binomial distribution test (sign test) compared the five ML approaches across phenotypes for statistical significance. We developed an approach to automatically label training examples using ICD-9 diagnosis codes for the ML approaches being evaluated. Nine phenotypic models for each ML approach were evaluated, resulting in better overall model performance in AUROC using ILP when compared to PART (p=0.039), J48 (p=0.003) and JRIP (p=0.003). ILP has the potential to improve phenotyping by independently delivering clinically expert interpretable rules for phenotype definitions, or intuitive phenotypes to assist experts. Relational learning using ILP offers a viable approach to EHR-driven phenotyping. Copyright © 2014 Elsevier Inc. All rights reserved.

The Generic Spacecraft Analyst Assistant (GenSAA): A tool for automating spacecraft monitoring with expert systems

NASA Technical Reports Server (NTRS)

Hughes, Peter M.; Luczak, Edward C.

1991-01-01

Flight Operations Analysts (FOAs) in the Payload Operations Control Center (POCC) are responsible for monitoring a satellite's health and safety. As satellites become more complex and data rates increase, FOAs are quickly approaching a level of information saturation. The FOAs in the spacecraft control center for the COBE (Cosmic Background Explorer) satellite are currently using a fault isolation expert system named the Communications Link Expert Assistance Resource (CLEAR), to assist in isolating and correcting communications link faults. Due to the success of CLEAR and several other systems in the control center domain, many other monitoring and fault isolation expert systems will likely be developed to support control center operations during the early 1990s. To facilitate the development of these systems, a project was initiated to develop a domain specific tool, named the Generic Spacecraft Analyst Assistant (GenSAA). GenSAA will enable spacecraft analysts to easily build simple real-time expert systems that perform spacecraft monitoring and fault isolation functions. Lessons learned during the development of several expert systems at Goddard, thereby establishing the foundation of GenSAA's objectives and offering insights in how problems may be avoided in future project, are described. This is followed by a description of the capabilities, architecture, and usage of GenSAA along with a discussion of its application to future NASA missions.

Automatic classification of scar tissue in late gadolinium enhancement cardiac MRI for the assessment of left-atrial wall injury after radiofrequency ablation

PubMed Central

Morris, Alan; Burgon, Nathan; McGann, Christopher; MacLeod, Robert; Cates, Joshua

2013-01-01

Radiofrequency ablation is a promising procedure for treating atrial fibrillation (AF) that relies on accurate lesion delivery in the left atrial (LA) wall for success. Late Gadolinium Enhancement MRI (LGE MRI) at three months post-ablation has proven effective for noninvasive assessment of the location and extent of scar formation, which are important factors for predicting patient outcome and planning of redo ablation procedures. We have developed an algorithm for automatic classification in LGE MRI of scar tissue in the LA wall and have evaluated accuracy and consistency compared to manual scar classifications by expert observers. Our approach clusters voxels based on normalized intensity and was chosen through a systematic comparison of the performance of multivariate clustering on many combinations of image texture. Algorithm performance was determined by overlap with ground truth, using multiple overlap measures, and the accuracy of the estimation of the total amount of scar in the LA. Ground truth was determined using the STAPLE algorithm, which produces a probabilistic estimate of the true scar classification from multiple expert manual segmentations. Evaluation of the ground truth data set was based on both inter- and intra-observer agreement, with variation among expert classifiers indicating the difficulty of scar classification for a given a dataset. Our proposed automatic scar classification algorithm performs well for both scar localization and estimation of scar volume: for ground truth datasets considered easy, variability from the ground truth was low; for those considered difficult, variability from ground truth was on par with the variability across experts. PMID:24236224

Towards actionable international comparisons of health system performance: expert revision of the OECD framework and quality indicators.

PubMed

Carinci, F; Van Gool, K; Mainz, J; Veillard, J; Pichora, E C; Januel, J M; Arispe, I; Kim, S M; Klazinga, N S

2015-04-01

To review and update the conceptual framework, indicator content and research priorities of the Organisation for Economic Cooperation and Development's (OECD) Health Care Quality Indicators (HCQI) project, after a decade of collaborative work. A structured assessment was carried out using a modified Delphi approach, followed by a consensus meeting, to assess the suite of HCQI for international comparisons, agree on revisions to the original framework and set priorities for research and development. International group of countries participating to OECD projects. Members of the OECD HCQI expert group. A reference matrix, based on a revised performance framework, was used to map and assess all seventy HCQI routinely calculated by the OECD expert group. A total of 21 indicators were agreed to be excluded, due to the following concerns: (i) relevance, (ii) international comparability, particularly where heterogeneous coding practices might induce bias, (iii) feasibility, when the number of countries able to report was limited and the added value did not justify sustained effort and (iv) actionability, for indicators that were unlikely to improve on the basis of targeted policy interventions. The revised OECD framework for HCQI represents a new milestone of a long-standing international collaboration among a group of countries committed to building common ground for performance measurement. The expert group believes that the continuation of this work is paramount to provide decision makers with a validated toolbox to directly act on quality improvement strategies. © The Author 2015. Published by Oxford University Press in association with the International Society for Quality in Health Care; all rights reserved.

Automatic classification of scar tissue in late gadolinium enhancement cardiac MRI for the assessment of left-atrial wall injury after radiofrequency ablation

NASA Astrophysics Data System (ADS)

Perry, Daniel; Morris, Alan; Burgon, Nathan; McGann, Christopher; MacLeod, Robert; Cates, Joshua

2012-03-01

Radiofrequency ablation is a promising procedure for treating atrial fibrillation (AF) that relies on accurate lesion delivery in the left atrial (LA) wall for success. Late Gadolinium Enhancement MRI (LGE MRI) at three months post-ablation has proven effective for noninvasive assessment of the location and extent of scar formation, which are important factors for predicting patient outcome and planning of redo ablation procedures. We have developed an algorithm for automatic classification in LGE MRI of scar tissue in the LA wall and have evaluated accuracy and consistency compared to manual scar classifications by expert observers. Our approach clusters voxels based on normalized intensity and was chosen through a systematic comparison of the performance of multivariate clustering on many combinations of image texture. Algorithm performance was determined by overlap with ground truth, using multiple overlap measures, and the accuracy of the estimation of the total amount of scar in the LA. Ground truth was determined using the STAPLE algorithm, which produces a probabilistic estimate of the true scar classification from multiple expert manual segmentations. Evaluation of the ground truth data set was based on both inter- and intra-observer agreement, with variation among expert classifiers indicating the difficulty of scar classification for a given a dataset. Our proposed automatic scar classification algorithm performs well for both scar localization and estimation of scar volume: for ground truth datasets considered easy, variability from the ground truth was low; for those considered difficult, variability from ground truth was on par with the variability across experts.

Localized Smart-Interpretation

NASA Astrophysics Data System (ADS)

Lundh Gulbrandsen, Mats; Mejer Hansen, Thomas; Bach, Torben; Pallesen, Tom

2014-05-01

The complex task of setting up a geological model consists not only of combining available geological information into a conceptual plausible model, but also requires consistency with availably data, e.g. geophysical data. However, in many cases the direct geological information, e.g borehole samples, are very sparse, so in order to create a geological model, the geologist needs to rely on the geophysical data. The problem is however, that the amount of geophysical data in many cases are so vast that it is practically impossible to integrate all of them in the manual interpretation process. This means that a lot of the information available from the geophysical surveys are unexploited, which is a problem, due to the fact that the resulting geological model does not fulfill its full potential and hence are less trustworthy. We suggest an approach to geological modeling that 1. allow all geophysical data to be considered when building the geological model 2. is fast 3. allow quantification of geological modeling. The method is constructed to build a statistical model, f(d,m), describing the relation between what the geologists interpret, d, and what the geologist knows, m. The para- meter m reflects any available information that can be quantified, such as geophysical data, the result of a geophysical inversion, elevation maps, etc... The parameter d reflects an actual interpretation, such as for example the depth to the base of a ground water reservoir. First we infer a statistical model f(d,m), by examining sets of actual interpretations made by a geological expert, [d1, d2, ...], and the information used to perform the interpretation; [m1, m2, ...]. This makes it possible to quantify how the geological expert performs interpolation through f(d,m). As the geological expert proceeds interpreting, the number of interpreted datapoints from which the statistical model is inferred increases, and therefore the accuracy of the statistical model increases. When a model f(d,m) successfully has been inferred, we are able to simulate how the geological expert would perform an interpretation given some external information m, through f(d|m). We will demonstrate this method applied on geological interpretation and densely sampled airborne electromagnetic data. In short, our goal is to build a statistical model describing how a geological expert performs geological interpretation given some geophysical data. We then wish to use this statistical model to perform semi automatic interpretation, everywhere where such geophysical data exist, in a manner consistent with the choices made by a geological expert. Benefits of such a statistical model are that 1. it provides a quantification of how a geological expert performs interpretation based on available diverse data 2. all available geophysical information can be used 3. it allows much faster interpretation of large data sets.

Multi-task feature learning by using trace norm regularization

NASA Astrophysics Data System (ADS)

Jiangmei, Zhang; Binfeng, Yu; Haibo, Ji; Wang, Kunpeng

2017-11-01

Multi-task learning can extract the correlation of multiple related machine learning problems to improve performance. This paper considers applying the multi-task learning method to learn a single task. We propose a new learning approach, which employs the mixture of expert model to divide a learning task into several related sub-tasks, and then uses the trace norm regularization to extract common feature representation of these sub-tasks. A nonlinear extension of this approach by using kernel is also provided. Experiments conducted on both simulated and real data sets demonstrate the advantage of the proposed approach.

Informing Public Perceptions About Climate Change: A 'Mental Models' Approach.

PubMed

Wong-Parodi, Gabrielle; Bruine de Bruin, Wändi

2017-10-01

As the specter of climate change looms on the horizon, people will face complex decisions about whether to support climate change policies and how to cope with climate change impacts on their lives. Without some grasp of the relevant science, they may find it hard to make informed decisions. Climate experts therefore face the ethical need to effectively communicate to non-expert audiences. Unfortunately, climate experts may inadvertently violate the maxims of effective communication, which require sharing communications that are truthful, brief, relevant, clear, and tested for effectiveness. Here, we discuss the 'mental models' approach towards developing communications, which aims to help experts to meet the maxims of effective communications, and to better inform the judgments and decisions of non-expert audiences.

Expert judgement and uncertainty quantification for climate change

NASA Astrophysics Data System (ADS)

Oppenheimer, Michael; Little, Christopher M.; Cooke, Roger M.

2016-05-01

Expert judgement is an unavoidable element of the process-based numerical models used for climate change projections, and the statistical approaches used to characterize uncertainty across model ensembles. Here, we highlight the need for formalized approaches to unifying numerical modelling with expert judgement in order to facilitate characterization of uncertainty in a reproducible, consistent and transparent fashion. As an example, we use probabilistic inversion, a well-established technique used in many other applications outside of climate change, to fuse two recent analyses of twenty-first century Antarctic ice loss. Probabilistic inversion is but one of many possible approaches to formalizing the role of expert judgement, and the Antarctic ice sheet is only one possible climate-related application. We recommend indicators or signposts that characterize successful science-based uncertainty quantification.

Using hybrid expert system approaches for engineering applications

NASA Technical Reports Server (NTRS)

Allen, R. H.; Boarnet, M. G.; Culbert, C. J.; Savely, R. T.

1987-01-01

In this paper, the use of hybrid expert system shells and hybrid (i.e., algorithmic and heuristic) approaches for solving engineering problems is reported. Aspects of various engineering problem domains are reviewed for a number of examples with specific applications made to recently developed prototype expert systems. Based on this prototyping experience, critical evaluations of and comparisons between commercially available tools, and some research tools, in the United States and Australia, and their underlying problem-solving paradigms are made. Characteristics of the implementation tool and the engineering domain are compared and practical software engineering issues are discussed with respect to hybrid tools and approaches. Finally, guidelines are offered with the hope that expert system development will be less time consuming, more effective, and more cost-effective than it has been in the past.

Role of occupational health services in the assessment and management of indoor air quality problems.

PubMed

Carrer, Paolo; Muzi, Giacomo

2011-01-01

The role of the occupational health services in the assessment and management of indoor air quality (IAQ) problems in non-industrial sectors (offices, banks, etc.) has been discussed by experts of the ICOH Scientific Committee on IAQ and Health and has been proposed as follow: 1. Collaboration in risk assessment--risk management; 2. Questionnaire survey; 3. Health surveillance (only when periodical health surveillance is already performed for other risks or when specific clinical examination of workers is required); 4. Health promotion (programs for a better IAQ management). A team approach with cooperation between medical and technical experts is recommended in the assessment and management of indoor air quality problems.

A consensus on liquid biopsy from the 2016 Chinese Lung Cancer Summit expert panel.

PubMed

Wu, Yi-Long; Wang, Chang-Li; Sun, Yan; Liao, Mei-Lin; Guan, Zhong-Zhen; Yang, Zhi-Min; Zhou, Qing-Hua; Lu, Shun; Cheng, Ying; Liu, Xiao-Qing; Zhang, Xu-Chao; Zhou, Caicun; Wang, Jie; Zhou, Qing; Song, Yong; Han, Bao-Hui; Ma, Zhi-Yong; Yang, Fan; Wang, Qun; Chuai, Shao-Kun; Shao, Yang; He, Wei; Zhu, Guanshan; Xiong, Lei; Wang, Jian-Jun; Chen, Ke-Neng; Zhang, Li; Mao, Wei-Min; Ma, Sheng-Lin; Feng, Ji-Feng; Yang, Xue-Ning; Xu, Lin; Chen, Gang; Zhao, Jian; Song, Qi-Bin; Shen-Tu, Yang; Qiao, Gui-Bin; Yu, Ding; Yu, Shi-Ying; Hu, Yi; Chen, Ming; Chen, Gong-Yan; Fan, Yun; Zhang, He-Long; Liang, Jun; Zhu, Guang-Ying; Cui, Jiu-Wei; Yang, Jin-Ji; Zhao, Qiong; Zhao, Ming-Fang; Lu, You; Chang, Jian-Hua; Li, Jun-Ling; Yang, Yue; Hu, Jie; Gu, Chun-Dong; Zhang, Yi-Chen; Zhong, Wen-Zhao

2017-01-01

The diagnosis and treatment of lung cancer have evolved into the era of precision medicine. Liquid biopsy, a minimally invasive approach, has emerged as a promising practice in genetic profiling and monitoring of lung cancer. Translating liquid biopsy from bench to bedside has encountered various challenges, including technique selection, protocol standardisation, data analysis and cost management. Regarding these challenges, the 2016 Chinese Lung Cancer Summit expert panel organised a trilateral forum involving oncologists, clinicians, clinical researchers, and industrial expertise on the 13th Chinese Lung Cancer Summit to formally discuss these controversies. Six consensuses were reached to guide the use of liquid biopsy and perform precision medicine in both clinic and research.

Knowledge base rule partitioning design for CLIPS

NASA Technical Reports Server (NTRS)

Mainardi, Joseph D.; Szatkowski, G. P.

1990-01-01

This describes a knowledge base (KB) partitioning approach to solve the problem of real-time performance using the CLIPS AI shell when containing large numbers of rules and facts. This work is funded under the joint USAF/NASA Advanced Launch System (ALS) Program as applied research in expert systems to perform vehicle checkout for real-time controller and diagnostic monitoring tasks. The Expert System advanced development project (ADP-2302) main objective is to provide robust systems responding to new data frames of 0.1 to 1.0 second intervals. The intelligent system control must be performed within the specified real-time window, in order to meet the demands of the given application. Partitioning the KB reduces the complexity of the inferencing Rete net at any given time. This reduced complexity improves performance but without undo impacts during load and unload cycles. The second objective is to produce highly reliable intelligent systems. This requires simple and automated approaches to the KB verification & validation task. Partitioning the KB reduces rule interaction complexity overall. Reduced interaction simplifies the V&V testing necessary by focusing attention only on individual areas of interest. Many systems require a robustness that involves a large number of rules, most of which are mutually exclusive under different phases or conditions. The ideal solution is to control the knowledge base by loading rules that directly apply for that condition, while stripping out all rules and facts that are not used during that cycle. The practical approach is to cluster rules and facts into associated 'blocks'. A simple approach has been designed to control the addition and deletion of 'blocks' of rules and facts, while allowing real-time operations to run freely. Timing tests for real-time performance for specific machines under R/T operating systems have not been completed but are planned as part of the analysis process to validate the design.

Evaluation of a Performance-Based Expert Elicitation: WHO Global Attribution of Foodborne Diseases

PubMed Central

Aspinall, W. P.; Cooke, R. M.; Havelaar, A. H.; Hoffmann, S.; Hald, T.

2016-01-01

For many societally important science-based decisions, data are inadequate, unreliable or non-existent, and expert advice is sought. In such cases, procedures for eliciting structured expert judgments (SEJ) are increasingly used. This raises questions regarding validity and reproducibility. This paper presents new findings from a large-scale international SEJ study intended to estimate the global burden of foodborne disease on behalf of WHO. The study involved 72 experts distributed over 134 expert panels, with panels comprising thirteen experts on average. Elicitations were conducted in five languages. Performance-based weighted solutions for target questions of interest were formed for each panel. These weights were based on individual expert’s statistical accuracy and informativeness, determined using between ten and fifteen calibration variables from the experts' field with known values. Equal weights combinations were also calculated. The main conclusions on expert performance are: (1) SEJ does provide a science-based method for attribution of the global burden of foodborne diseases; (2) equal weighting of experts per panel increased statistical accuracy to acceptable levels, but at the cost of informativeness; (3) performance-based weighting increased informativeness, while retaining accuracy; (4) due to study constraints individual experts’ accuracies were generally lower than in other SEJ studies, and (5) there was a negative correlation between experts' informativeness and statistical accuracy which attenuated as accuracy improved, revealing that the least accurate experts drive the negative correlation. It is shown, however, that performance-based weighting has the ability to yield statistically accurate and informative combinations of experts' judgments, thereby offsetting this contrary influence. The present findings suggest that application of SEJ on a large scale is feasible, and motivate the development of enhanced training and tools for remote elicitation of multiple, internationally-dispersed panels. PMID:26930595

A Step-Wise Approach to Elicit Triangular Distributions

NASA Technical Reports Server (NTRS)

Greenberg, Marc W.

2013-01-01

Adapt/combine known methods to demonstrate an expert judgment elicitation process that: 1.Models expert's inputs as a triangular distribution, 2.Incorporates techniques to account for expert bias and 3.Is structured in a way to help justify expert's inputs. This paper will show one way of "extracting" expert opinion for estimating purposes. Nevertheless, as with most subjective methods, there are many ways to do this.

A Comparison of Perceptual Strategies Utilized by Expert and Novice Performers.

ERIC Educational Resources Information Center

Moore, John O.

This paper reviews the literature comparing the perceptual strategies of expert and novice sport/skill performers in ball sports to specifically identify where differences exist. The following comparisons were made: (1) experts attend to fewer, somewhat different visual cues than beginners; (2) experts exhibit an ability to utilize advanced…

Cooperating Expert Systems For Space Station Power Distribution Management

NASA Astrophysics Data System (ADS)

Nguyen, T. A.; Chiou, W. C.

1987-02-01

In a complex system such as the manned Space Station, it is deem necessary that many expert systems must perform tasks in a concurrent and cooperative manner. An important question arise is: what cooperative-task-performing models are appropriate for multiple expert systems to jointly perform tasks. The solution to this question will provide a crucial automation design criteria for the Space Station complex systems architecture. Based on a client/server model for performing tasks, we have developed a system that acts as a front-end to support loosely-coupled communications between expert systems running on multiple Symbolics machines. As an example, we use two ART*-based expert systems to demonstrate the concept of parallel symbolic manipulation for power distribution management and dynamic load planner/scheduler in the simulated Space Station environment. This on-going work will also explore other cooperative-task-performing models as alternatives which can evaluate inter and intra expert system communication mechanisms. It will be served as a testbed and a bench-marking tool for other Space Station expert subsystem communication and information exchange.

The Field of View is More Useful in Golfers than Regular Exercisers

PubMed Central

Murphy, Karen

2017-01-01

Superior visual attention skills are vital for excellent sports performance. This study used a cognitive skills approach to examine expert and novice differences in a visual spatial attention task. Thirty-two males aged 18 to 42 years completed this study in return for course credit or monetary incentive. Participants were expert golfers (N = 18) or exercise controls (N = 14). Spatial attention was assessed using the useful field of view task which required participants to locate a target shown 10°, 20°, and 30° of eccentricity from centre in very brief presentations. At each degree of eccentricity, golfers were more accurate at locating the target than the exercise controls. These results provide support for the broad transfer hypothesis by demonstrating a link between golf expertise and better performance on an objective measure of spatial attention skills. Therefore, it appears that sports expertise can transfer to expertise in non-sport related tasks. PMID:28450973

Evaluating progressive-rendering algorithms in appearance design tasks.

PubMed

Jiawei Ou; Karlik, Ondrej; Křivánek, Jaroslav; Pellacini, Fabio

2013-01-01

Progressive rendering is becoming a popular alternative to precomputational approaches to appearance design. However, progressive algorithms create images exhibiting visual artifacts at early stages. A user study investigated these artifacts' effects on user performance in appearance design tasks. Novice and expert subjects performed lighting and material editing tasks with four algorithms: random path tracing, quasirandom path tracing, progressive photon mapping, and virtual-point-light rendering. Both the novices and experts strongly preferred path tracing to progressive photon mapping and virtual-point-light rendering. None of the participants preferred random path tracing to quasirandom path tracing or vice versa; the same situation held between progressive photon mapping and virtual-point-light rendering. The user workflow didn’t differ significantly with the four algorithms. The Web Extras include a video showing how four progressive-rendering algorithms converged (at http://youtu.be/ck-Gevl1e9s), the source code used, and other supplementary materials.

An evidential reasoning-based AHP approach for the selection of environmentally-friendly designs

DOE Office of Scientific and Technical Information (OSTI.GOV)

NG, C.Y., E-mail: ng.cy@cityu.edu.hk

Due to the stringent environmental regulatory requirements being imposed by cross-national bodies in recent years, manufacturers have to minimize the environmental impact of their products. Among those environmental impact evaluation tools available, Life Cycle Assessment (LCA) is often employed to quantify the product's environmental impact throughout its entire life cycle. However, owing to the requirements of expert knowledge in environmental science and vast effort for data collection in carrying out LCA, as well as the common absence of complete product information during product development processes, there is a need to develop a more suitable tool for product designers. An evidentialmore » reasoning-based approach, which aims at providing a fast-track method to perform design alternative evaluations for non-LCA experts, is therefore introduced as a new initiative to deal with the incomplete or uncertain information. The proposed approach also enables decision makers to quantitatively assess the life cycle phases and design alternatives by comparing their potential environmental impacts, thus effectively and efficiently facilitates the identification of greener designs. A case application is carried out to demonstrate the applicability of the proposed approach.« less

Developing expertise in surgery.

PubMed

Alderson, David

2010-01-01

The concept of expertise is widely embraced but poorly defined in surgery. Dictionary definitions differentiate between authority and experience, while a third view sees expertise as a mind-set rather than a status. Both absolute and relative models of expertise have been developed, and each allows a richer understanding of the application of these concepts to emerge. Trainees must develop both independent and interdependent expertise, and an appreciation of the essentially constructivist and uncertain nature of medical knowledge. Approach may be more important than innate talent; the concepts of 'flow', sustained 'deliberate practice' and 'adaptive expertise' are examples of expert approaches to learning. Non-analytical reasoning plays a key role in decision making at expert levels of practice. A technically gifted surgeon may be seen as a safety hazard rather than an expert if inter-dependent expertise has not been developed. Key roles of a surgical educator are to facilitate the development of an expert approach to education and to enable entry into and movement towards the centre of an expert community of practice.

The need for a comprehensive expert system development methodology

NASA Technical Reports Server (NTRS)

Baumert, John; Critchfield, Anna; Leavitt, Karen

1988-01-01

In a traditional software development environment, the introduction of standardized approaches has led to higher quality, maintainable products on the technical side and greater visibility into the status of the effort on the management side. This study examined expert system development to determine whether it differed enough from traditional systems to warrant a reevaluation of current software development methodologies. Its purpose was to identify areas of similarity with traditional software development and areas requiring tailoring to the unique needs of expert systems. A second purpose was to determine whether existing expert system development methodologies meet the needs of expert system development, management, and maintenance personnel. The study consisted of a literature search and personal interviews. It was determined that existing methodologies and approaches to developing expert systems are not comprehensive nor are they easily applied, especially to cradle to grave system development. As a result, requirements were derived for an expert system development methodology and an initial annotated outline derived for such a methodology.

Probabilistic inversion of expert assessments to inform projections about Antarctic ice sheet responses.

PubMed

Fuller, Robert William; Wong, Tony E; Keller, Klaus

2017-01-01

The response of the Antarctic ice sheet (AIS) to changing global temperatures is a key component of sea-level projections. Current projections of the AIS contribution to sea-level changes are deeply uncertain. This deep uncertainty stems, in part, from (i) the inability of current models to fully resolve key processes and scales, (ii) the relatively sparse available data, and (iii) divergent expert assessments. One promising approach to characterizing the deep uncertainty stemming from divergent expert assessments is to combine expert assessments, observations, and simple models by coupling probabilistic inversion and Bayesian inversion. Here, we present a proof-of-concept study that uses probabilistic inversion to fuse a simple AIS model and diverse expert assessments. We demonstrate the ability of probabilistic inversion to infer joint prior probability distributions of model parameters that are consistent with expert assessments. We then confront these inferred expert priors with instrumental and paleoclimatic observational data in a Bayesian inversion. These additional constraints yield tighter hindcasts and projections. We use this approach to quantify how the deep uncertainty surrounding expert assessments affects the joint probability distributions of model parameters and future projections.

a Study on Satellite Diagnostic Expert Systems Using Case-Based Approach

NASA Astrophysics Data System (ADS)

Park, Young-Tack; Kim, Jae-Hoon; Park, Hyun-Soo

1997-06-01

Many research works are on going to monitor and diagnose diverse malfunctions of satellite systems as the complexity and number of satellites increase. Currently, many works on monitoring and diagnosis are carried out by human experts but there are needs to automate much of the routine works of them. Hence, it is necessary to study on using expert systems which can assist human experts routine work by doing automatically, thereby allow human experts devote their expertise more critical and important areas of monitoring and diagnosis. In this paper, we are employing artificial intelligence techniques to model human experts' knowledge and inference the constructed knowledge. Especially, case-based approaches are used to construct a knowledge base to model human expert capabilities which use previous typical exemplars. We have designed and implemented a prototype case-based system for diagnosing satellite malfunctions using cases. Our system remembers typical failure cases and diagnoses a current malfunction by indexing the case base. Diverse methods are used to build a more user friendly interface which allows human experts can build a knowledge base in as easy way.

The BASES expert statement on emotion regulation in sport.

PubMed

Lane, Andrew M; Beedie, Chris J; Jones, Marc V; Uphill, Mark; Devonport, Tracey J

2012-01-01

Emotions experienced before and during sports competition have been found to influence sports performance. Emotion regulation is defined as the automatic or deliberate use of strategies to initiate, maintain, modify or display emotions (Gross & Thompson, 2007) and is proposed to occur when a discrepancy exists between current and desired emotions. Two distinct motivations to regulate emotion - hedonic and instrumental (in short, for pleasure or for purpose) - have been proposed (Tamir, 2009). The instrumental approach might provide a more fruitful area of investigation for sports researchers as some athletes hold beliefs that supposedly pleasant emotions such as happiness and calmness associate with poor performance and supposedly unpleasant emotions such as anxiety and anger associate with good performance (Hanin, 2010). Athletes are more likely to try to regulate an emotion if they believe that doing so will facilitate performance. Strategies that encourage re-appraisal of factors that trigger emotions are proposed to be preferable. In this British Association of Sport and Exercise Sciences (BASES) expert statement, a summary of the key theoretical issues are offered leading to evidence-based recommendations for practitioners and researchers.

[Primary care screening of problems in the elderly and a proposal for a screening protocol with a multidimensional approach].

PubMed

Lino, Valéria Teresa Saraiva; Portela, Margareth Crisóstomo; Camacho, Luiz Antonio Bastos; Rodrigues, Nadia Cristina Pinheiro; Andrade, Monica Kramer de Noronha; O'Dwyer, Gisele

2016-07-21

The objectives were to examine psychometric properties of a screening test for the elderly and to propose a protocol for use in primary care. The method consisted of four stages: (1) inter-evaluator reliability for performance tests and self-assessment questions for eight functions; (2) sensitivity and specificity of questions on depression and social support; (3) meeting of experts to select instrumental activities of daily living (IADL); and (4) elaboration of the protocol. Screening lasted 16 minutes. Inter-evaluator reliability was excellent for performance tests but poor for questions. Depression and social support showed satisfactory sensitivity and specificity (0.74/0.77 and 0.77/0.96). Four IADL were selected by more than 55% of the experts. Following the results, a screening protocol was elaborated that prioritized the use of performance tests, maintaining questions on mood, social support, and IADL. The study suggests better reproducibility of performance tests when compared to questions. For mood and social support, the questions may provide a first screening stage. The proposed protocol allows rapid screening of problems.

Crossword expertise as recognitional decision making: an artificial intelligence approach

PubMed Central

Thanasuan, Kejkaew; Mueller, Shane T.

2014-01-01

The skills required to solve crossword puzzles involve two important aspects of lexical memory: semantic information in the form of clues that indicate the meaning of the answer, and orthographic patterns that constrain the possibilities but may also provide hints to possible answers. Mueller and Thanasuan (2013) proposed a model accounting for the simple memory access processes involved in solving individual crossword clues, but expert solvers also bring additional skills and strategies to bear on solving complete puzzles. In this paper, we developed an computational model of crossword solving that incorporates strategic and other factors, and is capable of solving crossword puzzles in a human-like fashion, in order to understand the complete set of skills needed to solve a crossword puzzle. We compare our models to human expert and novice solvers to investigate how different strategic and structural factors in crossword play impact overall performance. Results reveal that expert crossword solving relies heavily on fluent semantic memory search and retrieval, which appear to allow experts to take better advantage of orthographic-route solutions, and experts employ strategies that enable them to use orthographic information. Furthermore, other processes central to traditional AI models (error correction and backtracking) appear to be of less importance for human players. PMID:25309483

Crossword expertise as recognitional decision making: an artificial intelligence approach.

PubMed

Thanasuan, Kejkaew; Mueller, Shane T

2014-01-01

THE SKILLS REQUIRED TO SOLVE CROSSWORD PUZZLES INVOLVE TWO IMPORTANT ASPECTS OF LEXICAL MEMORY: semantic information in the form of clues that indicate the meaning of the answer, and orthographic patterns that constrain the possibilities but may also provide hints to possible answers. Mueller and Thanasuan (2013) proposed a model accounting for the simple memory access processes involved in solving individual crossword clues, but expert solvers also bring additional skills and strategies to bear on solving complete puzzles. In this paper, we developed an computational model of crossword solving that incorporates strategic and other factors, and is capable of solving crossword puzzles in a human-like fashion, in order to understand the complete set of skills needed to solve a crossword puzzle. We compare our models to human expert and novice solvers to investigate how different strategic and structural factors in crossword play impact overall performance. Results reveal that expert crossword solving relies heavily on fluent semantic memory search and retrieval, which appear to allow experts to take better advantage of orthographic-route solutions, and experts employ strategies that enable them to use orthographic information. Furthermore, other processes central to traditional AI models (error correction and backtracking) appear to be of less importance for human players.

An SSME High Pressure Oxidizer Turbopump diagnostic system using G2 real-time expert system

NASA Technical Reports Server (NTRS)

Guo, Ten-Huei

1991-01-01

An expert system which diagnoses various seal leakage faults in the High Pressure Oxidizer Turbopump of the SSME was developed using G2 real-time expert system. Three major functions of the software were implemented: model-based data generation, real-time expert system reasoning, and real-time input/output communication. This system is proposed as one module of a complete diagnostic system for the SSME. Diagnosis of a fault is defined as the determination of its type, severity, and likelihood. Since fault diagnosis is often accomplished through the use of heuristic human knowledge, an expert system based approach has been adopted as a paradigm to develop this diagnostic system. To implement this approach, a software shell which can be easily programmed to emulate the human decision process, the G2 Real-Time Expert System, was selected. Lessons learned from this implementation are discussed.

An SSME high pressure oxidizer turbopump diagnostic system using G2(TM) real-time expert system

NASA Technical Reports Server (NTRS)

Guo, Ten-Huei

1991-01-01

An expert system which diagnoses various seal leakage faults in the High Pressure Oxidizer Turbopump of the SSME was developed using G2(TM) real-time expert system. Three major functions of the software were implemented: model-based data generation, real-time expert system reasoning, and real-time input/output communication. This system is proposed as one module of a complete diagnostic system for Space Shuttle Main Engine. Diagnosis of a fault is defined as the determination of its type, severity, and likelihood. Since fault diagnosis is often accomplished through the use of heuristic human knowledge, an expert system based approach was adopted as a paradigm to develop this diagnostic system. To implement this approach, a software shell which can be easily programmed to emulate the human decision process, the G2 Real-Time Expert System, was selected. Lessons learned from this implementation are discussed.

Investigating the Role of Domain General Mechanisms in the Acquisition of Domain Specific Expertise

ERIC Educational Resources Information Center

Kaufman, Scott Barry

2007-01-01

The expert performance approach championed by Ericsson et al. provides a scientific way forward for research on giftedness, and offers exciting new ways to further one's understanding of the determinants of high ability within a particular domain of expertise. While the methods the authors use are commendable and are likely to further one's…

Perceived Learning Difficulty and Actual Performance: Explicit and Implicit Knowledge of L2 English Grammar Points among Instructed Adult Learners

ERIC Educational Resources Information Center

Silva, Luis Humberto Rodríguez; Roehr-Brackin, Karen

2016-01-01

This article draws on an approach that conceptualizes L2 learning difficulty in terms of implicit and explicit knowledge. In a study with first language Mexican Spanish university-level learners (n = 30), their teachers (n = 11), and applied linguistics experts (n = 3), we investigated the relationship between (a) these groups' difficulty…

Sweet-spot training for early esophageal cancer detection

NASA Astrophysics Data System (ADS)

van der Sommen, Fons; Zinger, Svitlana; Schoon, Erik J.; de With, Peter H. N.

2016-03-01

Over the past decade, the imaging tools for endoscopists have improved drastically. This has enabled physicians to visually inspect the intestinal tissue for early signs of malignant lesions. Besides this, recent studies show the feasibility of supportive image analysis for endoscopists, but the analysis problem is typically approached as a segmentation task where binary ground truth is employed. In this study, we show that the detection of early cancerous tissue in the gastrointestinal tract cannot be approached as a binary segmentation problem and it is crucial and clinically relevant to involve multiple experts for annotating early lesions. By employing the so-called sweet spot for training purposes as a metric, a much better detection performance can be achieved. Furthermore, a multi-expert-based ground truth, i.e. a golden standard, enables an improved validation of the resulting delineations. For this purpose, besides the sweet spot we also propose another novel metric, the Jaccard Golden Standard (JIGS) that can handle multiple ground-truth annotations. Our experiments involving these new metrics and based on the golden standard show that the performance of a detection algorithm of early neoplastic lesions in Barrett's esophagus can be increased significantly, demonstrating a 10 percent point increase in the resulting F1 detection score.

The effect of observing novice and expert performance on acquisition of surgical skills on a robotic platform

PubMed Central

Harris, David J.; Vine, Samuel J.; Wilson, Mark R.; McGrath, John S.; LeBel, Marie-Eve

2017-01-01

Background Observational learning plays an important role in surgical skills training, following the traditional model of learning from expertise. Recent findings have, however, highlighted the benefit of observing not only expert performance but also error-strewn performance. The aim of this study was to determine which model (novice vs. expert) would lead to the greatest benefits when learning robotically assisted surgical skills. Methods 120 medical students with no prior experience of robotically-assisted surgery completed a ring-carrying training task on three occasions; baseline, post-intervention and at one-week follow-up. The observation intervention consisted of a video model performing the ring-carrying task, with participants randomly assigned to view an expert model, a novice model, a mixed expert/novice model or no observation (control group). Participants were assessed for task performance and surgical instrument control. Results There were significant group differences post-intervention, with expert and novice observation groups outperforming the control group, but there were no clear group differences at a retention test one week later. There was no difference in performance between the expert-observing and error-observing groups. Conclusions Similar benefits were found when observing the traditional expert model or the error-strewn model, suggesting that viewing poor performance may be as beneficial as viewing expertise in the early acquisition of robotic surgical skills. Further work is required to understand, then inform, the optimal curriculum design when utilising observational learning in surgical training. PMID:29141046

The roles of deliberate practice and innate ability in developing expertise: evidence and implications.

PubMed

Kulasegaram, Kulamakan M; Grierson, Lawrence E M; Norman, Geoffrey R

2013-10-01

Medical education research focuses extensively on experience and deliberate practice (DP) as key factors in the development of expert performance. The research on DP minimises the role of individual ability in expert performance. This claim ignores a large body of research supporting the importance of innate individual cognitive differences. We review the relationship between DP and an innate individual ability, working memory (WM) capacity, to illustrate how both DP and individual ability predict expert performance. This narrative review examines the relationship between DP and WM in accounting for expert performance. Studies examining DP, WM and individual differences were identified through a targeted search. Although all studies support extensive DP as a factor in explaining expertise, much research suggests individual cognitive differences, such as WM capacity, predict expert performance after controlling for DP. The extent to which this occurs may be influenced by the nature of the task under study and the cognitive processes used by experts. The importance of WM capacity is greater for tasks that are non-routine or functionally complex. Clinical reasoning displays evidence of this task-dependent importance of individual ability. No single factor is both necessary and sufficient in explaining expertise, and individual abilities such as WM can be important. These individual abilities are likely to contribute to expert performance in clinical settings. Medical education research and practice should identify the individual differences in novices and experts that are important to clinical performance. © 2013 John Wiley & Sons Ltd.

Impact of Expert Teaching Quality on Novice Academic Performance in the Jigsaw Cooperative Learning Method

ERIC Educational Resources Information Center

Berger, Roland; Hänze, Martin

2015-01-01

We assessed the impact of expert students' instructional quality on the academic performance of novice students in 12th-grade physics classes organized in an expert model of cooperative learning ("jigsaw classroom"). The instructional quality of 129 expert students was measured by a newly developed rating system. As expected, when…

Methods to elicit probability distributions from experts: a systematic review of reported practice in health technology assessment.

PubMed

Grigore, Bogdan; Peters, Jaime; Hyde, Christopher; Stein, Ken

2013-11-01

Elicitation is a technique that can be used to obtain probability distribution from experts about unknown quantities. We conducted a methodology review of reports where probability distributions had been elicited from experts to be used in model-based health technology assessments. Databases including MEDLINE, EMBASE and the CRD database were searched from inception to April 2013. Reference lists were checked and citation mapping was also used. Studies describing their approach to the elicitation of probability distributions were included. Data was abstracted on pre-defined aspects of the elicitation technique. Reports were critically appraised on their consideration of the validity, reliability and feasibility of the elicitation exercise. Fourteen articles were included. Across these studies, the most marked features were heterogeneity in elicitation approach and failure to report key aspects of the elicitation method. The most frequently used approaches to elicitation were the histogram technique and the bisection method. Only three papers explicitly considered the validity, reliability and feasibility of the elicitation exercises. Judged by the studies identified in the review, reports of expert elicitation are insufficient in detail and this impacts on the perceived usability of expert-elicited probability distributions. In this context, the wider credibility of elicitation will only be improved by better reporting and greater standardisation of approach. Until then, the advantage of eliciting probability distributions from experts may be lost.

Reference Standards, Judges, and Comparison Subjects

PubMed Central

Hripcsak, George; Wilcox, Adam

2002-01-01

Medical informatics systems are often designed to perform at the level of human experts. Evaluation of the performance of these systems is often constrained by lack of reference standards, either because the appropriate response is not known or because no simple appropriate response exists. Even when performance can be assessed, it is not always clear whether the performance is sufficient or reasonable. These challenges can be addressed if an evaluator enlists the help of clinical domain experts. 1) The experts can carry out the same tasks as the system, and then their responses can be combined to generate a reference standard. 2)The experts can judge the appropriateness of system output directly. 3) The experts can serve as comparison subjects with which the system can be compared. These are separate roles that have different implications for study design, metrics, and issues of reliability and validity. Diagrams help delineate the roles of experts in complex study designs. PMID:11751799

Expert training with standardized operative technique helps establish a successful penile prosthetics program for urologic resident education.

PubMed

King, Ashley B; Klausner, Adam P; Johnson, Corey M; Moore, Blake W; Wilson, Steven K; Grob, B Mayer

2011-10-01

The challenge of resident education in urologic surgery programs is to overcome disparity imparted by diverse patient populations, limited training times, and inequalities in the availability of expert surgical educators. Specifically, in the area of prosthetic urology, only a small proportion of programs have full-time faculty available to train residents in this discipline. To examine whether a new model using yearly training sessions from a recognized expert can establish a successful penile prosthetics program and result in better outcomes, higher case volumes, and willingness to perform more complex surgeries. A recognized expert conducted one to two operative training sessions yearly to teach standardized technique for penile prosthetics to residents. Each session consisted of three to four operative cases performed under the direct supervision of the expert. Retrospective data were collected from all penile prosthetic operations before (February, 2000 to June, 2004: N = 44) and after (July, 2004 to October, 2007: N = 79) implementation of these sessions. Outcomes reviewed included patient age, race, medical comorbidities, operative time, estimated blood loss, type of prosthesis, operative approach, drain usage, length of stay, and complications including revision/explantation rates. Statistical analysis was performed using Student's t-tests, Fisher's tests, and survival curves using the Kaplan-Meier technique (P value ≤ 0.05 to define statistical significance). Patient characteristics were not significantly different pre- vs. post-training. Operative time and estimated blood loss significantly decreased. Inflatable implants increased from 19/44 (43.2%, pre-training) to 69/79 (87.3%, post-training) (P < 0.01). Operations per year increased from 9.96 (pre-training) to 24 (post-training) (P < 0.01). Revision/explantation occurred in 11/44 patients (25%, pre-training) vs. 7/79 (8.9%, post-training) (P < 0.05). These data demonstrate that yearly sessions with a recognized expert can improve surgical outcomes, type, and volume of implants and can reduce explantation/revision rates. This represents an excellent model for improved training of urologic residents in penile prosthetics surgery. © 2011 International Society for Sexual Medicine.

Decision analysis and risk models for land development affecting infrastructure systems.

PubMed

Thekdi, Shital A; Lambert, James H

2012-07-01

Coordination and layering of models to identify risks in complex systems such as large-scale infrastructure of energy, water, and transportation is of current interest across application domains. Such infrastructures are increasingly vulnerable to adjacent commercial and residential land development. Land development can compromise the performance of essential infrastructure systems and increase the costs of maintaining or increasing performance. A risk-informed approach to this topic would be useful to avoid surprise, regret, and the need for costly remedies. This article develops a layering and coordination of models for risk management of land development affecting infrastructure systems. The layers are: system identification, expert elicitation, predictive modeling, comparison of investment alternatives, and implications of current decisions for future options. The modeling layers share a focus on observable factors that most contribute to volatility of land development and land use. The relevant data and expert evidence include current and forecasted growth in population and employment, conservation and preservation rules, land topography and geometries, real estate assessments, market and economic conditions, and other factors. The approach integrates to a decision framework of strategic considerations based on assessing risk, cost, and opportunity in order to prioritize needs and potential remedies that mitigate impacts of land development to the infrastructure systems. The approach is demonstrated for a 5,700-mile multimodal transportation system adjacent to 60,000 tracts of potential land development. © 2011 Society for Risk Analysis.

The Effect of Expert Performance Microtiming on Listeners' Experience of Groove in Swing or Funk Music

PubMed Central

Senn, Olivier; Kilchenmann, Lorenz; von Georgi, Richard; Bullerjahn, Claudia

2016-01-01

This study tested the influence of expert performance microtiming on listeners' experience of groove. Two professional rhythm section performances (bass/drums) in swing and funk style were recorded, and the performances' original microtemporal deviations from a regular metronomic grid were scaled to several levels of magnitude. Music expert (n = 79) and non-expert (n = 81) listeners rated the groove qualities of stimuli using a newly developed questionnaire that measures three dimensions of the groove experience (Entrainment, Enjoyment, and the absence of Irritation). Findings show that music expert listeners were more sensitive to microtiming manipulations than non-experts. Across both expertise groups and for both styles, groove ratings were high for microtiming magnitudes equal or smaller than those originally performed and decreased for exaggerated microtiming magnitudes. In particular, both the fully quantized music and the music with the originally performed microtiming pattern were rated equally high on groove. This means that neither the claims of PD theory (that microtiming deviations are necessary for groove) nor the opposing exactitude hypothesis (that microtiming deviations are detrimental to groove) were supported by the data. PMID:27761117

The Effect of Expert Performance Microtiming on Listeners' Experience of Groove in Swing or Funk Music.

PubMed

Senn, Olivier; Kilchenmann, Lorenz; von Georgi, Richard; Bullerjahn, Claudia

2016-01-01

This study tested the influence of expert performance microtiming on listeners' experience of groove. Two professional rhythm section performances (bass/drums) in swing and funk style were recorded, and the performances' original microtemporal deviations from a regular metronomic grid were scaled to several levels of magnitude. Music expert ( n = 79) and non-expert ( n = 81) listeners rated the groove qualities of stimuli using a newly developed questionnaire that measures three dimensions of the groove experience ( Entrainment, Enjoyment , and the absence of Irritation ). Findings show that music expert listeners were more sensitive to microtiming manipulations than non-experts. Across both expertise groups and for both styles, groove ratings were high for microtiming magnitudes equal or smaller than those originally performed and decreased for exaggerated microtiming magnitudes. In particular, both the fully quantized music and the music with the originally performed microtiming pattern were rated equally high on groove. This means that neither the claims of PD theory (that microtiming deviations are necessary for groove) nor the opposing exactitude hypothesis (that microtiming deviations are detrimental to groove) were supported by the data.

Crowdsourcing Assessment of Surgeon Dissection of Renal Artery and Vein During Robotic Partial Nephrectomy: A Novel Approach for Quantitative Assessment of Surgical Performance.

PubMed

Powers, Mary K; Boonjindasup, Aaron; Pinsky, Michael; Dorsey, Philip; Maddox, Michael; Su, Li-Ming; Gettman, Matthew; Sundaram, Chandru P; Castle, Erik P; Lee, Jason Y; Lee, Benjamin R

2016-04-01

We sought to describe a methodology of crowdsourcing for obtaining quantitative performance ratings of surgeons performing renal artery and vein dissection of robotic partial nephrectomy (RPN). We sought to compare assessment of technical performance obtained from the crowdsourcers with that of surgical content experts (CE). Our hypothesis is that the crowd can score performances of renal hilar dissection comparably to surgical CE using the Global Evaluative Assessment of Robotic Skills (GEARS). A group of resident and attending robotic surgeons submitted a total of 14 video clips of RPN during hilar dissection. These videos were rated by both crowd and CE for technical skills performance using GEARS. A minimum of 3 CE and 30 Amazon Mechanical Turk crowdworkers evaluated each video with the GEARS scale. Within 13 days, we received ratings of all videos from all CE, and within 11.5 hours, we received 548 GEARS ratings from crowdworkers. Even though CE were exposed to a training module, internal consistency across videos of CE GEARS ratings remained low (ICC = 0.38). Despite this, we found that crowdworker GEARS ratings of videos were highly correlated with CE ratings at both the video level (R = 0.82, p < 0.001) and surgeon level (R = 0.84, p < 0.001). Similarly, crowdworker ratings of the renal artery dissection were highly correlated with expert assessments (R = 0.83, p < 0.001) for the unique surgery-specific assessment question. We conclude that crowdsourced assessment of qualitative performance ratings may be an alternative and/or adjunct to surgical experts' ratings and would provide a rapid scalable solution to triage technical skills.

Relationship between automation trust and operator performance for the novice and expert in spacecraft rendezvous and docking (RVD).

PubMed

Niu, Jianwei; Geng, He; Zhang, Yijing; Du, Xiaoping

2018-09-01

Operator trust in automation is a crucial factor influencing its use and operational performance. However, the relationship between automation trust and performance remains poorly understood and requires further investigation. The objective of this paper is to explore the difference in trust and performance on automation-aided spacecraft rendezvous and docking (RVD) between the novice and the expert and to investigate the relationship between automation trust and performance as well. We employed a two-factor mixed design, with training skill (novice and expert) and automation mode (manual RVD and automation aided RVD) serving as the two factors. Twenty participants, 10 novices and 10 experts, were recruited to conduct six RVD tasks for two automation levels. After the tasks, operator performance was recorded by the desktop hand-held docking training equipment. Operator trust was also measured by a 12-items questionnaire at the beginning and end of each trial. As a result, automation narrowed the performance gap significantly between the novice and the expert, and the automation trust showed a marginally significant difference between the novice and the expert. Furthermore, the result demonstrated that the attitude angle control error of the expert was related to the total trust score, whereas other automation performance indicators were not related to the total score of trust. However, automation performance was related to the dimensions of trust, such as entrust, harmful, and dependable. Copyright © 2018 Elsevier Ltd. All rights reserved.

An expert system for integrated structural analysis and design optimization for aerospace structures

NASA Technical Reports Server (NTRS)

1992-01-01

The results of a research study on the development of an expert system for integrated structural analysis and design optimization is presented. An Object Representation Language (ORL) was developed first in conjunction with a rule-based system. This ORL/AI shell was then used to develop expert systems to provide assistance with a variety of structural analysis and design optimization tasks, in conjunction with procedural modules for finite element structural analysis and design optimization. The main goal of the research study was to provide expertise, judgment, and reasoning capabilities in the aerospace structural design process. This will allow engineers performing structural analysis and design, even without extensive experience in the field, to develop error-free, efficient and reliable structural designs very rapidly and cost-effectively. This would not only improve the productivity of design engineers and analysts, but also significantly reduce time to completion of structural design. An extensive literature survey in the field of structural analysis, design optimization, artificial intelligence, and database management systems and their application to the structural design process was first performed. A feasibility study was then performed, and the architecture and the conceptual design for the integrated 'intelligent' structural analysis and design optimization software was then developed. An Object Representation Language (ORL), in conjunction with a rule-based system, was then developed using C++. Such an approach would improve the expressiveness for knowledge representation (especially for structural analysis and design applications), provide ability to build very large and practical expert systems, and provide an efficient way for storing knowledge. Functional specifications for the expert systems were then developed. The ORL/AI shell was then used to develop a variety of modules of expert systems for a variety of modeling, finite element analysis, and design optimization tasks in the integrated aerospace structural design process. These expert systems were developed to work in conjunction with procedural finite element structural analysis and design optimization modules (developed in-house at SAT, Inc.). The complete software, AutoDesign, so developed, can be used for integrated 'intelligent' structural analysis and design optimization. The software was beta-tested at a variety of companies, used by a range of engineers with different levels of background and expertise. Based on the feedback obtained by such users, conclusions were developed and are provided.

An expert system for integrated structural analysis and design optimization for aerospace structures

NASA Astrophysics Data System (ADS)

1992-04-01

The results of a research study on the development of an expert system for integrated structural analysis and design optimization is presented. An Object Representation Language (ORL) was developed first in conjunction with a rule-based system. This ORL/AI shell was then used to develop expert systems to provide assistance with a variety of structural analysis and design optimization tasks, in conjunction with procedural modules for finite element structural analysis and design optimization. The main goal of the research study was to provide expertise, judgment, and reasoning capabilities in the aerospace structural design process. This will allow engineers performing structural analysis and design, even without extensive experience in the field, to develop error-free, efficient and reliable structural designs very rapidly and cost-effectively. This would not only improve the productivity of design engineers and analysts, but also significantly reduce time to completion of structural design. An extensive literature survey in the field of structural analysis, design optimization, artificial intelligence, and database management systems and their application to the structural design process was first performed. A feasibility study was then performed, and the architecture and the conceptual design for the integrated 'intelligent' structural analysis and design optimization software was then developed. An Object Representation Language (ORL), in conjunction with a rule-based system, was then developed using C++. Such an approach would improve the expressiveness for knowledge representation (especially for structural analysis and design applications), provide ability to build very large and practical expert systems, and provide an efficient way for storing knowledge. Functional specifications for the expert systems were then developed. The ORL/AI shell was then used to develop a variety of modules of expert systems for a variety of modeling, finite element analysis, and design optimization tasks in the integrated aerospace structural design process. These expert systems were developed to work in conjunction with procedural finite element structural analysis and design optimization modules (developed in-house at SAT, Inc.). The complete software, AutoDesign, so developed, can be used for integrated 'intelligent' structural analysis and design optimization. The software was beta-tested at a variety of companies, used by a range of engineers with different levels of background and expertise. Based on the feedback obtained by such users, conclusions were developed and are provided.

Supporting community annotation and user collaboration in the integrated microbial genomes (IMG) system

DOE PAGES

Chen, I-Min A.; Markowitz, Victor M.; Palaniappan, Krishna; ...

2016-04-26

Background: The exponential growth of genomic data from next generation technologies renders traditional manual expert curation effort unsustainable. Many genomic systems have included community annotation tools to address the problem. Most of these systems adopted a "Wiki-based" approach to take advantage of existing wiki technologies, but encountered obstacles in issues such as usability, authorship recognition, information reliability and incentive for community participation. Results: Here, we present a different approach, relying on tightly integrated method rather than "Wiki-based" method, to support community annotation and user collaboration in the Integrated Microbial Genomes (IMG) system. The IMG approach allows users to use existingmore » IMG data warehouse and analysis tools to add gene, pathway and biosynthetic cluster annotations, to analyze/reorganize contigs, genes and functions using workspace datasets, and to share private user annotations and workspace datasets with collaborators. We show that the annotation effort using IMG can be part of the research process to overcome the user incentive and authorship recognition problems thus fostering collaboration among domain experts. The usability and reliability issues are addressed by the integration of curated information and analysis tools in IMG, together with DOE Joint Genome Institute (JGI) expert review. Conclusion: By incorporating annotation operations into IMG, we provide an integrated environment for users to perform deeper and extended data analysis and annotation in a single system that can lead to publications and community knowledge sharing as shown in the case studies.« less

Supporting community annotation and user collaboration in the integrated microbial genomes (IMG) system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, I-Min A.; Markowitz, Victor M.; Palaniappan, Krishna

Background: The exponential growth of genomic data from next generation technologies renders traditional manual expert curation effort unsustainable. Many genomic systems have included community annotation tools to address the problem. Most of these systems adopted a "Wiki-based" approach to take advantage of existing wiki technologies, but encountered obstacles in issues such as usability, authorship recognition, information reliability and incentive for community participation. Results: Here, we present a different approach, relying on tightly integrated method rather than "Wiki-based" method, to support community annotation and user collaboration in the Integrated Microbial Genomes (IMG) system. The IMG approach allows users to use existingmore » IMG data warehouse and analysis tools to add gene, pathway and biosynthetic cluster annotations, to analyze/reorganize contigs, genes and functions using workspace datasets, and to share private user annotations and workspace datasets with collaborators. We show that the annotation effort using IMG can be part of the research process to overcome the user incentive and authorship recognition problems thus fostering collaboration among domain experts. The usability and reliability issues are addressed by the integration of curated information and analysis tools in IMG, together with DOE Joint Genome Institute (JGI) expert review. Conclusion: By incorporating annotation operations into IMG, we provide an integrated environment for users to perform deeper and extended data analysis and annotation in a single system that can lead to publications and community knowledge sharing as shown in the case studies.« less

The power of simplicity: a fast-and-frugal heuristics approach to performance science.

PubMed

Raab, Markus; Gigerenzer, Gerd

2015-01-01

Performance science is a fairly new multidisciplinary field that integrates performance domains such as sports, medicine, business, and the arts. To give its many branches a structure and its research a direction, it requires a theoretical framework. We demonstrate the applications of this framework with examples from sport and medicine. Because performance science deals mainly with situations of uncertainty rather than known risks, the needed framework can be provided by the fast-and-frugal heuristics approach. According to this approach, experts learn to rely on heuristics in an adaptive way in order to make accurate decisions. We investigate the adaptive use of heuristics in three ways: the descriptive study of the heuristics in the cognitive "adaptive toolbox;" the prescriptive study of their "ecological rationality," that is, the characterization of the situations in which a given heuristic works; and the engineering study of "intuitive design," that is, the design of transparent aids for making better decisions.

The power of simplicity: a fast-and-frugal heuristics approach to performance science

PubMed Central

Raab, Markus; Gigerenzer, Gerd

2015-01-01

Performance science is a fairly new multidisciplinary field that integrates performance domains such as sports, medicine, business, and the arts. To give its many branches a structure and its research a direction, it requires a theoretical framework. We demonstrate the applications of this framework with examples from sport and medicine. Because performance science deals mainly with situations of uncertainty rather than known risks, the needed framework can be provided by the fast-and-frugal heuristics approach. According to this approach, experts learn to rely on heuristics in an adaptive way in order to make accurate decisions. We investigate the adaptive use of heuristics in three ways: the descriptive study of the heuristics in the cognitive “adaptive toolbox;” the prescriptive study of their “ecological rationality,” that is, the characterization of the situations in which a given heuristic works; and the engineering study of “intuitive design,” that is, the design of transparent aids for making better decisions. PMID:26579051

Service quality benchmarking via a novel approach based on fuzzy ELECTRE III and IPA: an empirical case involving the Italian public healthcare context.

PubMed

La Fata, Concetta Manuela; Lupo, Toni; Piazza, Tommaso

2017-11-21

A novel fuzzy-based approach which combines ELECTRE III along with the Importance-Performance Analysis (IPA) is proposed in the present work to comparatively evaluate the service quality in the public healthcare context. Specifically, ELECTRE III is firstly considered to compare the service performance of examined hospitals in a noncompensatory manner. Afterwards, IPA is employed to support the service quality management to point out improvement needs and their priorities. The proposed approach also incorporates features of the Fuzzy Set Theory so as to address the possible uncertainty, subjectivity and vagueness of involved experts in evaluating the service quality. The model is applied to five major Sicilian public hospitals, and strengths and criticalities of the delivered service are finally highlighted and discussed. Although several approaches combining multi-criteria methods have already been proposed in the literature to evaluate the service performance in the healthcare field, to the best of the authors' knowledge the present work represents the first attempt at comparing service performance of alternatives in a noncompensatory manner in the investigated context.

Accuracy of patient-specific organ dose estimates obtained using an automated image segmentation algorithm.

PubMed

Schmidt, Taly Gilat; Wang, Adam S; Coradi, Thomas; Haas, Benjamin; Star-Lack, Josh

2016-10-01

The overall goal of this work is to develop a rapid, accurate, and automated software tool to estimate patient-specific organ doses from computed tomography (CT) scans using simulations to generate dose maps combined with automated segmentation algorithms. This work quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm. We hypothesized that the autosegmentation algorithm is sufficiently accurate to provide organ dose estimates, since small errors delineating organ boundaries will have minimal effect when computing mean organ dose. A leave-one-out validation study of the automated algorithm was performed with 20 head-neck CT scans expertly segmented into nine regions. Mean organ doses of the automatically and expertly segmented regions were computed from Monte Carlo-generated dose maps and compared. The automated segmentation algorithm estimated the mean organ dose to be within 10% of the expert segmentation for regions other than the spinal canal, with the median error for each organ region below 2%. In the spinal canal region, the median error was [Formula: see text], with a maximum absolute error of 28% for the single-atlas approach and 11% for the multiatlas approach. The results demonstrate that the automated segmentation algorithm can provide accurate organ dose estimates despite some segmentation errors.

Accuracy of patient-specific organ dose estimates obtained using an automated image segmentation algorithm

PubMed Central

Schmidt, Taly Gilat; Wang, Adam S.; Coradi, Thomas; Haas, Benjamin; Star-Lack, Josh

2016-01-01

Abstract. The overall goal of this work is to develop a rapid, accurate, and automated software tool to estimate patient-specific organ doses from computed tomography (CT) scans using simulations to generate dose maps combined with automated segmentation algorithms. This work quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm. We hypothesized that the autosegmentation algorithm is sufficiently accurate to provide organ dose estimates, since small errors delineating organ boundaries will have minimal effect when computing mean organ dose. A leave-one-out validation study of the automated algorithm was performed with 20 head-neck CT scans expertly segmented into nine regions. Mean organ doses of the automatically and expertly segmented regions were computed from Monte Carlo-generated dose maps and compared. The automated segmentation algorithm estimated the mean organ dose to be within 10% of the expert segmentation for regions other than the spinal canal, with the median error for each organ region below 2%. In the spinal canal region, the median error was −7%, with a maximum absolute error of 28% for the single-atlas approach and 11% for the multiatlas approach. The results demonstrate that the automated segmentation algorithm can provide accurate organ dose estimates despite some segmentation errors. PMID:27921070

Identifying Key Performance Indicators for Holistic Hospital Management with a Modified DEMATEL Approach

PubMed Central

Si, Sheng-Li; You, Xiao-Yue; Huang, Jia

2017-01-01

Performance analysis is an important way for hospitals to achieve higher efficiency and effectiveness in providing services to their customers. The performance of the healthcare system can be measured by many indicators, but it is difficult to improve them simultaneously due to the limited resources. A feasible way is to identify the central and influential indicators to improve healthcare performance in a stepwise manner. In this paper, we propose a hybrid multiple criteria decision making (MCDM) approach to identify key performance indicators (KPIs) for holistic hospital management. First, through integrating evidential reasoning approach and interval 2-tuple linguistic variables, various assessments of performance indicators provided by healthcare experts are modeled. Then, the decision making trial and evaluation laboratory (DEMATEL) technique is adopted to build an interactive network and visualize the causal relationships between the performance indicators. Finally, an empirical case study is provided to demonstrate the proposed approach for improving the efficiency of healthcare management. The results show that “accidents/adverse events”, “nosocomial infection”, ‘‘incidents/errors”, “number of operations/procedures” are significant influential indicators. Also, the indicators of “length of stay”, “bed occupancy” and “financial measures” play important roles in performance evaluation of the healthcare organization. The proposed decision making approach could be considered as a reference for healthcare administrators to enhance the performance of their healthcare institutions. PMID:28825613

Supervised Learning for Detection of Duplicates in Genomic Sequence Databases.

PubMed

Chen, Qingyu; Zobel, Justin; Zhang, Xiuzhen; Verspoor, Karin

2016-01-01

First identified as an issue in 1996, duplication in biological databases introduces redundancy and even leads to inconsistency when contradictory information appears. The amount of data makes purely manual de-duplication impractical, and existing automatic systems cannot detect duplicates as precisely as can experts. Supervised learning has the potential to address such problems by building automatic systems that learn from expert curation to detect duplicates precisely and efficiently. While machine learning is a mature approach in other duplicate detection contexts, it has seen only preliminary application in genomic sequence databases. We developed and evaluated a supervised duplicate detection method based on an expert curated dataset of duplicates, containing over one million pairs across five organisms derived from genomic sequence databases. We selected 22 features to represent distinct attributes of the database records, and developed a binary model and a multi-class model. Both models achieve promising performance; under cross-validation, the binary model had over 90% accuracy in each of the five organisms, while the multi-class model maintains high accuracy and is more robust in generalisation. We performed an ablation study to quantify the impact of different sequence record features, finding that features derived from meta-data, sequence identity, and alignment quality impact performance most strongly. The study demonstrates machine learning can be an effective additional tool for de-duplication of genomic sequence databases. All Data are available as described in the supplementary material.

Quantitative three-dimensional transrectal ultrasound (TRUS) for prostate imaging

NASA Astrophysics Data System (ADS)

Pathak, Sayan D.; Aarnink, Rene G.; de la Rosette, Jean J.; Chalana, Vikram; Wijkstra, Hessel; Haynor, David R.; Debruyne, Frans M. J.; Kim, Yongmin

1998-06-01

With the number of men seeking medical care for prostate diseases rising steadily, the need of a fast and accurate prostate boundary detection and volume estimation tool is being increasingly experienced by the clinicians. Currently, these measurements are made manually, which results in a large examination time. A possible solution is to improve the efficiency by automating the boundary detection and volume estimation process with minimal involvement from the human experts. In this paper, we present an algorithm based on SNAKES to detect the boundaries. Our approach is to selectively enhance the contrast along the edges using an algorithm called sticks and integrate it with a SNAKES model. This integrated algorithm requires an initial curve for each ultrasound image to initiate the boundary detection process. We have used different schemes to generate the curves with a varying degree of automation and evaluated its effects on the algorithm performance. After the boundaries are identified, the prostate volume is calculated using planimetric volumetry. We have tested our algorithm on 6 different prostate volumes and compared the performance against the volumes manually measured by 3 experts. With the increase in the user inputs, the algorithm performance improved as expected. The results demonstrate that given an initial contour reasonably close to the prostate boundaries, the algorithm successfully delineates the prostate boundaries in an image, and the resulting volume measurements are in close agreement with those made by the human experts.

Comparison of precision and speed in laparoscopic and robot-assisted surgical task performance.

PubMed

Zihni, Ahmed; Gerull, William D; Cavallo, Jaime A; Ge, Tianjia; Ray, Shuddhadeb; Chiu, Jason; Brunt, L Michael; Awad, Michael M

2018-03-01

Robotic platforms have the potential advantage of providing additional dexterity and precision to surgeons while performing complex laparoscopic tasks, especially for those in training. Few quantitative evaluations of surgical task performance comparing laparoscopic and robotic platforms among surgeons of varying experience levels have been done. We compared measures of quality and efficiency of Fundamentals of Laparoscopic Surgery task performance on these platforms in novices and experienced laparoscopic and robotic surgeons. Fourteen novices, 12 expert laparoscopic surgeons (>100 laparoscopic procedures performed, no robotics experience), and five expert robotic surgeons (>25 robotic procedures performed) performed three Fundamentals of Laparoscopic Surgery tasks on both laparoscopic and robotic platforms: peg transfer (PT), pattern cutting (PC), and intracorporeal suturing. All tasks were repeated three times by each subject on each platform in a randomized order. Mean completion times and mean errors per trial (EPT) were calculated for each task on both platforms. Results were compared using Student's t-test (P < 0.05 considered statistically significant). Among novices, greater errors were noted during laparoscopic PC (Lap 2.21 versus Robot 0.88 EPT, P < 0.001). Among expert laparoscopists, greater errors were noted during laparoscopic PT compared with robotic (PT: Lap 0.14 versus Robot 0.00 EPT, P = 0.04). Among expert robotic surgeons, greater errors were noted during laparoscopic PC compared with robotic (Lap 0.80 versus Robot 0.13 EPT, P = 0.02). Among expert laparoscopists, task performance was slower on the robotic platform compared with laparoscopy. In comparisons of expert laparoscopists performing tasks on the laparoscopic platform and expert robotic surgeons performing tasks on the robotic platform, expert robotic surgeons demonstrated fewer errors during the PC task (P = 0.009). Robotic assistance provided a reduction in errors at all experience levels for some laparoscopic tasks, but no benefit in the speed of task performance. Robotic assistance may provide some benefit in precision of surgical task performance. Copyright © 2017 Elsevier Inc. All rights reserved.

A Deliberate Practice Approach to Teaching Phylogenetic Analysis

ERIC Educational Resources Information Center

Hobbs, F. Collin; Johnson, Daniel J.; Kearns, Katherine D.

2013-01-01

One goal of postsecondary education is to assist students in developing expert-level understanding. Previous attempts to encourage expert-level understanding of phylogenetic analysis in college science classrooms have largely focused on isolated, or "one-shot," in-class activities. Using a deliberate practice instructional approach, we…

Objective assessment in residency-based training for transoral robotic surgery.

PubMed

Curry, Martin; Malpani, Anand; Li, Ryan; Tantillo, Thomas; Jog, Amod; Blanco, Ray; Ha, Patrick K; Califano, Joseph; Kumar, Rajesh; Richmon, Jeremy

2012-10-01

To develop a robotic surgery training regimen integrating objective skill assessment for otolaryngology and head and neck surgery trainees consisting of training modules of increasing complexity leading up to procedure-specific training. In particular, we investigated applications of such a training approach for surgical extirpation of oropharyngeal tumors via a transoral approach using the da Vinci robotic system. Prospective blinded data collection and objective evaluation (Objective Structured Assessment of Technical Skills [OSATS]) of three distinct phases using the da Vinci robotic surgical system in an academic university medical engineering/computer science laboratory setting. Between September 2010 and July 2011, eight otolaryngology-head and neck surgery residents and four staff experts from an academic hospital participated in three distinct phases of robotic surgery training involving 1) robotic platform operational skills, 2) set up of the patient side system, and 3) a complete ex vivo surgical extirpation of an oropharyngeal tumor located in the base of tongue. Trainees performed multiple (four) approximately equally spaced training sessions in each stage of the training. In addition to trainees, baseline performance data were obtained for the experts. Each surgical stage was documented with motion and event data captured from the application programming interfaces of the da Vinci system, as well as separate video cameras as appropriate. All data were assessed using automated skill measures of task efficiency and correlated with structured assessment (OSATS and similar Likert scale) from three experts to assess expert and trainee differences and compute automated and expert assessed learning curves. Our data show that such training results in an improved didactic robotic knowledge base and improved clinical efficiency with respect to the set up and console manipulation. Experts (e.g., average OSATS, 25; standard deviation [SD], 3.1; module 1, suturing) and trainees (average OSATS, 15.9; SD, 3.9; week 1) are well separated at the beginning of the training, and the separation reduces significantly (expert average OSATS, 27.6; SD, 2.7; trainee average OSATS, 24.2; SD, 6.8; module 3) at the conclusion of the training. Learning curves in each of the three stages show diminishing differences between the experts and trainees, which is also consistent with expert assessment. Subjective assessment by experts verified the clinical utility of the module 3 surgical environment, and a survey of trainees consistently rated the curriculum as very useful in progression to human operating room assistance. Structured curricular robotic surgery training with objective assessment promises to reduce the overhead for mentors, allow detailed assessment of human-machine interface skills, and create customized training models for individualized training. This preliminary study verifies the utility of such training in improving human-machine operations skills (module 1), and operating room and surgical skills (modules 2 and 3). In contrast to current coarse measures of total operating time and subjective assessment of error for short mass training sessions, these methods may allow individual tasks to be removed from the trainee regimen when skill levels are within the standard deviation of the experts for these tasks, which can greatly enhance overall efficiency of the training regimen and allow time for additional and more complex training to be incorporated in the same time frame. Copyright © 2012 The American Laryngological, Rhinological, and Otological Society, Inc.

Automated eddy current analysis of materials

NASA Technical Reports Server (NTRS)

Workman, Gary L.

1990-01-01

This research effort focused on the use of eddy current techniques for characterizing flaws in graphite-based filament-wound cylindrical structures. A major emphasis was on incorporating artificial intelligence techniques into the signal analysis portion of the inspection process. Developing an eddy current scanning system using a commercial robot for inspecting graphite structures (and others) has been a goal in the overall concept and is essential for the final implementation for expert system interpretation. Manual scans, as performed in the preliminary work here, do not provide sufficiently reproducible eddy current signatures to be easily built into a real time expert system. The expert systems approach to eddy current signal analysis requires that a suitable knowledge base exist in which correct decisions as to the nature of the flaw can be performed. In eddy current or any other expert systems used to analyze signals in real time in a production environment, it is important to simplify computational procedures as much as possible. For that reason, we have chosen to use the measured resistance and reactance values for the preliminary aspects of this work. A simple computation, such as phase angle of the signal, is certainly within the real time processing capability of the computer system. In the work described here, there is a balance between physical measurements and finite element calculations of those measurements. The goal is to evolve into the most cost effective procedures for maintaining the correctness of the knowledge base.

Implementing clinical governance in Isfahan hospitals: Barriers and solutions, 2014.

PubMed

Ferdosi, Masoud; Ziyari, Farhad Bahman; Ollahi, Mehran Nemat; Salmani, Amaneh Rahim; Niknam, Noureddin

2016-01-01

In the new approach, all health care providers have been obligated to maintain and improve the quality and have been accountable for it. One of the ways is the implementation of clinical governance (CG). More accurate understanding of its challenges can help to improve its performance. In this study, barriers of CG implementation are investigated from the perspective of the hospitals involved. Besides, some solutions are suggested based on stakeholders' opinions. This study used combined method (qualitative content analysis and questionnaire) in hospitals affiliated to Isfahan University of Medical Sciences in 2014. First, experts, and stakeholders talked about CG implementation obstacles in a semi-structured interview. Interviews were confirmed by the interviewee (double check). After analyzing the interviews using reduction coding the questionnaire was drawn up. The questionnaire "validity was confirmed by Cronbach's alpha (0/891)" and its reliability was obtained using experts confirmation. Data analyzing was performed using SPSS (18) software. According to results staffing and management factors were the main obstacles. After them, were factors related to organizational culture, infrastructure elements, information, sociocultural and then process factors. The learning barriers were in final rank. Thirty-four solutions was proposed by experts and divided into subset of eight major barriers. Most solutions were offered on modifying processes and minimal solutions about modifying of organizational culture, sociocultural, and educational factors. Removing the obstacles, especially management and human resource factors can be effective by facilitating and accelerating CG. Furthermore, use of experts and stakeholders opinions can help to remove CG barriers.

Detection of discontinuous patterns in spontaneous brain activity of neonates and fetuses.

PubMed

Vairavan, Srinivasan; Eswaran, Hari; Haddad, Naim; Rose, Douglas F; Preissl, Hubert; Wilson, James D; Lowery, Curtis L; Govindan, Rathinaswamy B

2009-11-01

The discontinuous patterns in neonatal magnetoencephalographic (MEG) data are quantified with a novel Hilbert phase (HP) based approach. The expert neurologists' scores were used as the gold standard. The performance of this approach was analyzed using a receiver operating characteristic (ROC) curve, and it was compared with two other approaches, namely spectral ratio (SR) and discrete wavelet transform (DWT) that have been proposed for the detection of discontinuous patterns in neonatal EEG. The area under the ROC curve (AUC) was used as a performance measure. AUCs obtained for SR, HP, and DWT were 0.87, 0.80, and 0.56, respectively. Although the performance of HP was lower than SR, it carries information about the frequency content of the signal that helps to distinguish brain patterns from artifacts such as cardiac residuals. Based on this property, the HP approach was extended to fetal MEG data. Further, using the frequency property of the HP approach, burst duration and interburst interval were computed for the discontinuous patterns detected and they are in agreement with reported values.

Impact of Expert Teaching Quality on Novice Academic Performance in the Jigsaw Cooperative Learning Method

NASA Astrophysics Data System (ADS)

Berger, Roland; Hänze, Martin

2015-01-01

We assessed the impact of expert students' instructional quality on the academic performance of novice students in 12th-grade physics classes organized in an expert model of cooperative learning ('jigsaw classroom'). The instructional quality of 129 expert students was measured by a newly developed rating system. As expected, when aggregating across all four subtopics taught, regression analysis revealed that academic performance of novice students increases with the quality of expert students' instruction. The difficulty of subtopics, however, moderates this effect: higher instructional quality of more difficult subtopics did not lead to better academic performance of novice students. We interpret this finding in the light of Cognitive Load Theory. Demanding tasks cause high intrinsic cognitive load and hindered the novice students' learning.

Bayesian network models for error detection in radiotherapy plans

NASA Astrophysics Data System (ADS)

Kalet, Alan M.; Gennari, John H.; Ford, Eric C.; Phillips, Mark H.

2015-04-01

The purpose of this study is to design and develop a probabilistic network for detecting errors in radiotherapy plans for use at the time of initial plan verification. Our group has initiated a multi-pronged approach to reduce these errors. We report on our development of Bayesian models of radiotherapy plans. Bayesian networks consist of joint probability distributions that define the probability of one event, given some set of other known information. Using the networks, we find the probability of obtaining certain radiotherapy parameters, given a set of initial clinical information. A low probability in a propagated network then corresponds to potential errors to be flagged for investigation. To build our networks we first interviewed medical physicists and other domain experts to identify the relevant radiotherapy concepts and their associated interdependencies and to construct a network topology. Next, to populate the network’s conditional probability tables, we used the Hugin Expert software to learn parameter distributions from a subset of de-identified data derived from a radiation oncology based clinical information database system. These data represent 4990 unique prescription cases over a 5 year period. Under test case scenarios with approximately 1.5% introduced error rates, network performance produced areas under the ROC curve of 0.88, 0.98, and 0.89 for the lung, brain and female breast cancer error detection networks, respectively. Comparison of the brain network to human experts performance (AUC of 0.90 ± 0.01) shows the Bayes network model performs better than domain experts under the same test conditions. Our results demonstrate the feasibility and effectiveness of comprehensive probabilistic models as part of decision support systems for improved detection of errors in initial radiotherapy plan verification procedures.

The validation index: a new metric for validation of segmentation algorithms using two or more expert outlines with application to radiotherapy planning.

PubMed

Juneja, Prabhjot; Evans, Philp M; Harris, Emma J

2013-08-01

Validation is required to ensure automated segmentation algorithms are suitable for radiotherapy target definition. In the absence of true segmentation, algorithmic segmentation is validated against expert outlining of the region of interest. Multiple experts are used to overcome inter-expert variability. Several approaches have been studied in the literature, but the most appropriate approach to combine the information from multiple expert outlines, to give a single metric for validation, is unclear. None consider a metric that can be tailored to case-specific requirements in radiotherapy planning. Validation index (VI), a new validation metric which uses experts' level of agreement was developed. A control parameter was introduced for the validation of segmentations required for different radiotherapy scenarios: for targets close to organs-at-risk and for difficult to discern targets, where large variation between experts is expected. VI was evaluated using two simulated idealized cases and data from two clinical studies. VI was compared with the commonly used Dice similarity coefficient (DSCpair - wise) and found to be more sensitive than the DSCpair - wise to the changes in agreement between experts. VI was shown to be adaptable to specific radiotherapy planning scenarios.

Probabilistic inversion of expert assessments to inform projections about Antarctic ice sheet responses

PubMed Central

Wong, Tony E.; Keller, Klaus

2017-01-01

The response of the Antarctic ice sheet (AIS) to changing global temperatures is a key component of sea-level projections. Current projections of the AIS contribution to sea-level changes are deeply uncertain. This deep uncertainty stems, in part, from (i) the inability of current models to fully resolve key processes and scales, (ii) the relatively sparse available data, and (iii) divergent expert assessments. One promising approach to characterizing the deep uncertainty stemming from divergent expert assessments is to combine expert assessments, observations, and simple models by coupling probabilistic inversion and Bayesian inversion. Here, we present a proof-of-concept study that uses probabilistic inversion to fuse a simple AIS model and diverse expert assessments. We demonstrate the ability of probabilistic inversion to infer joint prior probability distributions of model parameters that are consistent with expert assessments. We then confront these inferred expert priors with instrumental and paleoclimatic observational data in a Bayesian inversion. These additional constraints yield tighter hindcasts and projections. We use this approach to quantify how the deep uncertainty surrounding expert assessments affects the joint probability distributions of model parameters and future projections. PMID:29287095

ART/Ada and CLIPS/Ada

NASA Technical Reports Server (NTRS)

Culbert, Chris

1990-01-01

Although they have reached a point of commercial viability, expert systems were originally developed in artificial intelligence (AI) research environments. Many of the available tools still work best in such environments. These environments typically utilize special hardware such as LISP machines and relatively unfamiliar languages such as LISP or Prolog. Space Station applications will require deep integration of expert system technology with applications developed in conventional languages, specifically Ada. The ability to apply automation to Space Station functions could be greatly enhanced by widespread availability of state-of-the-art expert system tools based on Ada. Although there have been some efforts to examine the use of Ada for AI applications, there are few, if any, existing products which provide state-of-the-art AI capabilities in an Ada tool. The goal of the ART/Ada Design Project is to conduct research into the implementation in Ada of state-of-the-art hybrid expert systems building tools (ESBT's). This project takes the following approach: using the existing design of the ART-IM ESBT as a starting point, analyze the impact of the Ada language and Ada development methodologies on that design; redesign the system in Ada; and analyze its performance. The research project will attempt to achieve a comprehensive understanding of the potential for embedding expert systems in Ada systems for eventual application in future Space Station Freedom projects. During Phase 1 of the project, initial requirements analysis, design, and implementation of the kernel subset of ART-IM functionality was completed. During Phase 2, the effort has been focused on the implementation and performance analysis of several versions with increasing functionality. Since production quality ART/Ada tools will not be available for a considerable time, and additional subtask of this project will be the completion of an Ada version of the CLIPS expert system shell developed by NASA. This tool will provide full syntactic compatibility with any eventual products of the ART/Ada design while allowing SSFP developers early access to this technology.

Visualisation Enhancement of HoloCatT Matrix

NASA Astrophysics Data System (ADS)

Rosli, Nor Azlin; Mohamed, Azlinah; Khan, Rahmattullah

Graphology and personality psychology are two different analyses approach perform by two different groups of people, but addresses the personality of the person that were analyzed. It is of interest to visualize a system that would aid personality identification given information visualization of these two domains. Therefore, a research in identifying the relationship between those two domains has been carried out by producing the HoloCatT Matrix, a combination of graphology features and a selected personality traits approach. The objectives of this research are to identify new features of the existing HoloCatT Matrix and validate the new version of matrix with two (2) related group of experts. A set of questionnaire has been distributed to a group of Personologist to identify the relationship and an interview has been done with a Graphologist in validating the matrix. Based on the analysis, 87.5% of the relation confirmed by both group of experts and subsequently the third (3rd) version of HoloCatT Matrix is obtained.

Online Dectection and Modeling of Safety Boundaries for Aerospace Application Using Bayesian Statistics

NASA Technical Reports Server (NTRS)

He, Yuning

2015-01-01

The behavior of complex aerospace systems is governed by numerous parameters. For safety analysis it is important to understand how the system behaves with respect to these parameter values. In particular, understanding the boundaries between safe and unsafe regions is of major importance. In this paper, we describe a hierarchical Bayesian statistical modeling approach for the online detection and characterization of such boundaries. Our method for classification with active learning uses a particle filter-based model and a boundary-aware metric for best performance. From a library of candidate shapes incorporated with domain expert knowledge, the location and parameters of the boundaries are estimated using advanced Bayesian modeling techniques. The results of our boundary analysis are then provided in a form understandable by the domain expert. We illustrate our approach using a simulation model of a NASA neuro-adaptive flight control system, as well as a system for the detection of separation violations in the terminal airspace.

A Distributed Artificial Intelligence Approach To Object Identification And Classification

NASA Astrophysics Data System (ADS)

Sikka, Digvijay I.; Varshney, Pramod K.; Vannicola, Vincent C.

1989-09-01

This paper presents an application of Distributed Artificial Intelligence (DAI) tools to the data fusion and classification problem. Our approach is to use a blackboard for information management and hypothe-ses formulation. The blackboard is used by the knowledge sources (KSs) for sharing information and posting their hypotheses on, just as experts sitting around a round table would do. The present simulation performs classification of an Aircraft(AC), after identifying it by its features, into disjoint sets (object classes) comprising of the five commercial ACs; Boeing 747, Boeing 707, DC10, Concord and Boeing 727. A situation data base is characterized by experimental data available from the three levels of expert reasoning. Ohio State University ElectroScience Laboratory provided this experimental data. To validate the architecture presented, we employ two KSs for modeling the sensors, aspect angle polarization feature and the ellipticity data. The system has been implemented on Symbolics 3645, under Genera 7.1, in Common LISP.

Comparison of Educational Inspectors' Role of Program Leadership in the Foundation Period of Republic of Turkey with Today

ERIC Educational Resources Information Center

Usta, Mehmet Emin

2018-01-01

From the very early periods of accepting management as a science, main goal of inspection is seen as a control mechanism. Classical management approach evaluated staff as people who need strict inspections by experts to better perform since they are perceived as unreliable and irresponsible on their duties. Later on, expertise areas related to…

An Integrated Approach to the Teaching and Learning of Science and Mathematics Utilising Technology--The Teachers' Perspective

ERIC Educational Resources Information Center

Johnston, Jennifer; Riordain, Maire Ni; Walshe, Grainne

2014-01-01

The concept and importance of curriculum integration in Science and Mathematics has come to the fore in the recent years (Czerniak, 2007). Ireland's Science and Mathematics performance is well documented and extensively reported in the media and elsewhere (e.g. Expert Group on Future Skills Needs, 2008; Task Force on the Physical Sciences, 2002).…

Smart Sensor-Based Motion Detection System for Hand Movement Training in Open Surgery.

PubMed

Sun, Xinyao; Byrns, Simon; Cheng, Irene; Zheng, Bin; Basu, Anup

2017-02-01

We introduce a smart sensor-based motion detection technique for objective measurement and assessment of surgical dexterity among users at different experience levels. The goal is to allow trainees to evaluate their performance based on a reference model shared through communication technology, e.g., the Internet, without the physical presence of an evaluating surgeon. While in the current implementation we used a Leap Motion Controller to obtain motion data for analysis, our technique can be applied to motion data captured by other smart sensors, e.g., OptiTrack. To differentiate motions captured from different participants, measurement and assessment in our approach are achieved using two strategies: (1) low level descriptive statistical analysis, and (2) Hidden Markov Model (HMM) classification. Based on our surgical knot tying task experiment, we can conclude that finger motions generated from users with different surgical dexterity, e.g., expert and novice performers, display differences in path length, number of movements and task completion time. In order to validate the discriminatory ability of HMM for classifying different movement patterns, a non-surgical task was included in our analysis. Experimental results demonstrate that our approach had 100 % accuracy in discriminating between expert and novice performances. Our proposed motion analysis technique applied to open surgical procedures is a promising step towards the development of objective computer-assisted assessment and training systems.

Multisensor data fusion for IED threat detection

NASA Astrophysics Data System (ADS)

Mees, Wim; Heremans, Roel

2012-10-01

In this paper we present the multi-sensor registration and fusion algorithms that were developed for a force protection research project in order to detect threats against military patrol vehicles. The fusion is performed at object level, using a hierarchical evidence aggregation approach. It first uses expert domain knowledge about the features used to characterize the detected threats, that is implemented in the form of a fuzzy expert system. The next level consists in fusing intra-sensor and inter-sensor information. Here an ordered weighted averaging operator is used. The object level fusion between candidate threats that are detected asynchronously on a moving vehicle by sensors with different imaging geometries, requires an accurate sensor to world coordinate transformation. This image registration will also be discussed in this paper.

Early esophageal cancer detection using RF classifiers

NASA Astrophysics Data System (ADS)

Janse, Markus H. A.; van der Sommen, Fons; Zinger, Svitlana; Schoon, Erik J.; de With, Peter H. N.

2016-03-01

Esophageal cancer is one of the fastest rising forms of cancer in the Western world. Using High-Definition (HD) endoscopy, gastroenterology experts can identify esophageal cancer at an early stage. Recent research shows that early cancer can be found using a state-of-the-art computer-aided detection (CADe) system based on analyzing static HD endoscopic images. Our research aims at extending this system by applying Random Forest (RF) classification, which introduces a confidence measure for detected cancer regions. To visualize this data, we propose a novel automated annotation system, employing the unique characteristics of the previous confidence measure. This approach allows reliable modeling of multi-expert knowledge and provides essential data for real-time video processing, to enable future use of the system in a clinical setting. The performance of the CADe system is evaluated on a 39-patient dataset, containing 100 images annotated by 5 expert gastroenterologists. The proposed system reaches a precision of 75% and recall of 90%, thereby improving the state-of-the-art results by 11 and 6 percentage points, respectively.

A Thermal Expert System (TEXSYS) development overview - AI-based control of a Space Station prototype thermal bus

NASA Technical Reports Server (NTRS)

Glass, B. J.; Hack, E. C.

1990-01-01

A knowledge-based control system for real-time control and fault detection, isolation and recovery (FDIR) of a prototype two-phase Space Station Freedom external thermal control system (TCS) is discussed in this paper. The Thermal Expert System (TEXSYS) has been demonstrated in recent tests to be capable of both fault anticipation and detection and real-time control of the thermal bus. Performance requirements were achieved by using a symbolic control approach, layering model-based expert system software on a conventional numerical data acquisition and control system. The model-based capabilities of TEXSYS were shown to be advantageous during software development and testing. One representative example is given from on-line TCS tests of TEXSYS. The integration and testing of TEXSYS with a live TCS testbed provides some insight on the use of formal software design, development and documentation methodologies to qualify knowledge-based systems for on-line or flight applications.

Automated robot-assisted surgical skill evaluation: Predictive analytics approach.

PubMed

Fard, Mahtab J; Ameri, Sattar; Darin Ellis, R; Chinnam, Ratna B; Pandya, Abhilash K; Klein, Michael D

2018-02-01

Surgical skill assessment has predominantly been a subjective task. Recently, technological advances such as robot-assisted surgery have created great opportunities for objective surgical evaluation. In this paper, we introduce a predictive framework for objective skill assessment based on movement trajectory data. Our aim is to build a classification framework to automatically evaluate the performance of surgeons with different levels of expertise. Eight global movement features are extracted from movement trajectory data captured by a da Vinci robot for surgeons with two levels of expertise - novice and expert. Three classification methods - k-nearest neighbours, logistic regression and support vector machines - are applied. The result shows that the proposed framework can classify surgeons' expertise as novice or expert with an accuracy of 82.3% for knot tying and 89.9% for a suturing task. This study demonstrates and evaluates the ability of machine learning methods to automatically classify expert and novice surgeons using global movement features. Copyright © 2017 John Wiley & Sons, Ltd.

Planning and Resource Management in an Intelligent Automated Power Management System

NASA Technical Reports Server (NTRS)

Morris, Robert A.

1991-01-01

Power system management is a process of guiding a power system towards the objective of continuous supply of electrical power to a set of loads. Spacecraft power system management requires planning and scheduling, since electrical power is a scarce resource in space. The automation of power system management for future spacecraft has been recognized as an important R&D goal. Several automation technologies have emerged including the use of expert systems for automating human problem solving capabilities such as rule based expert system for fault diagnosis and load scheduling. It is questionable whether current generation expert system technology is applicable for power system management in space. The objective of the ADEPTS (ADvanced Electrical Power management Techniques for Space systems) is to study new techniques for power management automation. These techniques involve integrating current expert system technology with that of parallel and distributed computing, as well as a distributed, object-oriented approach to software design. The focus of the current study is the integration of new procedures for automatically planning and scheduling loads with procedures for performing fault diagnosis and control. The objective is the concurrent execution of both sets of tasks on separate transputer processors, thus adding parallelism to the overall management process.

Pilot age and expertise predict flight simulator performance: a 3-year longitudinal study.

PubMed

Taylor, Joy L; Kennedy, Quinn; Noda, Art; Yesavage, Jerome A

2007-02-27

Expert knowledge may compensate for age-related declines in basic cognitive and sensory-motor abilities in some skill domains. We investigated the influence of age and aviation expertise (indexed by Federal Aviation Administration pilot ratings) on longitudinal flight simulator performance. Over a 3-year period, 118 general aviation pilots aged 40 to 69 years were tested annually, in which their flight performance was scored in terms of 1) executing air-traffic controller communications; 2) traffic avoidance; 3) scanning cockpit instruments; 4) executing an approach to landing; and 5) a flight summary score. More expert pilots had better flight summary scores at baseline and showed less decline over time. Secondary analyses revealed that expertise effects were most evident in the accuracy of executing aviation communications, the measure on which performance declined most sharply over time. Regarding age, even though older pilots initially performed worse than younger pilots, over time older pilots showed less decline in flight summary scores than younger pilots. Secondary analyses revealed that the oldest pilots did well over time because their traffic avoidance performance improved more vs younger pilots. These longitudinal findings support previous cross-sectional studies in aviation as well as non-aviation domains, which demonstrated the advantageous effect of prior experience and specialized expertise on older adults' skilled cognitive performances.

Pilot age and expertise predict flight simulator performance

PubMed Central

Kennedy, Quinn; Noda, Art; Yesavage, Jerome A.

2010-01-01

Background Expert knowledge may compensate for age-related declines in basic cognitive and sensory-motor abilities in some skill domains. We investigated the influence of age and aviation expertise (indexed by Federal Aviation Administration pilot ratings) on longitudinal flight simulator performance. Methods Over a 3-year period, 118 general aviation pilots aged 40 to 69 years were tested annually, in which their flight performance was scored in terms of 1) executing air-traffic controller communications; 2) traffic avoidance; 3) scanning cockpit instruments; 4) executing an approach to landing; and 5) a flight summary score. Results More expert pilots had better flight summary scores at baseline and showed less decline over time. Secondary analyses revealed that expertise effects were most evident in the accuracy of executing aviation communications, the measure on which performance declined most sharply over time. Regarding age, even though older pilots initially performed worse than younger pilots, over time older pilots showed less decline in flight summary scores than younger pilots. Secondary analyses revealed that the oldest pilots did well over time because their traffic avoidance performance improved more vs younger pilots. Conclusions These longitudinal findings support previous cross-sectional studies in aviation as well as non-aviation domains, which demonstrated the advantageous effect of prior experience and specialized expertise on older adults’ skilled cognitive performances. PMID:17325270

Expert Concept Mapping Study on Mobile Learning

ERIC Educational Resources Information Center

Borner, Dirk; Glahn, Christian; Stoyanov, Slavi; Kalz, Marco; Specht, Marcus

2010-01-01

Purpose: The present paper introduces concept mapping as a structured participative conceptualization approach to identify clusters of ideas and opinions generated by experts within the domain of mobile learning. Utilizing this approach, the paper aims to contribute to a definition of key domain characteristics by identifying the main educational…

Pediatric regional examination of the musculoskeletal system: a practice- and consensus-based approach.

PubMed

Foster, Helen; Kay, Lesley; May, Carl; Rapley, Tim

2011-11-01

Competent examination of the pediatric musculoskeletal (MSK) system is a vital component of clinical assessment of children with MSK presentations. The aim was to develop a regional MSK examination for school-age children that is age appropriate and reflects clinical practice. Qualitative and quantitative analyses involving video observation of clinical examination technique, systematic review, and expert consensus were employed to reveal descriptions, frequencies, and variations in technique for joint regions in various clinical scenarios. Systematic review and data from clinical observation were combined with feedback from a group of pediatric MSK experts through a web-based survey. All results were collated and discussed by consensus development groups to derive the pediatric Regional Examination of the Musculoskeletal System (pREMS). A total of 48 pediatric MSK expert clinicians were involved to derive pREMS. Systematic review revealed a paucity of evidence about regional pediatric MSK examination. Video observations of MSK examinations (a total of 2,901 maneuvers) performed by pediatric MSK experts (n = 11 doctors and 8 therapists) of 89 school-age children attending outpatient clinics in 7 UK pediatric rheumatology centers were followed by semistructured interviews with 14 of 19 clinicians. Video observation showed variation in examination techniques, most frequently at the hip and knee in the context of mechanical and inflammatory clinical scenarios. pREMS is the first practice- and consensus-based regional pediatric MSK examination for school-age children. The structured approach is an important step toward improved pediatric MSK clinical skills relevant to clinical training. Copyright © 2011 by the American College of Rheumatology.

To Think or Not to Think: The Apparent Paradox of Expert Skill in Music Performance

ERIC Educational Resources Information Center

Geeves, Andrew; McIlwain, Doris J. F.; Sutton, John; Christensen, Wayne

2014-01-01

Expert skill in music performance involves an apparent paradox. On stage, expert musicians are required accurately to retrieve information that has been encoded over hours of practice. Yet they must also remain open to the demands of the ever-changing situational contingencies with which they are faced during performance. To further explore this…

The Impact of Selected Contextual Factors on Experts' Clinical Reasoning Performance (Does Context Impact Clinical Reasoning Performance in Experts?)

ERIC Educational Resources Information Center

Durning, Steven J.; Artino, Anthony R.; Boulet, John R.; Dorrance, Kevin; van der Vleuten, Cees; Schuwirth, Lambert

2012-01-01

Context specificity, or the variation in a participant's performance from one case, or situation, to the next, is a recognized problem in medical education. However, studies have not explored the potential reasons for context specificity in experts using the lens of situated cognition and cognitive load theories (CLT). Using these theories, we…

How much expert knowledge is it worth to put in conceptual hydrological models?

NASA Astrophysics Data System (ADS)

Antonetti, Manuel; Zappa, Massimiliano

2017-04-01

Both modellers and experimentalists agree on using expert knowledge to improve our conceptual hydrological simulations on ungauged basins. However, they use expert knowledge differently for both hydrologically mapping the landscape and parameterising a given hydrological model. Modellers use generally very simplified (e.g. topography-based) mapping approaches and put most of the knowledge for constraining the model by defining parameter and process relational rules. In contrast, experimentalists tend to invest all their detailed and qualitative knowledge about processes to obtain a spatial distribution of areas with different dominant runoff generation processes (DRPs) as realistic as possible, and for defining plausible narrow value ranges for each model parameter. Since, most of the times, the modelling goal is exclusively to simulate runoff at a specific site, even strongly simplified hydrological classifications can lead to satisfying results due to equifinality of hydrological models, overfitting problems and the numerous uncertainty sources affecting runoff simulations. Therefore, to test to which extent expert knowledge can improve simulation results under uncertainty, we applied a typical modellers' modelling framework relying on parameter and process constraints defined based on expert knowledge to several catchments on the Swiss Plateau. To map the spatial distribution of the DRPs, mapping approaches with increasing involvement of expert knowledge were used. Simulation results highlighted the potential added value of using all the expert knowledge available on a catchment. Also, combinations of event types and landscapes, where even a simplified mapping approach can lead to satisfying results, were identified. Finally, the uncertainty originated by the different mapping approaches was compared with the one linked to meteorological input data and catchment initial conditions.

Efficient fuzzy Bayesian inference algorithms for incorporating expert knowledge in parameter estimation

NASA Astrophysics Data System (ADS)

Rajabi, Mohammad Mahdi; Ataie-Ashtiani, Behzad

2016-05-01

Bayesian inference has traditionally been conceived as the proper framework for the formal incorporation of expert knowledge in parameter estimation of groundwater models. However, conventional Bayesian inference is incapable of taking into account the imprecision essentially embedded in expert provided information. In order to solve this problem, a number of extensions to conventional Bayesian inference have been introduced in recent years. One of these extensions is 'fuzzy Bayesian inference' which is the result of integrating fuzzy techniques into Bayesian statistics. Fuzzy Bayesian inference has a number of desirable features which makes it an attractive approach for incorporating expert knowledge in the parameter estimation process of groundwater models: (1) it is well adapted to the nature of expert provided information, (2) it allows to distinguishably model both uncertainty and imprecision, and (3) it presents a framework for fusing expert provided information regarding the various inputs of the Bayesian inference algorithm. However an important obstacle in employing fuzzy Bayesian inference in groundwater numerical modeling applications is the computational burden, as the required number of numerical model simulations often becomes extremely exhaustive and often computationally infeasible. In this paper, a novel approach of accelerating the fuzzy Bayesian inference algorithm is proposed which is based on using approximate posterior distributions derived from surrogate modeling, as a screening tool in the computations. The proposed approach is first applied to a synthetic test case of seawater intrusion (SWI) in a coastal aquifer. It is shown that for this synthetic test case, the proposed approach decreases the number of required numerical simulations by an order of magnitude. Then the proposed approach is applied to a real-world test case involving three-dimensional numerical modeling of SWI in Kish Island, located in the Persian Gulf. An expert elicitation methodology is developed and applied to the real-world test case in order to provide a road map for the use of fuzzy Bayesian inference in groundwater modeling applications.

Improved Vote Aggregation Techniques for the Geo-Wiki Cropland Capture Crowdsourcing Game

NASA Astrophysics Data System (ADS)

Baklanov, Artem; Fritz, Steffen; Khachay, Michael; Nurmukhametov, Oleg; Salk, Carl; See, Linda; Shchepashchenko, Dmitry

2016-04-01

Crowdsourcing is a new approach for solving data processing problems for which conventional methods appear to be inaccurate, expensive, or time-consuming. Nowadays, the development of new crowdsourcing techniques is mostly motivated by so called Big Data problems, including problems of assessment and clustering for large datasets obtained in aerospace imaging, remote sensing, and even in social network analysis. By involving volunteers from all over the world, the Geo-Wiki project tackles problems of environmental monitoring with applications to flood resilience, biomass data analysis and classification of land cover. For example, the Cropland Capture Game, which is a gamified version of Geo-Wiki, was developed to aid in the mapping of cultivated land, and was used to gather 4.5 million image classifications from the Earth's surface. More recently, the Picture Pile game, which is a more generalized version of Cropland Capture, aims to identify tree loss over time from pairs of very high resolution satellite images. Despite recent progress in image analysis, the solution to these problems is hard to automate since human experts still outperform the majority of machine learning algorithms and artificial systems in this field on certain image recognition tasks. The replacement of rare and expensive experts by a team of distributed volunteers seems to be promising, but this approach leads to challenging questions such as: how can individual opinions be aggregated optimally, how can confidence bounds be obtained, and how can the unreliability of volunteers be dealt with? In this paper, on the basis of several known machine learning techniques, we propose a technical approach to improve the overall performance of the majority voting decision rule used in the Cropland Capture Game. The proposed approach increases the estimated consistency with expert opinion from 77% to 86%.

A parallel strategy for implementing real-time expert systems using CLIPS

NASA Technical Reports Server (NTRS)

Ilyes, Laszlo A.; Villaseca, F. Eugenio; Delaat, John

1994-01-01

As evidenced by current literature, there appears to be a continued interest in the study of real-time expert systems. It is generally recognized that speed of execution is only one consideration when designing an effective real-time expert system. Some other features one must consider are the expert system's ability to perform temporal reasoning, handle interrupts, prioritize data, contend with data uncertainty, and perform context focusing as dictated by the incoming data to the expert system. This paper presents a strategy for implementing a real time expert system on the iPSC/860 hypercube parallel computer using CLIPS. The strategy takes into consideration not only the execution time of the software, but also those features which define a true real-time expert system. The methodology is then demonstrated using a practical implementation of an expert system which performs diagnostics on the Space Shuttle Main Engine (SSME). This particular implementation uses an eight node hypercube to process ten sensor measurements in order to simultaneously diagnose five different failure modes within the SSME. The main program is written in ANSI C and embeds CLIPS to better facilitate and debug the rule based expert system.

How Expert Pilots Think Cognitive Processes in Expert Decision Making

DTIC Science & Technology

1993-02-01

Management (CRM) This document is available to the public Advanced Qualification Program (AQP) through the National Technical Information Cognitive Task Analysis (CTA...8217 Selecting realistic EDM scenarios with critical events and performing a cognitive task analysis of novice vs. expert decision making for these events...scenarios with critical events and performing a cognitive task analysis of novice vs. expert decision making for these events is a basic requirement for

Spacecraft attitude control using a smart control system

NASA Technical Reports Server (NTRS)

Buckley, Brian; Wheatcraft, Louis

1992-01-01

Traditionally, spacecraft attitude control has been implemented using control loops written in native code for a space hardened processor. The Naval Research Lab has taken this approach during the development of the Attitude Control Electronics (ACE) package. After the system was developed and delivered, NRL decided to explore alternate technologies to accomplish this same task more efficiently. The approach taken by NRL was to implement the ACE control loops using systems technologies. The purpose of this effort was to: (1) research capabilities required of an expert system in processing a classic closed-loop control algorithm; (2) research the development environment required to design and test an embedded expert systems environment; (3) research the complexity of design and development of expert systems versus a conventional approach; and (4) test the resulting systems against the flight acceptance test software for both response and accuracy. Two expert systems were selected to implement the control loops. Criteria used for the selection of the expert systems included that they had to run in both embedded systems and ground based environments. Using two different expert systems allowed a comparison of the real-time capabilities, inferencing capabilities, and the ground-based development environment. The two expert systems chosen for the evaluation were Spacecraft Command Language (SCL), and NEXTPERT Object. SCL is a smart control system produced for the NRL by Interface and Control Systems (ICS). SCL was developed to be used for real-time command, control, and monitoring of a new generation of spacecraft. NEXPERT Object is a commercially available product developed by Neuron Data. Results of the effort were evaluated using the ACE test bed. The ACE test bed had been developed and used to test the original flight hardware and software using simulators and flight-like interfaces. The test bed was used for testing the expert systems in a 'near-flight' environment. The technical approach, the system architecture, the development environments, knowledge base development, and results of this effort are detailed.

Effects of competitive pressure on expert performance: underlying psychological, physiological, and kinematic mechanisms.

PubMed

Cooke, Andrew; Kavussanu, Maria; McIntyre, David; Boardley, Ian D; Ring, Christopher

2011-08-01

Although it is well established that performance is influenced by competitive pressure, our understanding of the mechanisms which underlie the pressure-performance relationship is limited. The current experiment examined mediators of the relationship between competitive pressure and motor skill performance of experts. Psychological, physiological, and kinematic responses to three levels of competitive pressure were measured in 50 expert golfers, during a golf putting task. Elevated competitive pressure increased putting accuracy, anxiety, effort, and heart rate, but decreased grip force. Quadratic effects of pressure were noted for self-reported conscious processing and impact velocity. Mediation analyses revealed that effort and heart rate partially mediated improved performance. The findings indicate that competitive pressure elicits effects on expert performance through both psychological and physiological pathways. Copyright © 2011 Society for Psychophysiological Research.

An engineering approach to the use of expert systems technology in avionics applications

NASA Technical Reports Server (NTRS)

Duke, E. L.; Regenie, V. A.; Brazee, M.; Brumbaugh, R. W.

1986-01-01

The concept of using a knowledge compiler to transform the knowledge base and inference mechanism of an expert system into a conventional program is presented. The need to accommodate real-time systems requirements in applications such as embedded avionics is outlined. Expert systems and a brief comparison of expert systems and conventional programs are reviewed. Avionics applications of expert systems are discussed before the discussions of applying the proposed concept to example systems using forward and backward chaining.

Intrusion Detection Systems with Live Knowledge System

DTIC Science & Technology

2016-05-31

Ripple -down Rule (RDR) to maintain the knowledge from human experts with knowledge base generated by the Induct RDR, which is a machine-learning based RDR...propose novel approach that uses Ripple -down Rule (RDR) to maintain the knowledge from human experts with knowledge base generated by the Induct RDR...detection model by applying Induct RDR approach. The proposed induct RDR ( Ripple Down Rules) approach allows to acquire the phishing detection

Incorporating priors on expert performance parameters for segmentation validation and label fusion: a maximum a posteriori STAPLE

PubMed Central

Commowick, Olivier; Warfield, Simon K

2010-01-01

In order to evaluate the quality of segmentations of an image and assess intra- and inter-expert variability in segmentation performance, an Expectation Maximization (EM) algorithm for Simultaneous Truth And Performance Level Estimation (STAPLE) was recently developed. This algorithm, originally presented for segmentation validation, has since been used for many applications, such as atlas construction and decision fusion. However, the manual delineation of structures of interest is a very time consuming and burdensome task. Further, as the time required and burden of manual delineation increase, the accuracy of the delineation is decreased. Therefore, it may be desirable to ask the experts to delineate only a reduced number of structures or the segmentation of all structures by all experts may simply not be achieved. Fusion from data with some structures not segmented by each expert should be carried out in a manner that accounts for the missing information. In other applications, locally inconsistent segmentations may drive the STAPLE algorithm into an undesirable local optimum, leading to misclassifications or misleading experts performance parameters. We present a new algorithm that allows fusion with partial delineation and which can avoid convergence to undesirable local optima in the presence of strongly inconsistent segmentations. The algorithm extends STAPLE by incorporating prior probabilities for the expert performance parameters. This is achieved through a Maximum A Posteriori formulation, where the prior probabilities for the performance parameters are modeled by a beta distribution. We demonstrate that this new algorithm enables dramatically improved fusion from data with partial delineation by each expert in comparison to fusion with STAPLE. PMID:20879379

Incorporating priors on expert performance parameters for segmentation validation and label fusion: a maximum a posteriori STAPLE.

PubMed

Commowick, Olivier; Warfield, Simon K

2010-01-01

In order to evaluate the quality of segmentations of an image and assess intra- and inter-expert variability in segmentation performance, an Expectation Maximization (EM) algorithm for Simultaneous Truth And Performance Level Estimation (STAPLE) was recently developed. This algorithm, originally presented for segmentation validation, has since been used for many applications, such as atlas construction and decision fusion. However, the manual delineation of structures of interest is a very time consuming and burdensome task. Further, as the time required and burden of manual delineation increase, the accuracy of the delineation is decreased. Therefore, it may be desirable to ask the experts to delineate only a reduced number of structures or the segmentation of all structures by all experts may simply not be achieved. Fusion from data with some structures not segmented by each expert should be carried out in a manner that accounts for the missing information. In other applications, locally inconsistent segmentations may drive the STAPLE algorithm into an undesirable local optimum, leading to misclassifications or misleading experts performance parameters. We present a new algorithm that allows fusion with partial delineation and which can avoid convergence to undesirable local optima in the presence of strongly inconsistent segmentations. The algorithm extends STAPLE by incorporating prior probabilities for the expert performance parameters. This is achieved through a Maximum A Posteriori formulation, where the prior probabilities for the performance parameters are modeled by a beta distribution. We demonstrate that this new algorithm enables dramatically improved fusion from data with partial delineation by each expert in comparison to fusion with STAPLE.

Tuberculosis-Diagnostic Expert System: an architecture for translating patients information from the web for use in tuberculosis diagnosis.

PubMed

Osamor, Victor C; Azeta, Ambrose A; Ajulo, Oluseyi O

2014-12-01

Over 1.5-2 million tuberculosis deaths occur annually. Medical professionals are faced with a lot of challenges in delivering good health-care with unassisted automation in hospitals where there are several patients who need the doctor's attention. To automate the pre-laboratory screening process against tuberculosis infection to aid diagnosis and make it fast and accessible to the public via the Internet. The expert system we have built is designed to also take care of people who do not have access to medical experts, but would want to check their medical status. A rule-based approach has been used, and unified modeling language and the client-server architecture technique were applied to model the system and to develop it as a web-based expert system for tuberculosis diagnosis. Algorithmic rules in the Tuberculosis-Diagnosis Expert System necessitate decision coverage where tuberculosis is either suspected or not suspected. The architecture consists of a rule base, knowledge base, and patient database. These units interact with the inference engine, which receives patient' data through the Internet via a user interface. We present the architecture of the Tuberculosis-Diagnosis Expert System and its implementation. We evaluated it for usability to determine the level of effectiveness, efficiency and user satisfaction. The result of the usability evaluation reveals that the system has a usability of 4.08 out of a scale of 5. This is an indication of a more-than-average system performance. Several existing expert systems have been developed for the purpose of supporting different medical diagnoses, but none is designed to translate tuberculosis patients' symptomatic data for online pre-laboratory screening. Our Tuberculosis-Diagnosis Expert System is an effective solution for the implementation of the needed web-based expert system diagnosis. © The Author(s) 2013.

An expert system for diagnostics and estimation of steam turbine components condition

NASA Astrophysics Data System (ADS)

Murmansky, B. E.; Aronson, K. E.; Brodov, Yu. M.

2017-11-01

The report describes an expert system of probability type for diagnostics and state estimation of steam turbine technological subsystems components. The expert system is based on Bayes’ theorem and permits to troubleshoot the equipment components, using expert experience, when there is a lack of baseline information on the indicators of turbine operation. Within a unified approach the expert system solves the problems of diagnosing the flow steam path of the turbine, bearings, thermal expansion system, regulatory system, condensing unit, the systems of regenerative feed-water and hot water heating. The knowledge base of the expert system for turbine unit rotors and bearings contains a description of 34 defects and of 104 related diagnostic features that cause a change in its vibration state. The knowledge base for the condensing unit contains 12 hypotheses and 15 evidence (indications); the procedures are also designated for 20 state parameters estimation. Similar knowledge base containing the diagnostic features and faults hypotheses are formulated for other technological subsystems of turbine unit. With the necessary initial information available a number of problems can be solved within the expert system for various technological subsystems of steam turbine unit: for steam flow path it is the correlation and regression analysis of multifactor relationship between the vibration parameters variations and the regime parameters; for system of thermal expansions it is the evaluation of force acting on the longitudinal keys depending on the temperature state of the turbine cylinder; for condensing unit it is the evaluation of separate effect of the heat exchange surface contamination and of the presence of air in condenser steam space on condenser thermal efficiency performance, as well as the evaluation of term for condenser cleaning and for tube system replacement and so forth. With a lack of initial information the expert system enables to formulate a diagnosis, calculating the probability of faults hypotheses, given the degree of the expert confidence in estimation of turbine components operation parameters.

Identifying influenza-like illness presentation from unstructured general practice clinical narrative using a text classifier rule-based expert system versus a clinical expert.

PubMed

MacRae, Jayden; Love, Tom; Baker, Michael G; Dowell, Anthony; Carnachan, Matthew; Stubbe, Maria; McBain, Lynn

2015-10-06

We designed and validated a rule-based expert system to identify influenza like illness (ILI) from routinely recorded general practice clinical narrative to aid a larger retrospective research study into the impact of the 2009 influenza pandemic in New Zealand. Rules were assessed using pattern matching heuristics on routine clinical narrative. The system was trained using data from 623 clinical encounters and validated using a clinical expert as a gold standard against a mutually exclusive set of 901 records. We calculated a 98.2 % specificity and 90.2 % sensitivity across an ILI incidence of 12.4 % measured against clinical expert classification. Peak problem list identification of ILI by clinical coding in any month was 9.2 % of all detected ILI presentations. Our system addressed an unusual problem domain for clinical narrative classification; using notational, unstructured, clinician entered information in a community care setting. It performed well compared with other approaches and domains. It has potential applications in real-time surveillance of disease, and in assisted problem list coding for clinicians. Our system identified ILI presentation with sufficient accuracy for use at a population level in the wider research study. The peak coding of 9.2 % illustrated the need for automated coding of unstructured narrative in our study.

The impact of expertise in olfaction

PubMed Central

Royet, Jean-Pierre; Plailly, Jane; Saive, Anne-Lise; Veyrac, Alexandra; Delon-Martin, Chantal

2013-01-01

Olfactory expertise remains poorly understood, most likely because experts in odor, such as perfumers, sommeliers, and oenologists, are much rarer than experts in other modalities, such as musicians or sportsmen. In this review, we address the specificities of odor expertise in both odor experts and in a priori untrained individuals who have undergone specific olfactory training in the frame of an experiment, such as repeated exposure to odors or associative learning. Until the 21st century, only the behavioral effects of olfactory training of untrained control individuals had been reported, revealing an improvement of olfactory performance in terms of sensitivity, discrimination, memory, and identification. Behavioral studies of odor experts have been scarce, with inconsistent or inconclusive results. Recently, the development of cerebral imaging techniques has enabled the identification of brain areas and neural networks involved in odor processing, revealing functional and structural modifications as a function of experience. The behavioral approach to odor expertise has also evolved. Researchers have particularly focused on odor mental imagery, which is characteristic of odor experts, because this ability is absent in the average person but is part of a perfumer’s professional practice. This review summarizes behavioral, functional, and structural findings on odor expertise. These data are compared with those obtained using animals subjected to prolonged olfactory exposure or to olfactory-enriched environments and are discussed in the context of functional and structural plasticity. PMID:24379793

A Deliberate Practice Approach to Teaching Phylogenetic Analysis

PubMed Central

Hobbs, F. Collin; Johnson, Daniel J.; Kearns, Katherine D.

2013-01-01

One goal of postsecondary education is to assist students in developing expert-level understanding. Previous attempts to encourage expert-level understanding of phylogenetic analysis in college science classrooms have largely focused on isolated, or “one-shot,” in-class activities. Using a deliberate practice instructional approach, we designed a set of five assignments for a 300-level plant systematics course that incrementally introduces the concepts and skills used in phylogenetic analysis. In our assignments, students learned the process of constructing phylogenetic trees through a series of increasingly difficult tasks; thus, skill development served as a framework for building content knowledge. We present results from 5 yr of final exam scores, pre- and postconcept assessments, and student surveys to assess the impact of our new pedagogical materials on student performance related to constructing and interpreting phylogenetic trees. Students improved in their ability to interpret relationships within trees and improved in several aspects related to between-tree comparisons and tree construction skills. Student feedback indicated that most students believed our approach prepared them to engage in tree construction and gave them confidence in their abilities. Overall, our data confirm that instructional approaches implementing deliberate practice address student misconceptions, improve student experiences, and foster deeper understanding of difficult scientific concepts. PMID:24297294

Harmonization of ethics in health technology assessment: a revision of the Socratic approach.

PubMed

Hofmann, Bjørn; Droste, Sigrid; Oortwijn, Wija; Cleemput, Irina; Sacchini, Dario

2014-01-01

Ethics has been part of health technology assessment (HTA) from its beginning in the 1970s, and is currently part of HTA definitions. Several methods in ethics have been used in HTA. Some approaches have been developed especially for HTA, such as the Socratic approach, which has been used for a wide range of health technologies. The Socratic approach is used in several ways, and there is a need for harmonization to promote its usability and the transferability of its results. Accordingly, the objective of this study was to stimulate experts in ethics and HTA to revise the Socratic approach. Based on the current literature and experiences in applying methods in ethics, a panel of ethics experts involved in HTA critically analyzed the limitations of the Socratic approach during a face-to-face workshop. On the basis of this analysis a revision of the Socratic approach was agreed on after deliberation in several rounds through e-mail correspondence. Several limitations with the Socratic approach are identified and addressed in the revised version which consists of a procedure of six steps, 7 main questions and thirty-three explanatory and guiding questions. The revised approach has a broader scope and provides more guidance than its predecessor. Methods for information retrieval have been elaborated. The presented revision of the Socratic approach is the result of a joint effort of experts in the field of ethics and HTA. Consensus is reached in the expert panel on an approach that is considered to be more clear, comprehensive, and applicable for addressing ethical issues in HTA.

Fundamental arthroscopic skill differentiation with virtual reality simulation.

PubMed

Rose, Kelsey; Pedowitz, Robert

2015-02-01

The purpose of this study was to investigate the use and validity of virtual reality modules as part of the educational approach to mastering arthroscopy in a safe environment by assessing the ability to distinguish between experience levels. Additionally, the study aimed to evaluate whether experts have greater ambidexterity than do novices. Three virtual reality modules (Swemac/Augmented Reality Systems, Linkoping, Sweden) were created to test fundamental arthroscopic skills. Thirty participants-10 experts consisting of faculty, 10 intermediate participants consisting of orthopaedic residents, and 10 novices consisting of medical students-performed each exercise. Steady and Telescope was designed to train centering and image stability. Steady and Probe was designed to train basic triangulation. Track and Moving Target was designed to train coordinated motions of arthroscope and probe. Metrics reflecting speed, accuracy, and efficiency of motion were used to measure construct validity. Steady and Probe and Track a Moving Target both exhibited construct validity, with better performance by experts and intermediate participants than by novices (P < .05), whereas Steady and Telescope did not show validity. There was an overall trend toward better ambidexterity as a function of greater surgical experience, with experts consistently more proficient than novices throughout all 3 modules. This study represents a new way to assess basic arthroscopy skills using virtual reality modules developed through task deconstruction. Participants with the most arthroscopic experience performed better and were more consistent than novices on all 3 virtual reality modules. Greater arthroscopic experience correlates with more symmetry of ambidextrous performance. However, further adjustment of the modules may better simulate fundamental arthroscopic skills and discriminate between experience levels. Arthroscopy training is a critical element of orthopaedic surgery resident training. Developing techniques to safely and effectively train these skills is critical for patient safety and resident education. Copyright © 2015 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.

Feasibility of using a reliable automated Doppler flow velocity measurements for research and clinical practices

NASA Astrophysics Data System (ADS)

Zolgharni, Massoud; Dhutia, Niti M.; Cole, Graham D.; Willson, Keith; Francis, Darrel P.

2014-03-01

Echocardiographers are often unkeen to make the considerable time investment to make additional multiple measurements of Doppler velocity. Main hurdle to obtaining multiple measurements is the time required to manually trace a series of Doppler traces. To make it easier to analyse more beats, we present an automated system for Doppler envelope quantification. It analyses long Doppler strips, spanning many heartbeats, and does not require the electrocardiogram to isolate individual beats. We tested its measurement of velocity-time-integral and peak-velocity against the reference standard defined as the average of three experts who each made three separate measurements. The automated measurements of velocity-time-integral showed strong correspondence (R2 = 0.94) and good Bland-Altman agreement (SD = 6.92%) with the reference consensus expert values, and indeed performed as well as the individual experts (R2 = 0.90 to 0.96, SD = 5.66% to 7.64%). The same performance was observed for peak-velocities; (R2 = 0.98, SD = 2.95%) and (R2 = 0.93 to 0.98, SD = 2.94% to 5.12%). This automated technology allows <10 times as many beats to be acquired and analysed compared to the conventional manual approach, with each beat maintaining its accuracy.

A theory of expert leadership (TEL) in psychiatry.

PubMed

Goodall, Amanda H

2016-06-01

Leaders' technical competence - 'expert knowledge' - has been shown in many settings to be associated with better organizational performance. In universities, for example, there is longitudinal evidence that research-focused scholars make the best leaders; results from a hospital study show that doctors instead of professional managers are most closely associated with the best performing institutions. To explain these patterns, and raise hypotheses, a theory of expert leadership (TEL) has been developed that might explain these patterns. In this paper the framework for expert leadership is applied to psychiatry. The TEL proposes that psychiatric leaders, as opposed to non-expert managers, improve organizational performance through several channels. First, experts' knowledge influences organizational strategy. Second, having been 'one of them', a psychiatrist understands how to create the optimal work environment for psychiatric teams, through appropriate goal-setting, evaluation and support. These factors are positively associated with workers' wellbeing and performance. Third, exceptional psychiatrist-leaders are likely to set high standards for hiring. Fourth, leaders' credibility extends their influence among core workers, and also signals organizational priorities to stakeholders. Finally, a necessary prerequisite of TEL is that expert leaders have direct executive power inclusive of budgetary and strategic oversight. © The Royal Australian and New Zealand College of Psychiatrists 2015.

The Viewpoints of Students and Evaluation Experts About Performance Processes of Faculty Member Evaluation at Mazandaran University of Medical Sciences, 2014.

PubMed

Ghahrani, Nassim; Balaghafari, Azita; Aligolbandi, Kobra; Vahedi, Mohammad; Siamian, Hasan

2015-06-01

One of the most common ways used in most of the countries and Iran to determine the status of teacher training is the evaluation by students. The most common method of evaluation is the survey questionnaire, the content of a number of questions about educational activities provided to the students. The researchers plan to evaluate the students' and experts' performances at Mazandaran University of Medical Sciences on the process of evaluating the performance of teachers, they examined in 2014. This study surveys the students and experts in the evaluation of faculty members' performance process. The study subjects were 3904 students and 37 evaluation expert of Mazandaran University of Medical Sciences. Using Cochran sampling formula of 350 students through proportional stratified random sampling were selected. The experts' viewpoint, method was used. Data collection tools consisted of 14 questions with answers Yes, or, I don't know. Descriptive Statistical analysis of the data and chi-square test was performed. From total of 350 students, 346 and the entire 37 evaluations expert participated in this study. Most of the students, 80 (23.12%) and the largest number of experts, 8 (21.62%) were from Sari Allied Medical Sciences Faculty. Most of the demographic information about gender were, 255 female students (74.56%) and 29 female experts (78.37%). In most age groups of students, 188 (55.62 percent) were in the category of 18 to 20 years, and the experts, 19 (51.35%) were in the category of 22 and 31 years. Most students, 232 of them (70.95%) were in semester 2 and 4. Most experts, 20 (54.05 percent) were under 10 years of work experience. The comparison between the views of students and experts in the evaluation process between the schools of Mazandaran University of Medical Sciences, Sari School of Nursing and Midwifery, there was difference between the opinions of experts and students (p-value=0.01. It showed 86.7% student and 33.3% of experts is satisfied with the evaluation process. on comparison of students and experts viewpoints on the implementation of the evaluation process, it is noteworthy that among students of different opinions on how the evaluation process. It worth to mention that there is insignificant difference between their viewpoints and majority of students and evaluation experts with the evaluation the process. In addition, the experts evaluated at different schools, most of them are satisfied the process.

Becoming an Expert: Developing Expertise in an Applied Discipline

ERIC Educational Resources Information Center

Kuhlmann, Diane Orlich; Ardichvili, Alexandre

2015-01-01

Purpose: This paper aims to examine the development of expertise in an applied discipline by addressing the research question: How is professional expertise developed in an applied profession? Design/methodology/approach: Using a grounded theory methodology (GTM), nine technical-tax experts, and three experienced, non-expert tax professionals were…

Factors Influencing Continuing Professional Development: A Delphi Study among Nursing Experts

ERIC Educational Resources Information Center

Brekelmans, Gerard; Poell, Rob F.; van Wijk, Kees

2013-01-01

Purpose: The aim of this paper is to present an inventory of expert opinions on the factors that influence the participation of registered nurses in continuing professional development (CPD) activities. Design/methodology/approach: A Delphi study was conducted among 38 Dutch experts (nursing employers, managers, education institutions, and…

Robot companions and ethics a pragmatic approach of ethical design.

PubMed

Cornet, Gérard

2013-12-01

From his experience as ethical expert for two Robot Companion prototype projects aiming at empowering older MCI persons to remain at home and to support their family carers, Gerard Cornet, Gerontologist, review the ethical rules, principles and pragmatic approaches in different cultures. The ethical process of these two funded projects, one European, Companionable (FP7 e-inclusion call1), the other French, Quo vadis (ANR tecsan) are described from the inclusion of the targeted end users in the process, to the assessment and ranking of their main needs and whishes to design the specifications, test the performance expected. Obstacles to turn round and limits for risks evaluation (directs or implicit), acceptability, utility, respect of intimacy and dignity, and balance with freedom and security and frontiers to artificial intelligence are discussed As quoted in the discussion with the French and Japanese experts attending the Toulouse Robotics and medicine symposium (March 26th 2011), the need of a new ethical approach, going further the present ethical rules is needed for the design and social status of ethical robots, having capacity cas factor of progress and global quality of innovation design in an ageing society.

A Hybrid Fuzzy Model for Lean Product Development Performance Measurement

NASA Astrophysics Data System (ADS)

Osezua Aikhuele, Daniel; Mohd Turan, Faiz

2016-02-01

In the effort for manufacturing companies to meet up with the emerging consumer demands for mass customized products, many are turning to the application of lean in their product development process, and this is gradually moving from being a competitive advantage to a necessity. However, due to lack of clear understanding of the lean performance measurements, many of these companies are unable to implement and fully integrated the lean principle into their product development process. Extensive literature shows that only few studies have focus systematically on the lean product development performance (LPDP) evaluation. In order to fill this gap, the study therefore proposed a novel hybrid model based on Fuzzy Reasoning Approach (FRA), and the extension of Fuzzy-AHP and Fuzzy-TOPSIS methods for the assessment of the LPDP. Unlike the existing methods, the model considers the importance weight of each of the decision makers (Experts) since the performance criteria/attributes are required to be rated, and these experts have different level of expertise. The rating is done using a new fuzzy Likert rating scale (membership-scale) which is designed such that it can address problems resulting from information lost/distortion due to closed-form scaling and the ordinal nature of the existing Likert scale.

An Expert System for the Development of Efficient Parallel Code

NASA Technical Reports Server (NTRS)

Jost, Gabriele; Chun, Robert; Jin, Hao-Qiang; Labarta, Jesus; Gimenez, Judit

2004-01-01

We have built the prototype of an expert system to assist the user in the development of efficient parallel code. The system was integrated into the parallel programming environment that is currently being developed at NASA Ames. The expert system interfaces to tools for automatic parallelization and performance analysis. It uses static program structure information and performance data in order to automatically determine causes of poor performance and to make suggestions for improvements. In this paper we give an overview of our programming environment, describe the prototype implementation of our expert system, and demonstrate its usefulness with several case studies.

Bottom-up approaches to strengthening child protection systems: Placing children, families, and communities at the center.

PubMed

Wessells, Michael G

2015-05-01

Efforts to strengthen national child protection systems have frequently taken a top-down approach of imposing formal, government-managed services. Such expert-driven approaches are often characterized by low use of formal services and the misalignment of the nonformal and formal aspects of the child protection system. This article examines an alternative approach of community-driven, bottom-up work that enables nonformal-formal collaboration and alignment, greater use of formal services, internally driven social change, and high levels of community ownership. The dominant approach of reliance on expert-driven Child Welfare Committees produces low levels of community ownership. Using an approach developed and tested in rural Sierra Leone, community-driven action, including collaboration and linkages with the formal system, promoted the use of formal services and achieved increased ownership, effectiveness, and sustainability of the system. The field needs less reliance on expert-driven approaches and much wider use of slower, community-driven, bottom-up approaches to child protection. Copyright © 2015 The Author. Published by Elsevier Ltd.. All rights reserved.

Expert anticipatory skill in striking sports: a review and a model.

PubMed

Müller, Sean; Abernethy, Bruce

2012-06-01

Expert performers in striking sports can hit objects moving at high speed with incredible precision. Exceptionally well developed anticipation skills are necessary to cope with the severe constraints on interception. In this papr we provide a review of the empirical evidence regarding expert interception in striking sports and propose a preliminary model of expert anticipation. Central to the review and the model is the notion that the visual information used to guide the sequential phases of the striking action is systematically different between experts and nonexperts. Knowing the factors that contribute to expert anticipation, and how anticipation may guide skilled performance in striking sports, has practical implications for assessment and training across skill levels.

Engineering monitoring expert system's developer

NASA Technical Reports Server (NTRS)

Lo, Ching F.

1991-01-01

This research project is designed to apply artificial intelligence technology including expert systems, dynamic interface of neural networks, and hypertext to construct an expert system developer. The developer environment is specifically suited to building expert systems which monitor the performance of ground support equipment for propulsion systems and testing facilities. The expert system developer, through the use of a graphics interface and a rule network, will be transparent to the user during rule constructing and data scanning of the knowledge base. The project will result in a software system that allows its user to build specific monitoring type expert systems which monitor various equipments used for propulsion systems or ground testing facilities and accrues system performance information in a dynamic knowledge base.

Educator of the Court: The Role of the Expert Witness in Cases Involving Autism Spectrum Disorder

PubMed Central

Berryessa, Colleen M.

2017-01-01

The role of the expert witness in legal contexts is to educate fact finders of the court who may have no background in the expert’s area. This role can be especially difficult for those who assist in cases involving individuals with Autism Spectrum Disorder (ASD). As expert assistance on ASD is crucial to ensuring just outcomes for individuals diagnosed with ASD, knowledge on how expert witnesses perceive and approach their roles, and what factors may influence these perceptions, is essential. This qualitative research utilizes semi-structured interviews with a sample of expert witnesses in cases involving ASD, analyzed using a grounded-theory constant comparative analytic approach. Data reveal that experts appear to view their roles in court as reconstructionists, educators, myth-dispellers, and most of all, communicators, actively using their testimony to fill these roles in cases. These results also allow for the development of a model that illustrates two areas that coalesce to affect how experts view their roles in court: (1) personal experiences of experts in cases in which they have been involved; and (2) influences outside experts’ personal experiences, such as their general opinions or observations regarding ASD and its relationship to the criminal justice system. PMID:28943746

Radio Galaxy Zoo: Machine learning for radio source host galaxy cross-identification

NASA Astrophysics Data System (ADS)

Alger, M. J.; Banfield, J. K.; Ong, C. S.; Rudnick, L.; Wong, O. I.; Wolf, C.; Andernach, H.; Norris, R. P.; Shabala, S. S.

2018-05-01

We consider the problem of determining the host galaxies of radio sources by cross-identification. This has traditionally been done manually, which will be intractable for wide-area radio surveys like the Evolutionary Map of the Universe (EMU). Automated cross-identification will be critical for these future surveys, and machine learning may provide the tools to develop such methods. We apply a standard approach from computer vision to cross-identification, introducing one possible way of automating this problem, and explore the pros and cons of this approach. We apply our method to the 1.4 GHz Australian Telescope Large Area Survey (ATLAS) observations of the Chandra Deep Field South (CDFS) and the ESO Large Area ISO Survey South 1 (ELAIS-S1) fields by cross-identifying them with the Spitzer Wide-area Infrared Extragalactic (SWIRE) survey. We train our method with two sets of data: expert cross-identifications of CDFS from the initial ATLAS data release and crowdsourced cross-identifications of CDFS from Radio Galaxy Zoo. We found that a simple strategy of cross-identifying a radio component with the nearest galaxy performs comparably to our more complex methods, though our estimated best-case performance is near 100 per cent. ATLAS contains 87 complex radio sources that have been cross-identified by experts, so there are not enough complex examples to learn how to cross-identify them accurately. Much larger datasets are therefore required for training methods like ours. We also show that training our method on Radio Galaxy Zoo cross-identifications gives comparable results to training on expert cross-identifications, demonstrating the value of crowdsourced training data.

An expert system-based approach to prediction of year-to-year climatic variations in the North Atlantic region

NASA Astrophysics Data System (ADS)

Rodionov, S. N.; Martin, J. H.

1999-07-01

A novel approach to climate forecasting on an interannual time scale is described. The approach is based on concepts and techniques from artificial intelligence and expert systems. The suitability of this approach to climate diagnostics and forecasting problems and its advantages compared with conventional forecasting techniques are discussed. The article highlights some practical aspects of the development of climatic expert systems (CESs) and describes an implementation of such a system for the North Atlantic (CESNA). Particular attention is paid to the content of CESNA's knowledge base and those conditions that make climatic forecasts one to several years in advance possible. A detailed evaluation of the quality of the experimental real-time forecasts made by CESNA for the winters of 1995-1996, 1996-1997 and 1997-1998 are presented.

The need for a systematic approach to disaster psychosocial response: a suggested competency framework.

PubMed

Cox, Robin S; Danford, Taryn

2014-04-01

Competency models attempt to define what makes expert performers "experts." Successful disaster psychosocial planning and the institutionalizing of psychosocial response within emergency management require clearly-defined skill sets. This necessitates anticipating both the short- and long-term psychosocial implications of a disaster or health emergency (ie, pandemic) by developing effective and sustained working relationships among psychosocial providers, programs, and other planning partners. The following article outlines recommended competencies for psychosocial responders to enable communities and organizations to prepare for and effectively manage a disaster response. Competency-based models are founded on observable performance or behavioral indicators, attitudes, traits, or personalities related to effective performance in a specific role or job. After analyzing the literature regarding competency-based frameworks, a proposed competency framework that details 13 competency domains is suggested. Each domain describes a series of competencies and suggests behavioral indicators for each competency and, where relevant, associated training expectations. These domains have been organized under three distinct categories or types of competencies: general competency domains; disaster psychosocial intervention competency domains; and disaster psychosocial program leadership and coordination competency domains. Competencies do not replace job descriptions nor should they be confused with performance assessments. What they can do is update and revise job descriptions; orient existing and new employees to their disaster/emergency roles and responsibilities; target training needs; provide the basis for ongoing self-assessment by agencies and individuals as they evaluate their readiness to respond; and provide a job- or role-relevant basis for performance appraisal dimensions or standards and review discussions. Using a modular approach to psychosocial planning, service providers can improve their response capacity by utilizing differences in levels of expertise and training. The competencies outlined in this paper can thus be used to standardize expectations about levels of psychosocial support interventions. In addition this approach provides an adaptable framework that can be adjusted for various contexts.

Bias in dissemination of clinical research findings: structured OPEN framework of what, who and why, based on literature review and expert consensus.

PubMed

Bassler, Dirk; Mueller, Katharina F; Briel, Matthias; Kleijnen, Jos; Marusic, Ana; Wager, Elizabeth; Antes, Gerd; von Elm, Erik; Altman, Douglas G; Meerpohl, Joerg J

2016-01-21

The aim of this study is to review highly cited articles that focus on non-publication of studies, and to develop a consistent and comprehensive approach to defining (non-) dissemination of research findings. We performed a scoping review of definitions of the term 'publication bias' in highly cited publications. Ideas and experiences of a core group of authors were collected in a draft document, which was complemented by the findings from our literature search. The draft document including findings from the literature search was circulated to an international group of experts and revised until no additional ideas emerged and consensus was reached. We propose a new approach to the comprehensive conceptualisation of (non-) dissemination of research. Our 'What, Who and Why?' approach includes issues that need to be considered when disseminating research findings (What?), the different players who should assume responsibility during the various stages of conducting a clinical trial and disseminating clinical trial documents (Who?), and motivations that might lead the various players to disseminate findings selectively, thereby introducing bias in the dissemination process (Why?). Our comprehensive framework of (non-) dissemination of research findings, based on the results of a scoping literature search and expert consensus will facilitate the development of future policies and guidelines regarding the multifaceted issue of selective publication, historically referred to as 'publication bias'. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

A multi-method approach to curriculum development for in-service training in China's newly established health emergency response offices.

PubMed

Wang, Yadong; Li, Xiangrui; Yuan, Yiwen; Patel, Mahomed S

2014-01-01

To describe an innovative approach for developing and implementing an in-service curriculum in China for staff of the newly established health emergency response offices (HEROs), and that is generalisable to other settings. The multi-method training needs assessment included reviews of the competency domains needed to implement the International Health Regulations (2005) as well as China's policies and emergency regulations. The review, iterative interviews and workshops with experts in government, academia, the military, and with HERO staff were reviewed critically by an expert technical advisory panel. Over 1600 participants contributed to curriculum development. Of the 18 competency domains identified as essential for HERO staff, nine were developed into priority in-service training modules to be conducted over 2.5 weeks. Experts from academia and experienced practitioners prepared and delivered each module through lectures followed by interactive problem-solving exercises and desktop simulations to help trainees apply, experiment with, and consolidate newly acquired knowledge and skills. This study adds to the emerging literature on China's enduring efforts to strengthen its emergency response capabilities since the outbreak of SARS in 2003. The multi-method approach to curriculum development in partnership with senior policy-makers, researchers, and experienced practitioners can be applied in other settings to ensure training is responsive and customized to local needs, resources and priorities. Ongoing curriculum development should reflect international standards and be coupled with the development of appropriate performance support systems at the workplace for motivating staff to apply their newly acquired knowledge and skills effectively and creatively.

Interictal epileptiform discharge characteristics underlying expert interrater agreement.

PubMed

Bagheri, Elham; Dauwels, Justin; Dean, Brian C; Waters, Chad G; Westover, M Brandon; Halford, Jonathan J

2017-10-01

The presence of interictal epileptiform discharges (IED) in the electroencephalogram (EEG) is a key finding in the medical workup of a patient with suspected epilepsy. However, inter-rater agreement (IRA) regarding the presence of IED is imperfect, leading to incorrect and delayed diagnoses. An improved understanding of which IED attributes mediate expert IRA might help in developing automatic methods for IED detection able to emulate the abilities of experts. Therefore, using a set of IED scored by a large number of experts, we set out to determine which attributes of IED predict expert agreement regarding the presence of IED. IED were annotated on a 5-point scale by 18 clinical neurophysiologists within 200 30-s EEG segments from recordings of 200 patients. 5538 signal analysis features were extracted from the waveforms, including wavelet coefficients, morphological features, signal energy, nonlinear energy operator response, electrode location, and spectrogram features. Feature selection was performed by applying elastic net regression and support vector regression (SVR) was applied to predict expert opinion, with and without the feature selection procedure and with and without several types of signal normalization. Multiple types of features were useful for predicting expert annotations, but particular types of wavelet features performed best. Local EEG normalization also enhanced best model performance. As the size of the group of EEGers used to train the models was increased, the performance of the models leveled off at a group size of around 11. The features that best predict inter-rater agreement among experts regarding the presence of IED are wavelet features, using locally standardized EEG. Our models for predicting expert opinion based on EEGer's scores perform best with a large group of EEGers (more than 10). By examining a large group of EEG signal analysis features we found that wavelet features with certain wavelet basis functions performed best to identify IEDs. Local normalization also improves predictability, suggesting the importance of IED morphology over amplitude-based features. Although most IED detection studies in the past have used opinion from three or fewer experts, our study suggests a "wisdom of the crowd" effect, such that pooling over a larger number of expert opinions produces a better correlation between expert opinion and objectively quantifiable features of the EEG. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Experience Does Not Equal Expertise in Recognizing Infrequent Incoming Gunfire: Neural Markers for Experience and Task Expertise at Peak Behavioral Performance

PubMed Central

Sherwin, Jason Samuel; Gaston, Jeremy Rodney

2015-01-01

For a soldier, decisions to use force can happen rapidly and sometimes lead to undesired consequences. In many of these situations, there is a rapid assessment by the shooter that recognizes a threat and responds to it with return fire. But the neural processes underlying these rapid decisions are largely unknown, especially amongst those with extensive weapons experience and expertise. In this paper, we investigate differences in weapons experts and non-experts during an incoming gunfire detection task. Specifically, we analyzed the electroencephalography (EEG) of eleven expert marksmen/soldiers and eleven non-experts while they listened to an audio scene consisting of a sequence of incoming and non-incoming gunfire events. Subjects were tasked with identifying each event as quickly as possible and committing their choice via a motor response. Contrary to our hypothesis, experts did not have significantly better behavioral performance or faster response time than novices. Rather, novices indicated trends of better behavioral performance than experts. These group differences were more dramatic in the EEG correlates of incoming gunfire detection. Using machine learning, we found condition-discriminating EEG activity among novices showing greater magnitude and covering longer periods than those found in experts. We also compared group-level source reconstruction on the maximum discriminating neural correlates and found that each group uses different neural structures to perform the task. From condition-discriminating EEG and source localization, we found that experts perceive more categorical overlap between incoming and non-incoming gunfire. Consequently, the experts did not perform as well behaviorally as the novices. We explain these unexpected group differences as a consequence of experience with gunfire not being equivalent to expertise in recognizing incoming gunfire. PMID:25658335

Defining landscape resistance values in least-cost connectivity models for the invasive grey squirrel: a comparison of approaches using expert-opinion and habitat suitability modelling.

PubMed

Stevenson-Holt, Claire D; Watts, Kevin; Bellamy, Chloe C; Nevin, Owen T; Ramsey, Andrew D

2014-01-01

Least-cost models are widely used to study the functional connectivity of habitat within a varied landscape matrix. A critical step in the process is identifying resistance values for each land cover based upon the facilitating or impeding impact on species movement. Ideally resistance values would be parameterised with empirical data, but due to a shortage of such information, expert-opinion is often used. However, the use of expert-opinion is seen as subjective, human-centric and unreliable. This study derived resistance values from grey squirrel habitat suitability models (HSM) in order to compare the utility and validity of this approach with more traditional, expert-led methods. Models were built and tested with MaxEnt, using squirrel presence records and a categorical land cover map for Cumbria, UK. Predictions on the likelihood of squirrel occurrence within each land cover type were inverted, providing resistance values which were used to parameterise a least-cost model. The resulting habitat networks were measured and compared to those derived from a least-cost model built with previously collated information from experts. The expert-derived and HSM-inferred least-cost networks differ in precision. The HSM-informed networks were smaller and more fragmented because of the higher resistance values attributed to most habitats. These results are discussed in relation to the applicability of both approaches for conservation and management objectives, providing guidance to researchers and practitioners attempting to apply and interpret a least-cost approach to mapping ecological networks.

Expert opinion on landslide susceptibility elicted by probabilistic inversion from scenario rankings

NASA Astrophysics Data System (ADS)

Lee, Katy; Dashwood, Claire; Lark, Murray

2016-04-01

For many natural hazards the opinion of experts, with experience in assessing susceptibility under different circumstances, is a valuable source of information on which to base risk assessments. This is particularly important where incomplete process understanding, and limited data, limit the scope to predict susceptibility by mechanistic or statistical modelling. The expert has a tacit model of a system, based on their understanding of processes and their field experience. This model may vary in quality, depending on the experience of the expert. There is considerable interest in how one may elicit expert understanding by a process which is transparent and robust, to provide a basis for decision support. One approach is to provide experts with a set of scenarios, and then to ask them to rank small overlapping subsets of these with respect to susceptibility. Methods of probabilistic inversion have been used to compute susceptibility scores for each scenario, implicit in the expert ranking. It is also possible to model these scores as functions of measurable properties of the scenarios. This approach has been used to assess susceptibility of animal populations to invasive diseases, to assess risk to vulnerable marine environments and to assess the risk in hypothetical novel technologies for food production. We will present the results of a study in which a group of geologists with varying degrees of expertise in assessing landslide hazards were asked to rank sets of hypothetical simplified scenarios with respect to land slide susceptibility. We examine the consistency of their rankings and the importance of different properties of the scenarios in the tacit susceptibility model that their rankings implied. Our results suggest that this is a promising approach to the problem of how experts can communicate their tacit model of uncertain systems to those who want to make use of their expertise.

The role of communication in breast cancer screening: a qualitative study with Australian experts.

PubMed

Parker, Lisa M; Rychetnik, Lucie; Carter, Stacy M

2015-10-19

One well-accepted strategy for optimising outcomes in mammographic breast cancer screening is to improve communication with women about screening. It is not always clear, however, what it is that communication should be expected to achieve, and why or how this is so. We investigated Australian experts' opinions on breast screening communication. Our research questions were: 1 What are the views of Australian experts about communicating with consumers on breast screening? 2 How do experts reason about this topic? We used a qualitative methodology, interviewing 33 breast screening experts across Australia with recognisable influence in the Australian mammographic breast cancer screening setting. We used purposive and theoretical sampling to identify experts from different professional roles (including clinicians, program managers, policy makers, advocates and researchers) with a range of opinions about communication in breast screening. Experts discussed the topic of communication with consumers by focusing on two main questions: how strongly to guide consumers' breast cancer screening choices, and what to communicate about overdiagnosis. Each expert adopted one of three approaches to consumer communication depending on their views about these topics. We labelled these approaches: Be screened; Be screened and here's why; Screening is available please consider whether it's right for you. There was a similar level of support for all three approaches. Experts' reasoning was grounded in how they conceived of and prioritised their underlying values including: delivering benefits, avoiding harms, delivering more benefits than harms, respecting autonomy and transparency. There is disagreement between experts regarding communication with breast screening consumers. Our study provides some insights into this persisting lack of consensus, highlighting the different meanings that experts give to values, and different ways that values are prioritised. We suggest that explicit discussion about ethical values might help to focus thinking, clarify concepts and promote consensus in policy around communication with consumers. More specifically, we suggest that decision-makers who are considering policy on screening communication should begin with identifying and agreeing on the specific values to be prioritised and use this to guide them in establishing what the communication aims will be and which communication strategy will achieve those aims.

[Can ICF core sets be helpful in preparing a social-medical expert report due to incapacity to work?--a first proposal].

PubMed

Kirschneck, M; Legner, R; Armbrust, W; Nowak, D; Cieza, A

2015-04-01

Social-medical expert reports from the German statutory pension insurance are essential for the German statutory pension regulatory authority to decide whether to grant services regarding participation as well as retirement pensions due to incapacity to work.The objective of this investigation is to determine whether the ICF Core Sets and other international approaches, such as the EUMASS Core Sets or ICF Core Set for vocational rehabilitation cover the content of the social-medical expert reports as well as to propose an approach how the ICF can be economically used by the social medicine practitioner when writing a social-medical expert report. A retrospective quantitative study design was used to translate a total of 294 social-medical expert reports from patients with low back pain (LBP) or chronic widespread pain (CWP) into the language of the ICF (linking) by 2 independent health professionals and compare the results with the ICF Core Sets for specific health conditions and other international approaches. The content of social-medical expert reports was largely reflected by the condition specific brief ICF Core Sets, brief ICF Core Sets for vocational rehabilitation and EUMASS Core Sets. The weighted Kappa statistic for the agreement between the 2 health professionals who translated the expert reports were in CWP 0.69 with a bootstrapped confidence interval of 0.67-0.71 and in LBP 0.73 (0.71-0.74). The analyses show that the content of social-medical expert reports varies enormously. A combination of a condition specific brief ICF Core Set as well as vocational rehabilitation and EUMASS ICF Core Sets as well as all ICF-categories from the expert reports that were named at least in 50% of it can largely provide a basis for preparing expert reports. Within the scope of implementation the need for a specific ICF Core Set for expert reports of the German statutory pension insurance should be further analyzed and discussed. © Georg Thieme Verlag KG Stuttgart · New York.

Knowledge acquisition and representation using fuzzy evidential reasoning and dynamic adaptive fuzzy Petri nets.

PubMed

Liu, Hu-Chen; Liu, Long; Lin, Qing-Lian; Liu, Nan

2013-06-01

The two most important issues of expert systems are the acquisition of domain experts' professional knowledge and the representation and reasoning of the knowledge rules that have been identified. First, during expert knowledge acquisition processes, the domain expert panel often demonstrates different experience and knowledge from one another and produces different types of knowledge information such as complete and incomplete, precise and imprecise, and known and unknown because of its cross-functional and multidisciplinary nature. Second, as a promising tool for knowledge representation and reasoning, fuzzy Petri nets (FPNs) still suffer a couple of deficiencies. The parameters in current FPN models could not accurately represent the increasingly complex knowledge-based systems, and the rules in most existing knowledge inference frameworks could not be dynamically adjustable according to propositions' variation as human cognition and thinking. In this paper, we present a knowledge acquisition and representation approach using the fuzzy evidential reasoning approach and dynamic adaptive FPNs to solve the problems mentioned above. As is illustrated by the numerical example, the proposed approach can well capture experts' diversity experience, enhance the knowledge representation power, and reason the rule-based knowledge more intelligently.

Supply chain value creation methodology under BSC approach

NASA Astrophysics Data System (ADS)

Golrizgashti, Seyedehfatemeh

2014-06-01

The objective of this paper is proposing a developed balanced scorecard approach to measure supply chain performance with the aim of creating more value in manufacturing and business operations. The most important metrics have been selected based on experts' opinion acquired by in-depth interviews focused on creating more value for stakeholders. Using factor analysis method, a survey research has been used to categorize selected metrics into balanced scorecard perspectives. The result identifies the intensity of correlation between perspectives and cause-and-effect chains among them using statistical method based on a real case study in home appliance manufacturing industries.

The effect of expert knowledge on medical search: medical experts have specialized abilities for detecting serious lesions.

PubMed

Nakashima, Ryoichi; Watanabe, Chisaki; Maeda, Eriko; Yoshikawa, Takeharu; Matsuda, Izuru; Miki, Soichiro; Yokosawa, Kazuhiko

2015-09-01

How does domain-specific knowledge influence the experts' performance in their domain of expertise? Specifically, can visual search experts find, with uniform efficiency, any type of target in their domain of expertise? We examined whether acquired knowledge of target importance influences an expert's visual search performance. In some professional searches (e.g., medical screenings), certain targets are rare; one aim of this study was to examine the extent to which experts miss such targets in their searches. In one experiment, radiologists (medical experts) engaged in a medical lesion search task in which both the importance (i.e., seriousness/gravity) and the prevalence of targets varied. Results showed decreased target detection rates in the low prevalence conditions (i.e., the prevalence effect). Also, experts were better at detecting important (versus unimportant) lesions. Results of an experiment using novices ruled out the possibility that decreased performance with unimportant targets was due to low target noticeability/visibility. Overall, the findings suggest that radiologists do not have a generalized ability to detect any type of lesion; instead, they have acquired a specialized ability to detect only those important lesions relevant for effective medical practices.

Different contributions of internal reviewers and external experts to labelling decisions on therapeutic indications in new drug reviews in Japan.

PubMed

Yokota, M; Kusama, M; Matsuki, N; Ono, S

2013-12-01

External experts play an important role in shaping regulatory decisions in the new drug review process in the United States, Europe and Japan. No rigorous study has been performed addressing how and to what extent external experts, in contrast to internal reviewers in the agency, influence the regulatory decisions during new drug reviews. We examined their contributions in Japanese regulatory reviews in contrast to the internal reviewers, focusing on the labelling decision on therapeutic indications. With the data set of 219 new molecular entities (NMEs) approved in Japan from 2000 to 2009, we observed how proposed indications in labelling were modified in a stepwise manner during the review process and conducted multinomial logistic analysis to examine the possible mechanism behind. We found that interim assessment of indications by the internal reviewers was modified substantially by the influence of the external experts in about 20% of the 219 NMEs. Our analysis suggested that internal reviewers provided their opinion mainly based on strict review discipline, whereas external experts added flexibility and reality to their reviews. Our analysis revealed different evaluations between internal reviewers and external experts during regulatory discussions in new drug reviews and how the external panel contributes to changing internal decisions. This study provides a new and quantitative approach to better label setting by emphasizing the contributions of each stakeholder in new drug reviews, which would improve the efficiency, quality and transparency of new drug reviews to enhance public health. © 2013 John Wiley & Sons Ltd.

Artificial intelligence techniques for ground test monitoring of rocket engines

NASA Technical Reports Server (NTRS)

Ali, Moonis; Gupta, U. K.

1990-01-01

An expert system is being developed which can detect anomalies in Space Shuttle Main Engine (SSME) sensor data significantly earlier than the redline algorithm currently in use. The training of such an expert system focuses on two approaches which are based on low frequency and high frequency analyses of sensor data. Both approaches are being tested on data from SSME tests and their results compared with the findings of NASA and Rocketdyne experts. Prototype implementations have detected the presence of anomalies earlier than the redline algorithms that are in use currently. It therefore appears that these approaches have the potential of detecting anomalies early eneough to shut down the engine or take other corrective action before severe damage to the engine occurs.

Differences in spatial working memory as a function of team sports expertise: the Corsi Block-tapping task in sport psychological assessment.

PubMed

Furley, Philip; Memmert, Daniel

2010-06-01

Individual differences in visuospatial abilities were investigated in experienced basketball players compared with nonathletes. Most research shows that experts and novices do not differ on basic cognitive ability tests. Nevertheless, there are some equivocal findings indicating there are differences in basic cognitive abilities such as attention. The goal of the present research was to investigate team-ball athletes in regard to their visuospatial abilities. 112 male college students (54 basketball players, 58 nonathlete college students) were tested in their spatial capacity with the Corsi Block-tapping Task. No differences in spatial capacity were evident between basketball players and nonathlete college students. The results are discussed in the context of the expert performance approach and individual difference research.

Development, Construction, and Content Validation of a Questionnaire to Test Mobile Shower Commode Usability

PubMed Central

Theodoros, Deborah G.; Russell, Trevor G.

2015-01-01

Background: Usability is an emerging domain of outcomes measurement in assistive technology provision. Currently, no questionnaires exist to test the usability of mobile shower commodes (MSCs) used by adults with spinal cord injury (SCI). Objective: To describe the development, construction, and initial content validation of an electronic questionnaire to test mobile shower commode usability for this population. Methods: The questionnaire was constructed using a mixed-methods approach in 5 phases: determining user preferences for the questionnaire’s format, developing an item bank of usability indicators from the literature and judgement of experts, constructing a preliminary questionnaire, assessing content validity with a panel of experts, and constructing the final questionnaire. Results: The electronic Mobile Shower Commode Assessment Tool Version 1.0 (eMAST 1.0) questionnaire tests MSC features and performance during activities identified using a mixed-methods approach and in consultation with users. It confirms that usability is complex and multidimensional. The final questionnaire contains 25 questions in 3 sections. The eMAST 1.0 demonstrates excellent content validity as determined by a small sample of expert clinicians. Conclusion: The eMAST 1.0 tests usability of MSCs from the perspective of adults with SCI and may be used to solicit feedback during MSC design, assessment, prescription, and ongoing use. Further studies assessing the eMAST’s psychometric properties, including studies with users of MSCs, are needed. PMID:25762862

A prototype knowledge-based simulation support system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hill, T.R.; Roberts, S.D.

1987-04-01

As a preliminary step toward the goal of an intelligent automated system for simulation modeling support, we explore the feasibility of the overall concept by generating and testing a prototypical framework. A prototype knowledge-based computer system was developed to support a senior level course in industrial engineering so that the overall feasibility of an expert simulation support system could be studied in a controlled and observable setting. The system behavior mimics the diagnostic (intelligent) process performed by the course instructor and teaching assistants, finding logical errors in INSIGHT simulation models and recommending appropriate corrective measures. The system was programmed inmore » a non-procedural language (PROLOG) and designed to run interactively with students working on course homework and projects. The knowledge-based structure supports intelligent behavior, providing its users with access to an evolving accumulation of expert diagnostic knowledge. The non-procedural approach facilitates the maintenance of the system and helps merge the roles of expert and knowledge engineer by allowing new knowledge to be easily incorporated without regard to the existing flow of control. The background, features and design of the system are describe and preliminary results are reported. Initial success is judged to demonstrate the utility of the reported approach and support the ultimate goal of an intelligent modeling system which can support simulation modelers outside the classroom environment. Finally, future extensions are suggested.« less

Bayesian assessment of overtriage and undertriage at a level I trauma centre.

PubMed

DiDomenico, Paul B; Pietzsch, Jan B; Paté-Cornell, M Elisabeth

2008-07-13

We analysed the trauma triage system at a specific level I trauma centre to assess rates of over- and undertriage and to support recommendations for system improvements. The triage process is designed to estimate the severity of patient injury and allocate resources accordingly, with potential errors of overestimation (overtriage) consuming excess resources and underestimation (undertriage) potentially leading to medical errors.We first modelled the overall trauma system using risk analysis methods to understand interdependencies among the actions of the participants. We interviewed six experienced trauma surgeons to obtain their expert opinion of the over- and undertriage rates occurring in the trauma centre. We then assessed actual over- and undertriage rates in a random sample of 86 trauma cases collected over a six-week period at the same centre. We employed Bayesian analysis to quantitatively combine the data with the prior probabilities derived from expert opinion in order to obtain posterior distributions. The results were estimates of overtriage and undertriage in 16.1 and 4.9% of patients, respectively. This Bayesian approach, which provides a quantitative assessment of the error rates using both case data and expert opinion, provides a rational means of obtaining a best estimate of the system's performance. The overall approach that we describe in this paper can be employed more widely to analyse complex health care delivery systems, with the objective of reduced errors, patient risk and excess costs.

Utilization of Expert Knowledge in a Multi-Objective Hydrologic Model Automatic Calibration Process

NASA Astrophysics Data System (ADS)

Quebbeman, J.; Park, G. H.; Carney, S.; Day, G. N.; Micheletty, P. D.

2016-12-01

Spatially distributed continuous simulation hydrologic models have a large number of parameters for potential adjustment during the calibration process. Traditional manual calibration approaches of such a modeling system is extremely laborious, which has historically motivated the use of automatic calibration procedures. With a large selection of model parameters, achieving high degrees of objective space fitness - measured with typical metrics such as Nash-Sutcliffe, Kling-Gupta, RMSE, etc. - can easily be achieved using a range of evolutionary algorithms. A concern with this approach is the high degree of compensatory calibration, with many similarly performing solutions, and yet grossly varying parameter set solutions. To help alleviate this concern, and mimic manual calibration processes, expert knowledge is proposed for inclusion within the multi-objective functions, which evaluates the parameter decision space. As a result, Pareto solutions are identified with high degrees of fitness, but also create parameter sets that maintain and utilize available expert knowledge resulting in more realistic and consistent solutions. This process was tested using the joint SNOW-17 and Sacramento Soil Moisture Accounting method (SAC-SMA) within the Animas River basin in Colorado. Three different elevation zones, each with a range of parameters, resulted in over 35 model parameters simultaneously calibrated. As a result, high degrees of fitness were achieved, in addition to the development of more realistic and consistent parameter sets such as those typically achieved during manual calibration procedures.

Mechanisms and Neural Basis of Object and Pattern Recognition: A Study with Chess Experts

ERIC Educational Resources Information Center

Bilalic, Merim; Langner, Robert; Erb, Michael; Grodd, Wolfgang

2010-01-01

Comparing experts with novices offers unique insights into the functioning of cognition, based on the maximization of individual differences. Here we used this expertise approach to disentangle the mechanisms and neural basis behind two processes that contribute to everyday expertise: object and pattern recognition. We compared chess experts and…

Development of Interactive Multimedia Courseware (e-CRAFT) for Craft Education

ERIC Educational Resources Information Center

Osman, Salyani; Sahari, Noraidah; Zin, Nor Azan Mat

2012-01-01

The way of teaching and learning traditional crafts have always used traditional apprenticeship learning methods where the expert facilitates transfer of practice skill sets to novices. As a craft has been taught in conventional approach, the students and experts have been facing several problems especially when expert needs to teach a group of…

Evaluation of SAPHIRE: an automated approach to indexing and retrieving medical literature.

PubMed Central

Hersh, W.; Hickam, D. H.; Haynes, R. B.; McKibbon, K. A.

1991-01-01

An analysis of SAPHIRE, an experimental information retrieval system featuring automated indexing and natural language retrieval, was performed on MEDLINE references using data previously generated for a MEDLINE evaluation. Compared with searches performed by novice and expert physicians using MEDLINE, SAPHIRE achieved comparable recall and precision. While its combined recall and precision performance did not equal the level of librarians, SAPHIRE did achieve a significantly higher level of absolute recall. SAPHIRE has other potential advantages over existing MEDLINE systems. Its natural language interface does not require knowledge of MeSH, and it provides relevance ranking of retrieved references. PMID:1807718

Hysteroscopic sterilization using a virtual reality simulator: assessment of learning curve.

PubMed

Janse, Juliënne A; Goedegebuure, Ruben S A; Veersema, Sebastiaan; Broekmans, Frank J M; Schreuder, Henk W R

2013-01-01

To assess the learning curve using a virtual reality simulator for hysteroscopic sterilization with the Essure method. Prospective multicenter study (Canadian Task Force classification II-2). University and teaching hospital in the Netherlands. Thirty novices (medical students) and five experts (gynecologists who had performed >150 Essure sterilization procedures). All participants performed nine repetitions of bilateral Essure placement on the simulator. Novices returned after 2 weeks and performed a second series of five repetitions to assess retention of skills. Structured observations on performance using the Global Rating Scale and parameters derived from the simulator provided measurements for analysis. The learning curve is represented by improvement per procedure. Two-way repeated-measures analysis of variance was used to analyze learning curves. Effect size (ES) was calculated to express the practical significance of the results (ES ≥ 0.50 indicates a large learning effect). For all parameters, significant improvements were found in novice performance within nine repetitions. Large learning effects were established for six of eight parameters (p < .001; ES, 0.50-0.96). Novices approached expert level within 9 to 14 repetitions. The learning curve established in this study endorses future implementation of the simulator in curricula on hysteroscopic skill acquisition for clinicians who are interested in learning this sterilization technique. Copyright © 2013 AAGL. Published by Elsevier Inc. All rights reserved.

The Nature of Expertise in Fingerprint Matching: Experts Can Do a Lot with a Little

PubMed Central

Thompson, Matthew B.; Tangen, Jason M.

2014-01-01

Expert decision making often seems impressive, even miraculous. People with genuine expertise in a particular domain can perform quickly and accurately, and with little information. In the series of experiments presented here, we manipulate the amount of “information” available to a group of experts whose job it is to identify the source of crime scene fingerprints. In Experiment 1, we reduced the amount of information available to experts by inverting fingerprint pairs and adding visual noise. There was no evidence for an inversion effect—experts were just as accurate for inverted prints as they were for upright prints—but expert performance with artificially noisy prints was impressive. In Experiment 2, we separated matching and nonmatching print pairs in time. Experts were conservative, but they were still able to discriminate pairs of fingerprints that were separated by five-seconds, even though the task was quite different from their everyday experience. In Experiment 3, we separated the print pairs further in time to test the long-term memory of experts compared to novices. Long-term recognition memory for experts and novices was the same, with both performing around chance. In Experiment 4, we presented pairs of fingerprints quickly to experts and novices in a matching task. Experts were more accurate than novices, particularly for similar nonmatching pairs, and experts were generally more accurate when they had more time. It is clear that experts can match prints accurately when there is reduced visual information, reduced opportunity for direct comparison, and reduced time to engage in deliberate reasoning. These findings suggest that non-analytic processing accounts for a substantial portion of the variance in expert fingerprint matching accuracy. Our conclusion is at odds with general wisdom in fingerprint identification practice and formal training, and at odds with the claims and explanations that are offered in court during expert testimony. PMID:25517509

An application of object-oriented knowledge representation to engineering expert systems

NASA Technical Reports Server (NTRS)

Logie, D. S.; Kamil, H.; Umaretiya, J. R.

1990-01-01

The paper describes an object-oriented knowledge representation and its application to engineering expert systems. The object-oriented approach promotes efficient handling of the problem data by allowing knowledge to be encapsulated in objects and organized by defining relationships between the objects. An Object Representation Language (ORL) was implemented as a tool for building and manipulating the object base. Rule-based knowledge representation is then used to simulate engineering design reasoning. Using a common object base, very large expert systems can be developed, comprised of small, individually processed, rule sets. The integration of these two schemes makes it easier to develop practical engineering expert systems. The general approach to applying this technology to the domain of the finite element analysis, design, and optimization of aerospace structures is discussed.

[Revoking the driver's license, assessment of driver competence, re-education and therapy in alcohol offenses: approaches to a necessary reorientation].

PubMed

Müller, A

1993-03-01

After representing figures and facts about drinking behaviour and drinking-driving offences of the 45 mio. drivers in Germany counter-measures for DWI-offenders are dealt with. The proceedings practised so far are critically analysed and new approaches are discussed. Primarily reviewed are special preventive measures and here particularly the selection procedure usually applied in Germany, in which on demand of the driver licensing authorities the question is to be answered by expert opinions whether there is to be reckoned with driving under the influence in die future, too. It is pointed out that the expert opinions are not valid enough for making a distinct decision in an individual case. Validity coefficients obtained in evaluation studies lie between phi = 0 and .28, raising the expectancy value of 50% resulting by chance at best only on to 65%. Moreover the psychological interview is understandably burdened with faked answers of the explored person, in several cases increased by a specific, professionally managed preparation for the testing situation. As a way out of the dilemma expert opinions prognosticating driver behaviour should be waived in favour of making diagnoses and proposals for treatment. The timing of regranting the driver license is then only determined by the revocation period pronounced by the court and by the performance of the measures recommended by the experts and demanded by the licensing authority. As a support for such a procedure a more consistent and resolute administration of the judicial revocation of driver license with longer periods for repeaters should be provided. Besides there is necessary a wider range of rehabilitation programs for drivers with alcoholic problems.

Towards an Online Seizure Advisory System-An Adaptive Seizure Prediction Framework Using Active Learning Heuristics.

PubMed

Karuppiah Ramachandran, Vignesh Raja; Alblas, Huibert J; Le, Duc V; Meratnia, Nirvana

2018-05-24

In the last decade, seizure prediction systems have gained a lot of attention because of their enormous potential to largely improve the quality-of-life of the epileptic patients. The accuracy of the prediction algorithms to detect seizure in real-world applications is largely limited because the brain signals are inherently uncertain and affected by various factors, such as environment, age, drug intake, etc., in addition to the internal artefacts that occur during the process of recording the brain signals. To deal with such ambiguity, researchers transitionally use active learning, which selects the ambiguous data to be annotated by an expert and updates the classification model dynamically. However, selecting the particular data from a pool of large ambiguous datasets to be labelled by an expert is still a challenging problem. In this paper, we propose an active learning-based prediction framework that aims to improve the accuracy of the prediction with a minimum number of labelled data. The core technique of our framework is employing the Bernoulli-Gaussian Mixture model (BGMM) to determine the feature samples that have the most ambiguity to be annotated by an expert. By doing so, our approach facilitates expert intervention as well as increasing medical reliability. We evaluate seven different classifiers in terms of the classification time and memory required. An active learning framework built on top of the best performing classifier is evaluated in terms of required annotation effort to achieve a high level of prediction accuracy. The results show that our approach can achieve the same accuracy as a Support Vector Machine (SVM) classifier using only 20 % of the labelled data and also improve the prediction accuracy even under the noisy condition.

Video Modeling by Experts with Video Feedback to Enhance Gymnastics Skills

ERIC Educational Resources Information Center

Boyer, Eva; Miltenberger, Raymond G.; Batsche, Catherine; Fogel, Victoria

2009-01-01

The effects of combining video modeling by experts with video feedback were analyzed with 4 female competitive gymnasts (7 to 10 years old) in a multiple baseline design across behaviors. During the intervention, after the gymnast performed a specific gymnastics skill, she viewed a video segment showing an expert gymnast performing the same skill…

Distributed Cognition in Sports Teams: Explaining Successful and Expert Performance

ERIC Educational Resources Information Center

Williamson, Kellie; Cox, Rochelle

2014-01-01

In this article we use a hybrid methodology to better understand the skilful performance of sports teams as an exemplar of distributed cognition. We highlight key differences between a team of individual experts (an aggregate system) and an expert team (an emergent system), and outline the kinds of shared characteristics likely to be found in an…

Establishment of a Quantitative Medical Technology Evaluation System and Indicators within Medical Institutions.

PubMed

Wu, Suo-Wei; Chen, Tong; Pan, Qi; Wei, Liang-Yu; Wang, Qin; Li, Chao; Song, Jing-Chen; Luo, Ji

2018-06-05

The development and application of medical technologies reflect the medical quality and clinical capacity of a hospital. It is also an effective approach in upgrading medical service and core competitiveness among medical institutions. This study aimed to build a quantitative medical technology evaluation system through questionnaire survey within medical institutions to perform an assessment to medical technologies more objectively and accurately, and promote the management of medical quality technologies and ensure the medical safety of various operations among the hospitals. A two-leveled quantitative medical technology evaluation system was built through a two-round questionnaire survey of chosen experts. The Delphi method was applied in identifying the structure of evaluation system and indicators. The judgment of the experts on the indicators was adopted in building the matrix so that the weight coefficient and maximum eigenvalue (λ max), consistency index (CI), and random consistency ratio (CR) could be obtained and collected. The results were verified through consistency tests, and the index weight coefficient of each indicator was conducted and calculated through analytical hierarchy process. Twenty-six experts of different medical fields were involved in the questionnaire survey, 25 of whom successfully responded to the two-round research. Altogether, 4 primary indicators (safety, effectiveness, innovativeness, and benefits), as well as 13 secondary indicators, were included in the evaluation system. The matrix is built to conduct the λ max, CI, and CR of each expert in the survey, and the index weight coefficients of primary indicators were 0.33, 0.28, 0.27, and 0.12, respectively, and the index weight coefficients of secondary indicators were conducted and calculated accordingly. As the two-round questionnaire survey of experts and statistical analysis were performed and credibility of the results was verified through consistency evaluation test, the study established a quantitative medical technology evaluation system model and assessment indicators within medical institutions based on the Delphi method and analytical hierarchy process. Moreover, further verifications, adjustments, and optimizations of the system and indicators will be performed in follow-up studies.

Mantle of the Expert: Integrating Dramatic Inquiry and Visual Arts in Social Studies

ERIC Educational Resources Information Center

Johnson, Edric C.; Liu, Katrina; Goble, Kristin

2015-01-01

This article introduces the social studies field to Dorothy Heatchote's Mantle of Expert (MOE). MOE is a dramatic inquiry approach used in several subject areas and can work at all levels in the social studies curriculum. The authors go into the development of using this approach in an elementary and middle teacher education program. After sharing…

Volunteer Expert Readers: Drawing on the University Community to Provide Professional Feedback for Engineering Student Writers

ERIC Educational Resources Information Center

Moskovitz, Cary

2017-01-01

This paper reports on a 3-year study utilizing a novel approach to providing students in an introductory engineering course with feedback on drafts of course writing projects. In the Volunteer Expert Reader (VER) approach, students are matched with university alumni or employees who have the background to give feedback from the perspective of the…

A Symbolic Approach Using Feature Construction Capable of Acquiring Information/Knowledge for Building Expert Systems.

ERIC Educational Resources Information Center

Major, Raymond L.

1998-01-01

Presents a technique for developing a knowledge-base of information to use in an expert system. Proposed approach employs a popular machine-learning algorithm along with a method for forming a finite number of features or conjuncts of at most n primitive attributes. Illustrates this procedure by examining qualitative information represented in a…

Investigating Cognitive Task Difficulties and Expert Skills in E-Learning Storyboards Using a Cognitive Task Analysis Technique

ERIC Educational Resources Information Center

Yusoff, Nor'ain Mohd; Salim, Siti Salwah

2012-01-01

E-learning storyboards have been a useful approach in distance learning development to support interaction between instructional designers and subject-matter experts. Current works show that researchers are focusing on different approaches for use in storyboards, and there is less emphasis on the effect of design and process difficulties faced by…

An Alternative Expert Knowledge Transfer Model: A Case Study of an Indigenous Storytelling Approach

ERIC Educational Resources Information Center

Spaulding, Roderick Jay

2010-01-01

The increasing complexity of technical work, the demand for highly skilled workers, and the vital challenges facing the world at large have combined to create a need for better ways to transfer knowledge, especially expert knowledge. In this dissertation, I attempted to see if an approach to this process that is more holistic than is typical in…

Symbolic Knowledge Processing for the Acquisition of Expert Behavior: A Study in Medicine.

DTIC Science & Technology

1984-05-01

information . It provides a model for this type of study, suggesting a different approach to the problem of learning and efficiency of knowledge -based...flow of information 2.2. Scope and description of the subsystems Three subsystems perform distinct operations using the preceding knowledge sources...which actually yields a new knowledge rCpresentation Ahere new external information is encoded in the combination and ordering of elements of the

Evaluation models of some morphological characteristics for talent scouting in sport.

PubMed

Rogulj, Nenad; Papić, Vladan; Cavala, Marijana

2009-03-01

In this paper, for the purpose of expert system evaluation within the scientific project "Talent scouting in sport", two methodological approaches for recognizing an athlete's morphological compatibility for various sports has been presented, evaluated and compared. First approach is based on the fuzzy logic and expert opinion about compatibility of proposed hypothetical morphological models for 14 different sports which are part of the expert system. Second approach is based on determining the differences between morphological characteristics of a tested individual and top athlete's morphological characteristics for particular sport. Logical and mathematical bases of both methodological approaches have been explained in detail. High prognostic efficiency in recognition of individual's sport has been determined. Some improvements in further development of both methods have been proposed. Results of the research so far suggest that this or similar approaches can be successfully used for detection of individual's morphological compatibility for different sports. Also, it is expected to be useful in the selection of young talents for particular sport.

The Viewpoints of Students and Evaluation Experts About Performance Processes of Faculty Member Evaluation at Mazandaran University of Medical Sciences, 2014

PubMed Central

Ghahrani, Nassim; Balaghafari, Azita; Aligolbandi, Kobra; Vahedi, Mohammad; Siamian, Hasan

2015-01-01

Background and purpose: One of the most common ways used in most of the countries and Iran to determine the status of teacher training is the evaluation by students. The most common method of evaluation is the survey questionnaire, the content of a number of questions about educational activities provided to the students. The researchers plan to evaluate the students’ and experts’ performances at Mazandaran University of Medical Sciences on the process of evaluating the performance of teachers, they examined in 2014. Materials and methods: This study surveys the students and experts in the evaluation of faculty members’ performance process. The study subjects were 3904 students and 37 evaluation expert of Mazandaran University of Medical Sciences. Using Cochran sampling formula of 350 students through proportional stratified random sampling were selected. The experts’ viewpoint, method was used. Data collection tools consisted of 14 questions with answers Yes, or, I don’t know. Descriptive Statistical analysis of the data and chi-square test was performed. Results: From total of 350 students, 346 and the entire 37 evaluations expert participated in this study. Most of the students, 80 (23.12%) and the largest number of experts, 8 (21.62%) were from Sari Allied Medical Sciences Faculty. Most of the demographic information about gender were, 255 female students (74.56%) and 29 female experts (78.37%). In most age groups of students, 188 (55.62 percent) were in the category of 18 to 20 years, and the experts, 19 (51.35%) were in the category of 22 and 31 years. Most students, 232 of them (70.95%) were in semester 2 and 4. Most experts, 20 (54.05 percent) were under 10 years of work experience. The comparison between the views of students and experts in the evaluation process between the schools of Mazandaran University of Medical Sciences, Sari School of Nursing and Midwifery, there was difference between the opinions of experts and students (p-value=0.01. It showed 86.7% student and 33.3% of experts is satisfied with the evaluation process. Conclusion: on comparison of students and experts viewpoints on the implementation of the evaluation process, it is noteworthy that among students of different opinions on how the evaluation process. It worth to mention that there is insignificant difference between their viewpoints and majority of students and evaluation experts with the evaluation the process. In addition, the experts evaluated at different schools, most of them are satisfied the process. PMID:26236169

VIDEO MODELING BY EXPERTS WITH VIDEO FEEDBACK TO ENHANCE GYMNASTICS SKILLS

PubMed Central

Boyer, Eva; Miltenberger, Raymond G; Batsche, Catherine; Fogel, Victoria

2009-01-01

The effects of combining video modeling by experts with video feedback were analyzed with 4 female competitive gymnasts (7 to 10 years old) in a multiple baseline design across behaviors. During the intervention, after the gymnast performed a specific gymnastics skill, she viewed a video segment showing an expert gymnast performing the same skill and then viewed a video replay of her own performance of the skill. The results showed that all gymnasts demonstrated improved performance across three gymnastics skills following exposure to the intervention. PMID:20514194

Video modeling by experts with video feedback to enhance gymnastics skills.

PubMed

Boyer, Eva; Miltenberger, Raymond G; Batsche, Catherine; Fogel, Victoria

2009-01-01

The effects of combining video modeling by experts with video feedback were analyzed with 4 female competitive gymnasts (7 to 10 years old) in a multiple baseline design across behaviors. During the intervention, after the gymnast performed a specific gymnastics skill, she viewed a video segment showing an expert gymnast performing the same skill and then viewed a video replay of her own performance of the skill. The results showed that all gymnasts demonstrated improved performance across three gymnastics skills following exposure to the intervention.

Text mining and natural language processing approaches for automatic categorization of lay requests to web-based expert forums.

PubMed

Himmel, Wolfgang; Reincke, Ulrich; Michelmann, Hans Wilhelm

2009-07-22

Both healthy and sick people increasingly use electronic media to obtain medical information and advice. For example, Internet users may send requests to Web-based expert forums, or so-called "ask the doctor" services. To automatically classify lay requests to an Internet medical expert forum using a combination of different text-mining strategies. We first manually classified a sample of 988 requests directed to a involuntary childlessness forum on the German website "Rund ums Baby" ("Everything about Babies") into one or more of 38 categories belonging to two dimensions ("subject matter" and "expectations"). After creating start and synonym lists, we calculated the average Cramer's V statistic for the association of each word with each category. We also used principle component analysis and singular value decomposition as further text-mining strategies. With these measures we trained regression models and determined, on the basis of best regression models, for any request the probability of belonging to each of the 38 different categories, with a cutoff of 50%. Recall and precision of a test sample were calculated as a measure of quality for the automatic classification. According to the manual classification of 988 documents, 102 (10%) documents fell into the category "in vitro fertilization (IVF)," 81 (8%) into the category "ovulation," 79 (8%) into "cycle," and 57 (6%) into "semen analysis." These were the four most frequent categories in the subject matter dimension (consisting of 32 categories). The expectation dimension comprised six categories; we classified 533 documents (54%) as "general information" and 351 (36%) as a wish for "treatment recommendations." The generation of indicator variables based on the chi-square analysis and Cramer's V proved to be the best approach for automatic classification in about half of the categories. In combination with the two other approaches, 100% precision and 100% recall were realized in 18 (47%) out of the 38 categories in the test sample. For 35 (92%) categories, precision and recall were better than 80%. For some categories, the input variables (ie, "words") also included variables from other categories, most often with a negative sign. For example, absence of words predictive for "menstruation" was a strong indicator for the category "pregnancy test." Our approach suggests a way of automatically classifying and analyzing unstructured information in Internet expert forums. The technique can perform a preliminary categorization of new requests and help Internet medical experts to better handle the mass of information and to give professional feedback.

Multiple neural network approaches to clinical expert systems

NASA Astrophysics Data System (ADS)

Stubbs, Derek F.

1990-08-01

We briefly review the concept of computer aided medical diagnosis and more extensively review the the existing literature on neural network applications in the field. Neural networks can function as simple expert systems for diagnosis or prognosis. Using a public database we develop a neural network for the diagnosis of a major presenting symptom while discussing the development process and possible approaches. MEDICAL EXPERTS SYSTEMS COMPUTER AIDED DIAGNOSIS Biomedicine is an incredibly diverse and multidisciplinary field and it is not surprising that neural networks with their many applications are finding more and more applications in the highly non-linear field of biomedicine. I want to concentrate on neural networks as medical expert systems for clinical diagnosis or prognosis. Expert Systems started out as a set of computerized " ifthen" rules. Everything was reduced to boolean logic and the promised land of computer experts was said to be in sight. It never came. Why? First the computer code explodes as the number of " ifs" increases. All the " ifs" have to interact. Second experts are not very good at reducing expertise to language. It turns out that experts recognize patterns and have non-verbal left-brain intuition decision processes. Third learning by example rather than learning by rule is the way natural brains works and making computers work by rule-learning is hideously labor intensive. Neural networks can learn from example. They learn the results

The Effect of Moderate and High-Intensity Fatigue on Groundstroke Accuracy in Expert and Non-Expert Tennis Players

PubMed Central

Lyons, Mark; Al-Nakeeb, Yahya; Hankey, Joanne; Nevill, Alan

2013-01-01

Exploring the effects of fatigue on skilled performance in tennis presents a significant challenge to the researcher with respect to ecological validity. This study examined the effects of moderate and high-intensity fatigue on groundstroke accuracy in expert and non-expert tennis players. The research also explored whether the effects of fatigue are the same regardless of gender and player’s achievement motivation characteristics. 13 expert (7 male, 6 female) and 17 non-expert (13 male, 4 female) tennis players participated in the study. Groundstroke accuracy was assessed using the modified Loughborough Tennis Skills Test. Fatigue was induced using the Loughborough Intermittent Tennis Test with moderate (70%) and high-intensities (90%) set as a percentage of peak heart rate (attained during a tennis-specific maximal hitting sprint test). Ratings of perceived exertion were used as an adjunct to the monitoring of heart rate. Achievement goal indicators for each player were assessed using the 2 x 2 Achievement Goals Questionnaire for Sport in an effort to examine if this personality characteristic provides insight into how players perform under moderate and high-intensity fatigue conditions. A series of mixed ANOVA’s revealed significant fatigue effects on groundstroke accuracy regardless of expertise. The expert players however, maintained better groundstroke accuracy across all conditions compared to the novice players. Nevertheless, in both groups, performance following high-intensity fatigue deteriorated compared to performance at rest and performance while moderately fatigued. Groundstroke accuracy under moderate levels of fatigue was equivalent to that at rest. Fatigue effects were also similar regardless of gender. No fatigue by expertise, or fatigue by gender interactions were found. Fatigue effects were also equivalent regardless of player’s achievement goal indicators. Future research is required to explore the effects of fatigue on performance in tennis using ecologically valid designs that mimic more closely the demands of match play. Key Points Groundstroke accuracy under moderate-intensity fatigue is equivalent to performance at rest. Groundstroke accuracy declines significantly in both expert (40.3% decline) and non-expert (49.6%) tennis players following high-intensity fatigue. Expert players are more consistent, hit more accurate shots and fewer out shots across all fatigue intensities. The effects of fatigue on groundstroke accuracy are the same regardless of gender and player’s achievement goal indicators. PMID:24149809

Expert Systems for Libraries at SCIL [Small Computers in Libraries]'88.

ERIC Educational Resources Information Center

Kochtanek, Thomas R.; And Others

1988-01-01

Six brief papers on expert systems for libraries cover (1) a knowledge-based approach to database design; (2) getting started in expert systems; (3) using public domain software to develop a business reference system; (4) a music cataloging inquiry system; (5) linguistic analysis of reference transactions; and (6) a model of a reference librarian.…

Teaching Beginning Dance Classes in Higher Education: Learning to Teach from an Expert Dance Educator

ERIC Educational Resources Information Center

You, JeongAe

2009-01-01

This qualitative case study examines the exemplary teaching approaches of an expert Korean dance educator who has been teaching beginning dance classes in higher education. The expert dance educator, possesses 28 years of teaching experience in higher education, is the recipient of a national award, is actively involved in professional activities,…

A Computer-Based, Interactive Videodisc Job Aid and Expert System for Electron Beam Lithography Integration and Diagnostic Procedures.

ERIC Educational Resources Information Center

Stevenson, Kimberly

This master's thesis describes the development of an expert system and interactive videodisc computer-based instructional job aid used for assisting in the integration of electron beam lithography devices. Comparable to all comprehensive training, expert system and job aid development require a criterion-referenced systems approach treatment to…

Expert Approaches to Analysis

DTIC Science & Technology

1999-03-01

of epistemic forms and games , which can form the basis for building a tool to support expert analyses. 15. SUBJECT TERMS Expert analysis Epistemic...forms Epistemic games SECURITY CLASSIFICATION OF 16. REPORT Unclassified 17. ABSTRACT Unclassified 18. THIS PAGE Unclassified 19. LIMITATION OF...1998 Principal Investigators: Allan Collins & William Ferguson BBN Technologies Introduction 1 Prior Work 2 Structural-Analysis Games 2 Functional

A 16th Suggestions for Educational Curriculum Improvement in Jordan, from the Experts Point of View

ERIC Educational Resources Information Center

Mahasneh, Omar

2015-01-01

The present research was conducted to identify the most important suggestions for educational curriculum improvement in Jordan, from the expert's point of view. A descriptive survey through data and information collection tool (questionnaire) was used as an approach. The study sample consisted of (620) educational experts in the field of…

Keeping an Eye on Noisy Movements: On Different Approaches to Perceptual-Motor Skill Research and Training.

PubMed

Dicks, Matt; Button, Chris; Davids, Keith; Chow, Jia Yi; van der Kamp, John

2017-04-01

Contemporary theorizing on the complementary nature of perception and action in expert performance has led to different emphases in the study of movement coordination and gaze behavior. On the one hand, coordination research has examined the role of variability in movement control, evidencing that variability facilitates individualized adaptations during both learning and performance. On the other hand, and at odds with this principle, the majority of gaze behavior studies have tended to average data over participants and trials, proposing the importance of universal 'optimal' gaze patterns in a given task, for all performers, irrespective of stage of learning. In this article, we discuss new lines of inquiry with the aim of reconciling these two distinct approaches. We consider the role of inter- and intra-individual variability in gaze behaviors and suggest directions for future research.

Developing a systematic approach to safer medication use during pregnancy: summary of a Centers for Disease Control and Prevention--convened meeting.

PubMed

Broussard, Cheryl S; Frey, Meghan T; Hernandez-Diaz, Sonia; Greene, Michael F; Chambers, Christina D; Sahin, Leyla; Collins Sharp, Beth A; Honein, Margaret A

2014-09-01

To address information gaps that limit informed clinical decisions on medication use in pregnancy, the Centers for Disease Control and Prevention (CDC) solicited expert input on a draft prototype outlining a systematic approach to evaluating the quality and strength of existing evidence for associated risks. The draft prototype outlined a process for the systematic review of available evidence and deliberations by a panel of experts to inform clinical decision making for managing health conditions in pregnancy. At an expert meeting convened by the CDC in January 2013, participants divided into working groups discussed decision points within the prototype. This report summarizes their discussions of best practices for formulating an expert review process, developing evidence summaries and treatment guidance, and disseminating information. There is clear recognition of current knowledge gaps and a strong collaboration of federal partners, academic experts, and professional organizations willing to work together toward safer medication use during pregnancy. Published by Elsevier Inc.

Developing a systematic approach to safer medication use during pregnancy: summary of a Centers for Disease Control and Prevention—convened meeting

PubMed Central

Broussard, Cheryl S.; Frey, Meghan T.; Hernandez-Diaz, Sonia; Greene, Michael F.; Chambers, Christina D.; Sahin, Leyla; Collins Sharp, Beth A.; Honein, Margaret A.

2015-01-01

To address information gaps that limit informed clinical decisions on medication use in pregnancy, the Centers for Disease Control and Prevention (CDC) solicited expert input on a draft prototype outlining a systematic approach to evaluating the quality and strength of existing evidence for associated risks. The draft prototype outlined a process for the systematic review of available evidence and deliberations by a panel of experts to inform clinical decision making for managing health conditions in pregnancy. At an expert meeting convened by the CDC in January 2013, participants divided into working groups discussed decision points within the prototype. This report summarizes their discussions of best practices for formulating an expert review process, developing evidence summaries and treatment guidance, and disseminating information. There is clear recognition of current knowledge gaps and a strong collaboration of federal partners, academic experts, and professional organizations willing to work together toward safer medication use during pregnancy. PMID:24881821

Using expert judgments to explore robust alternatives for forest management under climate change.

PubMed

McDaniels, Timothy; Mills, Tamsin; Gregory, Robin; Ohlson, Dan

2012-12-01

We develop and apply a judgment-based approach to selecting robust alternatives, which are defined here as reasonably likely to achieve objectives, over a range of uncertainties. The intent is to develop an approach that is more practical in terms of data and analysis requirements than current approaches, informed by the literature and experience with probability elicitation and judgmental forecasting. The context involves decisions about managing forest lands that have been severely affected by mountain pine beetles in British Columbia, a pest infestation that is climate-exacerbated. A forest management decision was developed as the basis for the context, objectives, and alternatives for land management actions, to frame and condition the judgments. A wide range of climate forecasts, taken to represent the 10-90% levels on cumulative distributions for future climate, were developed to condition judgments. An elicitation instrument was developed, tested, and revised to serve as the basis for eliciting probabilistic three-point distributions regarding the performance of selected alternatives, over a set of relevant objectives, in the short and long term. The elicitations were conducted in a workshop comprising 14 regional forest management specialists. We employed the concept of stochastic dominance to help identify robust alternatives. We used extensive sensitivity analysis to explore the patterns in the judgments, and also considered the preferred alternatives for each individual expert. The results show that two alternatives that are more flexible than the current policies are judged more likely to perform better than the current alternatives on average in terms of stochastic dominance. The results suggest judgmental approaches to robust decision making deserve greater attention and testing. © 2012 Society for Risk Analysis.

Model of critical diagnostic reasoning: achieving expert clinician performance.

PubMed

Harjai, Prashant Kumar; Tiwari, Ruby

2009-01-01

Diagnostic reasoning refers to the analytical processes used to determine patient health problems. While the education curriculum and health care system focus on training nurse clinicians to accurately recognize and rescue clinical situations, assessments of non-expert nurses have yielded less than satisfactory data on diagnostic competency. The contrast between the expert and non-expert nurse clinician raises the important question of how differences in thinking may contribute to a large divergence in accurate diagnostic reasoning. This article recognizes superior organization of one's knowledge base, using prototypes, and quick retrieval of pertinent information, using similarity recognition as two reasons for the expert's superior diagnostic performance. A model of critical diagnostic reasoning, using prototypes and similarity recognition, is proposed and elucidated using case studies. This model serves as a starting point toward bridging the gap between clinical data and accurate problem identification, verification, and management while providing a structure for a knowledge exchange between expert and non-expert clinicians.

Beyond the false negative rate: development of quality indicators for sentinel lymph node biopsy in breast cancer.

PubMed

Quan, May Lynn; Wells, Bryan J; McCready, David; Wright, Frances C; Fraser, Novlette; Gagliardi, Anna R

2010-02-01

Sentinel lymph node biopsy (SNLB) has been adopted as the standard method of axillary staging for women with clinically node-negative early-stage breast cancer. The false negative rate as a quality indicator is impractical given the need for a completion axillary dissection to calculate. The objective of this study was to develop practical quality indicators for SLNB using an expert consensus method and to determine if they were feasible to measure. We used a modified Delphi consensus process to develop quality indicators for SLNB. A multidisciplinary expert panel reviewed potential indicators extracted from the medical literature to select quality indicators that were relevant and measurable. Feasibility was determined by abstracting the quality indicator variables from a retrospective chart review. The expert panel prioritized 11 quality indicators as benchmarks for assessing the quality of surgical care in SNLB. Nine of the indicators were measurable at the chart or institutional level. A systematic evidence- and consensus-based approach was used to develop measurable quality indicators that could be used by practicing surgeons and administrators to evaluate performance of SLNB in breast cancer.

A spectral-knowledge-based approach for urban land-cover discrimination

NASA Technical Reports Server (NTRS)

Wharton, Stephen W.

1987-01-01

A prototype expert system was developed to demonstrate the feasibility of classifying multispectral remotely sensed data on the basis of spectral knowledge. The spectral expert was developed and tested with Thematic Mapper Simulator (TMS) data having eight spectral bands and a spatial resolution of 5 m. A knowledge base was developed that describes the target categories in terms of characteristic spectral relationships. The knowledge base was developed under the following assumptions: the data are calibrated to ground reflectance, the area is well illuminated, the pixels are dominated by a single category, and the target categories can be recognized without the use of spatial knowledge. Classification decisions are made on the basis of convergent evidence as derived from applying the spectral rules to a multiple spatial resolution representation of the image. The spectral expert achieved an accuracy of 80-percent correct or higher in recognizing 11 spectral categories in TMS data for the washington, DC, area. Classification performance can be expected to decrease for data that do not satisfy the above assumptions as illustrated by the 63-percent accuracy for 30-m resolution Thematic Mapper data.

Using Data Mining for Wine Quality Assessment

NASA Astrophysics Data System (ADS)

Cortez, Paulo; Teixeira, Juliana; Cerdeira, António; Almeida, Fernando; Matos, Telmo; Reis, José

Certification and quality assessment are crucial issues within the wine industry. Currently, wine quality is mostly assessed by physicochemical (e.g alcohol levels) and sensory (e.g. human expert evaluation) tests. In this paper, we propose a data mining approach to predict wine preferences that is based on easily available analytical tests at the certification step. A large dataset is considered with white vinho verde samples from the Minho region of Portugal. Wine quality is modeled under a regression approach, which preserves the order of the grades. Explanatory knowledge is given in terms of a sensitivity analysis, which measures the response changes when a given input variable is varied through its domain. Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selection and that is guided by the sensitivity analysis. The support vector machine achieved promising results, outperforming the multiple regression and neural network methods. Such model is useful for understanding how physicochemical tests affect the sensory preferences. Moreover, it can support the wine expert evaluations and ultimately improve the production.

Localizing Target Structures in Ultrasound Video

PubMed Central

Kwitt, R.; Vasconcelos, N.; Razzaque, S.; Aylward, S.

2013-01-01

The problem of localizing specific anatomic structures using ultrasound (US) video is considered. This involves automatically determining when an US probe is acquiring images of a previously defined object of interest, during the course of an US examination. Localization using US is motivated by the increased availability of portable, low-cost US probes, which inspire applications where inexperienced personnel and even first-time users acquire US data that is then sent to experts for further assessment. This process is of particular interest for routine examinations in underserved populations as well as for patient triage after natural disasters and large-scale accidents, where experts may be in short supply. The proposed localization approach is motivated by research in the area of dynamic texture analysis and leverages several recent advances in the field of activity recognition. For evaluation, we introduce an annotated and publicly available database of US video, acquired on three phantoms. Several experiments reveal the challenges of applying video analysis approaches to US images and demonstrate that good localization performance is possible with the proposed solution. PMID:23746488

Technology for Large-Scale Translation of Clinical Practice Guidelines: A Pilot Study of the Performance of a Hybrid Human and Computer-Assisted Approach.

PubMed

Van de Velde, Stijn; Macken, Lieve; Vanneste, Koen; Goossens, Martine; Vanschoenbeek, Jan; Aertgeerts, Bert; Vanopstal, Klaar; Vander Stichele, Robert; Buysschaert, Joost

2015-10-09

The construction of EBMPracticeNet, a national electronic point-of-care information platform in Belgium, began in 2011 to optimize quality of care by promoting evidence-based decision making. The project involved, among other tasks, the translation of 940 EBM Guidelines of Duodecim Medical Publications from English into Dutch and French. Considering the scale of the translation process, it was decided to make use of computer-aided translation performed by certificated translators with limited expertise in medical translation. Our consortium used a hybrid approach, involving a human translator supported by a translation memory (using SDL Trados Studio), terminology recognition (using SDL MultiTerm terminology databases) from medical terminology databases, and support from online machine translation. This resulted in a validated translation memory, which is now in use for the translation of new and updated guidelines. The objective of this experiment was to evaluate the performance of the hybrid human and computer-assisted approach in comparison with translation unsupported by translation memory and terminology recognition. A comparison was also made with the translation efficiency of an expert medical translator. We conducted a pilot study in which two sets of 30 new and 30 updated guidelines were randomized to one of three groups. Comparable guidelines were translated (1) by certificated junior translators without medical specialization using the hybrid method, (2) by an experienced medical translator without this support, and (3) by the same junior translators without the support of the validated translation memory. A medical proofreader who was blinded for the translation procedure, evaluated the translated guidelines for acceptability and adequacy. Translation speed was measured by recording translation and post-editing time. The human translation edit rate was calculated as a metric to evaluate the quality of the translation. A further evaluation was made of translation acceptability and adequacy. The average number of words per guideline was 1195 and the mean total translation time was 100.2 minutes/1000 words. No meaningful differences were found in the translation speed for new guidelines. The translation of updated guidelines was 59 minutes/1000 words faster (95% CI 2-115; P=.044) in the computer-aided group. Revisions due to terminology accounted for one third of the overall revisions by the medical proofreader. Use of the hybrid human and computer-aided translation by a non-expert translator makes the translation of updates of clinical practice guidelines faster and cheaper because of the benefits of translation memory. For the translation of new guidelines, there was no apparent benefit in comparison with the efficiency of translation unsupported by translation memory (whether by an expert or non-expert translator).

Technology for Large-Scale Translation of Clinical Practice Guidelines: A Pilot Study of the Performance of a Hybrid Human and Computer-Assisted Approach

PubMed Central

2015-01-01

Background The construction of EBMPracticeNet, a national electronic point-of-care information platform in Belgium, began in 2011 to optimize quality of care by promoting evidence-based decision making. The project involved, among other tasks, the translation of 940 EBM Guidelines of Duodecim Medical Publications from English into Dutch and French. Considering the scale of the translation process, it was decided to make use of computer-aided translation performed by certificated translators with limited expertise in medical translation. Our consortium used a hybrid approach, involving a human translator supported by a translation memory (using SDL Trados Studio), terminology recognition (using SDL MultiTerm terminology databases) from medical terminology databases, and support from online machine translation. This resulted in a validated translation memory, which is now in use for the translation of new and updated guidelines. Objective The objective of this experiment was to evaluate the performance of the hybrid human and computer-assisted approach in comparison with translation unsupported by translation memory and terminology recognition. A comparison was also made with the translation efficiency of an expert medical translator. Methods We conducted a pilot study in which two sets of 30 new and 30 updated guidelines were randomized to one of three groups. Comparable guidelines were translated (1) by certificated junior translators without medical specialization using the hybrid method, (2) by an experienced medical translator without this support, and (3) by the same junior translators without the support of the validated translation memory. A medical proofreader who was blinded for the translation procedure, evaluated the translated guidelines for acceptability and adequacy. Translation speed was measured by recording translation and post-editing time. The human translation edit rate was calculated as a metric to evaluate the quality of the translation. A further evaluation was made of translation acceptability and adequacy. Results The average number of words per guideline was 1195 and the mean total translation time was 100.2 minutes/1000 words. No meaningful differences were found in the translation speed for new guidelines. The translation of updated guidelines was 59 minutes/1000 words faster (95% CI 2-115; P=.044) in the computer-aided group. Revisions due to terminology accounted for one third of the overall revisions by the medical proofreader. Conclusions Use of the hybrid human and computer-aided translation by a non-expert translator makes the translation of updates of clinical practice guidelines faster and cheaper because of the benefits of translation memory. For the translation of new guidelines, there was no apparent benefit in comparison with the efficiency of translation unsupported by translation memory (whether by an expert or non-expert translator). PMID:26453372

Identifying psychophysiological indices of expert vs. novice performance in deadly force judgment and decision making

PubMed Central

Johnson, Robin R.; Stone, Bradly T.; Miranda, Carrie M.; Vila, Bryan; James, Lois; James, Stephen M.; Rubio, Roberto F.; Berka, Chris

2014-01-01

Objective: To demonstrate that psychophysiology may have applications for objective assessment of expertise development in deadly force judgment and decision making (DFJDM). Background: Modern training techniques focus on improving decision-making skills with participative assessment between trainees and subject matter experts primarily through subjective observation. Objective metrics need to be developed. The current proof of concept study explored the potential for psychophysiological metrics in deadly force judgment contexts. Method: Twenty-four participants (novice, expert) were recruited. All wore a wireless Electroencephalography (EEG) device to collect psychophysiological data during high-fidelity simulated deadly force judgment and decision-making simulations using a modified Glock firearm. Participants were exposed to 27 video scenarios, one-third of which would have justified use of deadly force. Pass/fail was determined by whether the participant used deadly force appropriately. Results: Experts had a significantly higher pass rate compared to novices (p < 0.05). Multiple metrics were shown to distinguish novices from experts. Hierarchical regression analyses indicate that psychophysiological variables are able to explain 72% of the variability in expert performance, but only 37% in novices. Discriminant function analysis (DFA) using psychophysiological metrics was able to discern between experts and novices with 72.6% accuracy. Conclusion: While limited due to small sample size, the results suggest that psychophysiology may be developed for use as an objective measure of expertise in DFDJM. Specifically, discriminant function measures may have the potential to objectively identify expert skill acquisition. Application: Psychophysiological metrics may create a performance model with the potential to optimize simulator-based DFJDM training. These performance models could be used for trainee feedback, and/or by the instructor to assess performance objectively. PMID:25100966

Interacting with notebook input devices: an analysis of motor performance and users' expertise.

PubMed

Sutter, Christine; Ziefle, Martina

2005-01-01

In the present study the usability of two different types of notebook input devices was examined. The independent variables were input device (touchpad vs. mini-joystick) and user expertise (expert vs. novice state). There were 30 participants, of whom 15 were touchpad experts and the other 15 were mini-joystick experts. The experimental tasks were a point-click task (Experiment 1) and a point-drag-drop task (Experiment 2). Dependent variables were the time and accuracy of cursor control. To assess carryover effects, we had the participants complete both experiments, using not only the input device for which they were experts but also the device for which they were novices. Results showed the touchpad performance to be clearly superior to mini-joystick performance. Overall, experts showed better performance than did novices. The significant interaction of input device and expertise showed that the use of an unknown device is difficult, but only for touchpad experts, who were remarkably slower and less accurate when using a mini-joystick. Actual and potential applications of this research include an evaluation of current notebook input devices. The outcomes allow ergonomic guidelines to be derived for optimized usage and design of the mini-joystick and touchpad devices.

An Expert Assistant for Computer Aided Parallelization

NASA Technical Reports Server (NTRS)

Jost, Gabriele; Chun, Robert; Jin, Haoqiang; Labarta, Jesus; Gimenez, Judit

2004-01-01

The prototype implementation of an expert system was developed to assist the user in the computer aided parallelization process. The system interfaces to tools for automatic parallelization and performance analysis. By fusing static program structure information and dynamic performance analysis data the expert system can help the user to filter, correlate, and interpret the data gathered by the existing tools. Sections of the code that show poor performance and require further attention are rapidly identified and suggestions for improvements are presented to the user. In this paper we describe the components of the expert system and discuss its interface to the existing tools. We present a case study to demonstrate the successful use in full scale scientific applications.

Achieving performance breakthroughs in an HMO business process through quality planning.

PubMed

Hanan, K B

1993-01-01

Kaiser Permanente's Georgia Region commissioned a quality planning team to design a new process to improve payments to its suppliers and vendors. The result of the team's effort was a 73 percent reduction in cycle time. This team's experiences point to the advantages of process redesign as a quality planning model, as well as some general guidelines for its most effective use in teams. If quality planning project teams are carefully configured, sufficiently expert in the existing process, and properly supported by management, organizations can achieve potentially dramatic improvements in process performance using this approach.

An Expert Systems Approach for PR Campaigns Research.

ERIC Educational Resources Information Center

Cameron, Glen T.; Curtin, Patricia A.

1992-01-01

Describes an expert system (the artificial intelligence program "Publics") that helps users identify key publics for public relations campaigns. Examines advantages and problems encountered in its use in public relations campaigns classrooms. (SR)

Qualitative Understanding of Magnetism at Three Levels of Expertise

NASA Astrophysics Data System (ADS)

Stefani, Francesco; Marshall, Jill

2010-03-01

This work set out to investigate the state of qualitative understanding of magnetism at various stages of expertise, and what approaches to problem-solving are used across the spectrum of expertise. We studied three groups: 10 novices, 10 experts-in-training, and 11 experts. Data collection involved structured interviews during which participants solved a series of non-standard problems designed to test for conceptual understanding of magnetism. The interviews were analyzed using a grounded theory approach. None of the novices and only a few of the experts in training showed a strong understanding of inductance, magnetic energy, and magnetic pressure; and for the most part they tended not to approach problems visually. Novices frequently described gist memories of demonstrations, text book problems, and rules (heuristics). However, these fragmentary mental models were not complete enough to allow them to reason productively. Experts-in-training were able to solve problems that the novices were not able to solve, many times simply because they had greater recall of the material, and therefore more confidence in their facts. Much of their thinking was concrete, based on mentally manipulating objects. The experts solved most of the problems in ways that were both effective and efficient. Part of the efficiency derived from their ability to visualize and thus reason in terms of field lines.

Qualitative Understanding of Magnetism at Three Levels of Expertise

NASA Astrophysics Data System (ADS)

Stefani, Francesco; Marshall, Jill

2009-04-01

This work set out to investigate the state of qualitative understanding of magnetism at various stages of expertise, and what approaches to problem-solving are used across the spectrum of expertise. We studied three groups: 10 novices, 10 experts-in-training, and 11 experts. Data collection involved structured interviews during which participants solved a series of non-standard problems designed to test for conceptual understanding of magnetism. The interviews were analyzed using a grounded theory approach. None of the novices and only a few of the experts in training showed a strong understanding of inductance, magnetic energy, and magnetic pressure; and for the most part they tended not to approach problems visually. Novices frequently described gist memories of demonstrations, text book problems, and rules (heuristics). However, these fragmentary mental models were not complete enough to allow them to reason productively. Experts-in-training were able to solve problems that the novices were not able to solve, many times simply because they had greater recall of the material, and therefore more confidence in their facts. Much of their thinking was concrete, based on mentally manipulating objects. The experts solved most of the problems in ways that were both effective and efficient. Part of the efficiency derived from their ability to visualize and thus reason in terms of field lines.

Analysis and Lessons Learned from an Online, Consultative Dialogue between Community Leaders and Climate Experts

NASA Astrophysics Data System (ADS)

Sylak-Glassman, E.; Clavin, C.

2016-12-01

Common approaches to climate resilience planning in the United States rely upon participatory planning approaches and dialogues between decision-makers, science translators, and subject matter experts. In an effort to explore alternative approaches support community climate resilience planning, a pilot of a public-private collaboration called the Resilience Dialogues was held in February and March of 2016. The Resilience Dialogues pilot was an online, asynchronous conversation between community leaders and climate experts, designed to help communities begin the process of climate resilience planning. In order to identify lessons learned from the pilot, we analyzed the discourse of the facilitated dialogues, administered surveys and conducted interviews with participants. Our analysis of the pilot suggests that participating community leaders found value in the consultative dialogue with climate experts, despite limited community-originated requests for climate information. Community leaders most often asked for advice regarding adaptation planning, including specific engineering guidance and advice on how to engage community members around the topic of resilience. Community leaders that had access to downscaled climate data asked experts about how to incorporate the data into their existing planning processes. The guidance sought by community leaders during the pilot shows a large range of hurdles that communities face in using climate information to inform their decision-making processes. Having a forum that connects community leaders with relevant experts and other community leaders who have familiarity with both climate impacts and municipal planning processes would likely help communities accelerate their resilience efforts.

A Qualitative Inquiry Into the Challenges of Medical Education for Retention of General Practitioners in Rural and Underserved Areas of Iran

PubMed Central

2016-01-01

Objectives General practitioners (GPs) retention in rural and underserved areas highly effects on accessibility of healthcare facilities across the country. Education seems to be a critical factor that affects GPs retention. Thus, the present study aimed at inquiry into medical education challenges that limit their retention in rural and underserved areas. Methods A qualitative approach was applied for the aim of this study. Data were gathered via 28 semi-structured interviews with experts at different levels of Iran’s health system as well as GPs who retained and refused to retain working in rural settings. Interviews mainly were performed face-to-face and in some cases via telephone during 2015 and then coded and analyzed using content analysis approach. Results Iran’s medical education is faced with several challenges that were categorized in four main themes including student selection, medical students’ perception about their field of study, education setting and approach, curriculum of medical education. According to experts this challenges could results in making GP graduates disinterested for practicing in rural and underserved areas. Conclusions Challenges that were found could have negative effects on retention. Modification in student’s perception about rural practice could be done via changing education setting and approach and curriculum. These modifications could improve GPs retention in rural and underserved areas. PMID:27951631

A novel method for expediting the development of patient-reported outcome measures and an evaluation across several populations

PubMed Central

Garrard, Lili; Price, Larry R.; Bott, Marjorie J.; Gajewski, Byron J.

2016-01-01

Item response theory (IRT) models provide an appropriate alternative to the classical ordinal confirmatory factor analysis (CFA) during the development of patient-reported outcome measures (PROMs). Current literature has identified the assessment of IRT model fit as both challenging and underdeveloped (Sinharay & Johnson, 2003; Sinharay, Johnson, & Stern, 2006). This study evaluates the performance of Ordinal Bayesian Instrument Development (OBID), a Bayesian IRT model with a probit link function approach, through applications in two breast cancer-related instrument development studies. The primary focus is to investigate an appropriate method for comparing Bayesian IRT models in PROMs development. An exact Bayesian leave-one-out cross-validation (LOO-CV) approach (Vehtari & Lampinen, 2002) is implemented to assess prior selection for the item discrimination parameter in the IRT model and subject content experts’ bias (in a statistical sense and not to be confused with psychometric bias as in differential item functioning) toward the estimation of item-to-domain correlations. Results support the utilization of content subject experts’ information in establishing evidence for construct validity when sample size is small. However, the incorporation of subject experts’ content information in the OBID approach can be sensitive to the level of expertise of the recruited experts. More stringent efforts need to be invested in the appropriate selection of subject experts to efficiently use the OBID approach and reduce potential bias during PROMs development. PMID:27667878

Evolving rule-based systems in two medical domains using genetic programming.

PubMed

Tsakonas, Athanasios; Dounias, Georgios; Jantzen, Jan; Axer, Hubertus; Bjerregaard, Beth; von Keyserlingk, Diedrich Graf

2004-11-01

To demonstrate and compare the application of different genetic programming (GP) based intelligent methodologies for the construction of rule-based systems in two medical domains: the diagnosis of aphasia's subtypes and the classification of pap-smear examinations. Past data representing (a) successful diagnosis of aphasia's subtypes from collaborating medical experts through a free interview per patient, and (b) correctly classified smears (images of cells) by cyto-technologists, previously stained using the Papanicolaou method. Initially a hybrid approach is proposed, which combines standard genetic programming and heuristic hierarchical crisp rule-base construction. Then, genetic programming for the production of crisp rule based systems is attempted. Finally, another hybrid intelligent model is composed by a grammar driven genetic programming system for the generation of fuzzy rule-based systems. Results denote the effectiveness of the proposed systems, while they are also compared for their efficiency, accuracy and comprehensibility, to those of an inductive machine learning approach as well as to those of a standard genetic programming symbolic expression approach. The proposed GP-based intelligent methodologies are able to produce accurate and comprehensible results for medical experts performing competitive to other intelligent approaches. The aim of the authors was the production of accurate but also sensible decision rules that could potentially help medical doctors to extract conclusions, even at the expense of a higher classification score achievement.

Is decentralization good for logistics systems? Evidence on essential medicine logistics in Ghana and Guatemala.

PubMed

Bossert, Thomas J; Bowser, Diana M; Amenyah, Johnnie K

2007-03-01

Efficient logistics systems move essential medicines down the supply chain to the service delivery point, and then to the end user. Experts on logistics systems tend to see the supply chain as requiring centralized control to be most effective. However, many health reforms have involved decentralization, which experts fear has disrupted the supply chain and made systems less effective. There is no consensus on an appropriate methodology for assessing the effectiveness of decentralization in general, and only a few studies have attempted to address decentralization of logistics systems. This paper sets out a framework and methodology of a pioneering exploratory study that examines the experiences of decentralization in two countries, Guatemala and Ghana, and presents suggestive results of how decentralization affected the performance of their logistics systems. The analytical approach assessed decentralization using the principal author's 'decision space' approach, which defines decentralization as the degree of choice that local officials have over different health system functions. In this case the approach focused on 15 different logistics functions and measured the relationship between the degree of choice and indicators of performance for each of the functions. The results of both studies indicate that less choice (i.e. more centralized) was associated with better performance for two key functions (inventory control and information systems), while more choice (i.e. more decentralized) over planning and budgeting was associated with better performance. With different systems of procurement in Ghana and Guatemala, we found that a system with some elements of procurement that are centralized (selection of firms and prices fixed by national tender) was positively related in Guatemala but negatively related in Ghana, where a system of 'cash and carry' cost recovery allowed more local choice. The authors conclude that logistics systems can be effectively decentralized for some functions while others should remain centralized. These preliminary findings, however, should be subject to alternative methodologies to confirm the findings.

The First Expert CAI System

PubMed Central

Feurzeig, Wallace

1984-01-01

The first expert instructional system, the Socratic System, was developed in 1964. One of the earliest applications of this system was in the area of differential diagnosis in clinical medicine. The power of the underlying instructional paradigm was demonstrated and the potential of the approach for valuably supplementing medical instruction was recognized. Twenty years later, despite further educationally significant advances in expert systems technology and enormous reductions in the cost of computers, expert instructional methods have found very little application in medical schools.

A citizen science approach to optimising computer aided detection (CAD) in mammography

NASA Astrophysics Data System (ADS)

Ionescu, Georgia V.; Harkness, Elaine F.; Hulleman, Johan; Astley, Susan M.

2018-03-01

Computer aided detection (CAD) systems assist medical experts during image interpretation. In mammography, CAD systems prompt suspicious regions which help medical experts to detect early signs of cancer. This is a challenging task and prompts may appear in regions that are actually normal, whilst genuine cancers may be missed. The effect prompting has on readers performance is not fully known. In order to explore the effects of prompting errors, we have created an online game (Bat Hunt), designed for non-experts, that mirrors mammographic CAD. This allows us to explore a wider parameter space. Users are required to detect bats in images of flocks of birds, with image difficulty matched to the proportions of screening mammograms in different BI-RADS density categories. Twelve prompted conditions were investigated, along with unprompted detection. On average, players achieved a sensitivity of 0.33 for unprompted detection, and sensitivities of 0.75, 0.83, and 0.92 respectively for 70%, 80%, and 90% of targets prompted, regardless of CAD specificity. False prompts distract players from finding unprompted targets if they appear in the same image. Player performance decreases when the number of false prompts increases, and increases proportionally with prompting sensitivity. Median lowest d' was for unprompted condition (1.08) and the highest for sensitivity 90% and 0.5 false prompts per image (d'=4.48).

The composite load spectra project

NASA Technical Reports Server (NTRS)

Newell, J. F.; Ho, H.; Kurth, R. E.

1990-01-01

Probabilistic methods and generic load models capable of simulating the load spectra that are induced in space propulsion system components are being developed. Four engine component types (the transfer ducts, the turbine blades, the liquid oxygen posts and the turbopump oxidizer discharge duct) were selected as representative hardware examples. The composite load spectra that simulate the probabilistic loads for these components are typically used as the input loads for a probabilistic structural analysis. The knowledge-based system approach used for the composite load spectra project provides an ideal environment for incremental development. The intelligent database paradigm employed in developing the expert system provides a smooth coupling between the numerical processing and the symbolic (information) processing. Large volumes of engine load information and engineering data are stored in database format and managed by a database management system. Numerical procedures for probabilistic load simulation and database management functions are controlled by rule modules. Rules were hard-wired as decision trees into rule modules to perform process control tasks. There are modules to retrieve load information and models. There are modules to select loads and models to carry out quick load calculations or make an input file for full duty-cycle time dependent load simulation. The composite load spectra load expert system implemented today is capable of performing intelligent rocket engine load spectra simulation. Further development of the expert system will provide tutorial capability for users to learn from it.

Mission to Mars: Connecting Diverse Student Groups with NASA Experts

NASA Technical Reports Server (NTRS)

Polsgrove, Tara; Jones, David; Sadowski-Fugitt, Leslie; Kowrach, Nicole

2012-01-01

The Museum of Science and Industry in Chicago has formulated an innovative approach to inspiring the next generation to pursue STEM education. Middle school students in Chicago and at nearby Challenger Learning Centers work in teams to design a mission to Mars. Each mission includes real time access to NASA experts through partnerships with Marshall Space Flight Center, Johnson Space Center, and the Jet Propulsion Laboratory. Interactive videoconferencing connects students at the museum with students at a Challenger Learning Center and with NASA experts. This paper describes the approach, the results from the program s first year, and future opportunities for nationwide expansion.

Stereo and photometric image sequence interpretation for detecting negative obstacles using active gaze control and performing an autonomous jink

NASA Astrophysics Data System (ADS)

Hofmann, Ulrich; Siedersberger, Karl-Heinz

2003-09-01

Driving cross-country, the detection and state estimation relative to negative obstacles like ditches and creeks is mandatory for safe operation. Very often, ditches can be detected both by different photometric properties (soil vs. vegetation) and by range (disparity) discontinuities. Therefore, algorithms should make use of both the photometric and geometric properties to reliably detect obstacles. This has been achieved in UBM's EMS-Vision System (Expectation-based, Multifocal, Saccadic) for autonomous vehicles. The perception system uses Sarnoff's image processing hardware for real-time stereo vision. This sensor provides both gray value and disparity information for each pixel at high resolution and framerates. In order to perform an autonomous jink, the boundaries of an obstacle have to be measured accurately for calculating a safe driving trajectory. Especially, ditches are often very extended, so due to the restricted field of vision of the cameras, active gaze control is necessary to explore the boundaries of an obstacle. For successful measurements of image features the system has to satisfy conditions defined by the perception expert. It has to deal with the time constraints of the active camera platform while performing saccades and to keep the geometric conditions defined by the locomotion expert for performing a jink. Therefore, the experts have to cooperate. This cooperation is controlled by a central decision unit (CD), which has knowledge about the mission and the capabilities available in the system and of their limitations. The central decision unit reacts dependent on the result of situation assessment by starting, parameterizing or stopping actions (instances of capabilities). The approach has been tested with the 5-ton van VaMoRs. Experimental results will be shown for driving in a typical off-road scenario.

Tracking Plasticity: Effects of Long-Term Rehearsal in Expert Dancers Encoding Music to Movement

PubMed Central

Bar, Rachel J.; DeSouza, Joseph F. X.

2016-01-01

Our knowledge of neural plasticity suggests that neural networks show adaptation to environmental and intrinsic change. In particular, studies investigating the neuroplastic changes associated with learning and practicing motor tasks have shown that practicing such tasks results in an increase in neural activation in several specific brain regions. However, studies comparing experts and non-experts suggest that experts employ less neuronal activation than non-experts when performing a familiar motor task. Here, we aimed to determine the long-term changes in neural networks associated with learning a new dance in professional ballet dancers over 34 weeks. Subjects visualized dance movements to music while undergoing fMRI scanning at four time points over 34-weeks. Results demonstrated that initial learning and performance at seven weeks led to increases in activation in cortical regions during visualization compared to the first week. However, at 34 weeks, the cortical networks showed reduced activation compared to week seven. Specifically, motor learning and performance over the 34 weeks showed the typical inverted-U-shaped function of learning. Further, our result demonstrate that learning of a motor sequence of dance movements to music in the real world can be visualized by expert dancers using fMRI and capture highly significant modeled fits of the brain network variance of BOLD signals from early learning to expert level performance. PMID:26824475

Optimization of space system development resources

NASA Astrophysics Data System (ADS)

Kosmann, William J.; Sarkani, Shahram; Mazzuchi, Thomas

2013-06-01

NASA has had a decades-long problem with cost growth during the development of space science missions. Numerous agency-sponsored studies have produced average mission level cost growths ranging from 23% to 77%. A new study of 26 historical NASA Science instrument set developments using expert judgment to reallocate key development resources has an average cost growth of 73.77%. Twice in history, a barter-based mechanism has been used to reallocate key development resources during instrument development. The mean instrument set development cost growth was -1.55%. Performing a bivariate inference on the means of these two distributions, there is statistical evidence to support the claim that using a barter-based mechanism to reallocate key instrument development resources will result in a lower expected cost growth than using the expert judgment approach. Agent-based discrete event simulation is the natural way to model a trade environment. A NetLogo agent-based barter-based simulation of science instrument development was created. The agent-based model was validated against the Cassini historical example, as the starting and ending instrument development conditions are available. The resulting validated agent-based barter-based science instrument resource reallocation simulation was used to perform 300 instrument development simulations, using barter to reallocate development resources. The mean cost growth was -3.365%. A bivariate inference on the means was performed to determine that additional significant statistical evidence exists to support a claim that using barter-based resource reallocation will result in lower expected cost growth, with respect to the historical expert judgment approach. Barter-based key development resource reallocation should work on spacecraft development as well as it has worked on instrument development. A new study of 28 historical NASA science spacecraft developments has an average cost growth of 46.04%. As barter-based key development resource reallocation has never been tried in a spacecraft development, no historical results exist, and a simulation of using that approach must be developed. The instrument development simulation should be modified to account for spacecraft development market participant differences. The resulting agent-based barter-based spacecraft resource reallocation simulation would then be used to determine if significant statistical evidence exists to prove a claim that using barter-based resource reallocation will result in lower expected cost growth.

Beethoven recordings reviewed: a systematic method for mapping the content of music performance criticism

PubMed Central

Alessandri, Elena; Williamson, Victoria J.; Eiholzer, Hubert; Williamon, Aaron

2015-01-01

Critical reviews offer rich data that can be used to investigate how musical experiences are conceptualized by expert listeners. However, these data also present significant challenges in terms of organization, analysis, and interpretation. This study presents a new systematic method for examining written responses to music, tested on a substantial corpus of music criticism. One hundred critical reviews of Beethoven’s piano sonata recordings, published in the Gramophone between August 1934 and July 2010, were selected using in-depth data reduction (qualitative/quantitative approach). The texts were then examined using thematic analysis in order to generate a visual descriptive model of expert critical review. This model reveals how the concept of evaluation permeates critical review. It also distinguishes between two types of descriptors. The first characterizes the performance in terms of specific actions or features of the musical sound (musical parameters, technique, and energy); the second appeals to higher-order properties (artistic style, character and emotion, musical structure, communicativeness) or assumed performer qualities (understanding, intentionality, spontaneity, sensibility, control, and care). The new model provides a methodological guide and conceptual basis for future studies of critical review in any genre. PMID:25741295

Beethoven recordings reviewed: a systematic method for mapping the content of music performance criticism.

PubMed

Alessandri, Elena; Williamson, Victoria J; Eiholzer, Hubert; Williamon, Aaron

2015-01-01

Critical reviews offer rich data that can be used to investigate how musical experiences are conceptualized by expert listeners. However, these data also present significant challenges in terms of organization, analysis, and interpretation. This study presents a new systematic method for examining written responses to music, tested on a substantial corpus of music criticism. One hundred critical reviews of Beethoven's piano sonata recordings, published in the Gramophone between August 1934 and July 2010, were selected using in-depth data reduction (qualitative/quantitative approach). The texts were then examined using thematic analysis in order to generate a visual descriptive model of expert critical review. This model reveals how the concept of evaluation permeates critical review. It also distinguishes between two types of descriptors. The first characterizes the performance in terms of specific actions or features of the musical sound (musical parameters, technique, and energy); the second appeals to higher-order properties (artistic style, character and emotion, musical structure, communicativeness) or assumed performer qualities (understanding, intentionality, spontaneity, sensibility, control, and care). The new model provides a methodological guide and conceptual basis for future studies of critical review in any genre.

The Principles of Designing an Expert System in Teaching Mathematics

ERIC Educational Resources Information Center

Salekhova, Lailya; Nurgaliev, Albert; Zaripova, Rinata; Khakimullina, Nailya

2013-01-01

This study reveals general didactic concepts of the Expert Systems (ES) development process in the educational area. The proof of concept is based on the example of teaching the 8th grade Algebra subject. The main contribution in this work is the implementation of innovative approaches in analysis and processing of data by expert system as well as…

Defining the Correctness of a Diagnosis: Differential Judgments and Expert Knowledge

ERIC Educational Resources Information Center

Kanter, Steven L.; Brosenitsch, Teresa A.; Mahoney, John F.; Staszewski, James

2010-01-01

Approaches that use a simulated patient case to study and assess diagnostic reasoning usually use the correct diagnosis of the case as a measure of success and as an anchor for other measures. Commonly, the correctness of a diagnosis is determined by the judgment of one or more experts. In this study, the consistency of experts' judgments of the…

Uncovering Predictors of Disagreement: Ensuring the Quality of Expert Ratings

ERIC Educational Resources Information Center

Hoth, Jessica; Schwarz, Björn; Kaiser, Gabriele; Busse, Andreas; König, Johannes; Blömeke, Sigrid

2016-01-01

Rating scales are a popular item format used in many types of assessments. Yet, defining which rating is correct often represents a challenge. Using expert ratings as benchmarks is one approach to ensuring the quality of a rating instrument. In this paper, such expert ratings are analyzed in detail taking a video-based test instrument of teachers'…

Design Of An Intelligent Robotic System Organizer Via Expert System Tecniques

NASA Astrophysics Data System (ADS)

Yuan, Peter H.; Valavanis, Kimon P.

1989-02-01

Intelligent Robotic Systems are a special type of Intelligent Machines. When modeled based on Vle theory of Intelligent Controls, they are composed of three interactive levels, namely: organization, coordination, and execution, ordered according, to the ,Principle of Increasing, Intelligence with Decreasing Precl.sion. Expert System techniques, are used to design an Intelligent Robotic System Organizer with a dynamic Knowledge Base and an interactive Inference Engine. Task plans are formulated using, either or both of a Probabilistic Approach and Forward Chapling Methodology, depending on pertinent information associated with a spec;fic requested job. The Intelligent Robotic System, Organizer is implemented and tested on a prototype system operating in an uncertain environment. An evaluation of-the performance, of the prototype system is conducted based upon the probability of generating a successful task sequence versus the number of trials taken by the organizer.

Unintended Consequences of Sensor, Signal, and Imaging Informatics: New Problems and New Solutions.

PubMed

Hughes, C; Voros, S; Moreau-Gaudry, A

2016-11-10

This synopsis presents a selection for the IMIA (International Medical Informatics Association) Yearbook 2016 of excellent research in the broad field of Sensor, Signal and Imaging Informatics published in the year 2015, with a focus on Unintended consequences: new problems and new solutions. We performed a systematic initial selection and a double blind peer review process to find the best papers in this domain published in 2015, from the PubMed and Web of Science databases. The set of MesH keywords used was provided by experts. The constant advances in medical technology allow ever more relevant diagnostic and therapeutic approaches to be designed. Nevertheless, there is a need to acquire expert knowledge of these innovations in order to identify precociously new associated problems for which new solutions need to be designed and developed.

Treat-to-target (T2T) recommendations for gout.

PubMed

Kiltz, U; Smolen, J; Bardin, T; Cohen Solal, A; Dalbeth, N; Doherty, M; Engel, B; Flader, C; Kay, J; Matsuoka, M; Perez-Ruiz, F; da Rocha Castelar-Pinheiro, G; Saag, K; So, A; Vazquez Mellado, J; Weisman, M; Westhoff, T H; Yamanaka, H; Braun, J

2017-04-01

The treat-to-target (T2T) concept has been applied successfully in several inflammatory rheumatic diseases. Gout is a chronic disease with a high burden of pain and inflammation. Because the pathogenesis of gout is strongly related to serum urate levels, gout may be an ideal disease in which to apply a T2T approach. Our aim was to develop international T2T recommendations for patients with gout. A committee of experts with experience in gout agreed upon potential targets and outcomes, which was the basis for the systematic literature search. Eleven rheumatologists, one cardiologist, one nephrologist, one general practitioner and one patient met in October 2015 to develop T2T recommendations based on the available scientific evidence. Levels of evidence, strength of recommendations and levels of agreement were derived. Although no randomised trial was identified in which a comparison with standard treatment or an evaluation of a T2T approach had been performed in patients with gout, indirect evidence was provided to focus on targets such as normalisation of serum urate levels. The expert group developed four overarching principles and nine T2T recommendations. They considered dissolution of crystals and prevention of flares to be fundamental; patient education, ensuring adherence to medications and monitoring of serum urate levels were also considered to be of major importance. This is the first application of the T2T approach developed for gout. Since no publication reports a trial comparing treatment strategies for gout, highly credible overarching principles and level D expert recommendations were created and agreed upon. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Application of stakeholder-based and modelling approaches for supporting robust adaptation decision making under future climatic uncertainty and changing urban-agricultural water demand

NASA Astrophysics Data System (ADS)

Bhave, Ajay; Dessai, Suraje; Conway, Declan; Stainforth, David

2016-04-01

Deep uncertainty in future climate change and socio-economic conditions necessitates the use of assess-risk-of-policy approaches over predict-then-act approaches for adaptation decision making. Robust Decision Making (RDM) approaches embody this principle and help evaluate the ability of adaptation options to satisfy stakeholder preferences under wide-ranging future conditions. This study involves the simultaneous application of two RDM approaches; qualitative and quantitative, in the Cauvery River Basin in Karnataka (population ~23 million), India. The study aims to (a) determine robust water resources adaptation options for the 2030s and 2050s and (b) compare the usefulness of a qualitative stakeholder-driven approach with a quantitative modelling approach. For developing a large set of future scenarios a combination of climate narratives and socio-economic narratives was used. Using structured expert elicitation with a group of climate experts in the Indian Summer Monsoon, climatic narratives were developed. Socio-economic narratives were developed to reflect potential future urban and agricultural water demand. In the qualitative RDM approach, a stakeholder workshop helped elicit key vulnerabilities, water resources adaptation options and performance criteria for evaluating options. During a second workshop, stakeholders discussed and evaluated adaptation options against the performance criteria for a large number of scenarios of climatic and socio-economic change in the basin. In the quantitative RDM approach, a Water Evaluation And Planning (WEAP) model was forced by precipitation and evapotranspiration data, coherent with the climatic narratives, together with water demand data based on socio-economic narratives. We find that compared to business-as-usual conditions options addressing urban water demand satisfy performance criteria across scenarios and provide co-benefits like energy savings and reduction in groundwater depletion, while options reducing agricultural water demand significantly affect downstream water availability. Water demand options demonstrate potential to improve environmental flow conditions and satisfy legal water supply requirements for downstream riparian states. On the other hand, currently planned large scale infrastructural projects demonstrate reduced value in certain scenarios, illustrating the impacts of lock-in effects of large scale infrastructure. From a methodological perspective, we find that while the stakeholder-driven approach revealed robust options in a resource-light manner and helped initiate much needed interaction amongst stakeholders, the modelling approach provides complementary quantitative information. The study reveals robust adaptation options for this important basin and provides a strong methodological basis for carrying out future studies that support adaptation decision making.

Sorting Through the Safety Data Haystack: Using Machine Learning to Identify Individual Case Safety Reports in Social-Digital Media.

PubMed

Comfort, Shaun; Perera, Sujan; Hudson, Zoe; Dorrell, Darren; Meireis, Shawman; Nagarajan, Meenakshi; Ramakrishnan, Cartic; Fine, Jennifer

2018-06-01

There is increasing interest in social digital media (SDM) as a data source for pharmacovigilance activities; however, SDM is considered a low information content data source for safety data. Given that pharmacovigilance itself operates in a high-noise, lower-validity environment without objective 'gold standards' beyond process definitions, the introduction of large volumes of SDM into the pharmacovigilance workflow has the potential to exacerbate issues with limited manual resources to perform adverse event identification and processing. Recent advances in medical informatics have resulted in methods for developing programs which can assist human experts in the detection of valid individual case safety reports (ICSRs) within SDM. In this study, we developed rule-based and machine learning (ML) models for classifying ICSRs from SDM and compared their performance with that of human pharmacovigilance experts. We used a random sampling from a collection of 311,189 SDM posts that mentioned Roche products and brands in combination with common medical and scientific terms sourced from Twitter, Tumblr, Facebook, and a spectrum of news media blogs to develop and evaluate three iterations of an automated ICSR classifier. The ICSR classifier models consisted of sub-components to annotate the relevant ICSR elements and a component to make the final decision on the validity of the ICSR. Agreement with human pharmacovigilance experts was chosen as the preferred performance metric and was evaluated by calculating the Gwet AC1 statistic (gKappa). The best performing model was tested against the Roche global pharmacovigilance expert using a blind dataset and put through a time test of the full 311,189-post dataset. During this effort, the initial strict rule-based approach to ICSR classification resulted in a model with an accuracy of 65% and a gKappa of 46%. Adding an ML-based adverse event annotator improved the accuracy to 74% and gKappa to 60%. This was further improved by the addition of an additional ML ICSR detector. On a blind test set of 2500 posts, the final model demonstrated a gKappa of 78% and an accuracy of 83%. In the time test, it took the final model 48 h to complete a task that would have taken an estimated 44,000 h for human experts to perform. The results of this study indicate that an effective and scalable solution to the challenge of ICSR detection in SDM includes a workflow using an automated ML classifier to identify likely ICSRs for further human SME review.

The development of expertise in pediatric rehabilitation therapists: changes in approach, self-knowledge, and use of enabling and customizing strategies.

PubMed

King, Gillian; Currie, Melissa; Bartlett, Doreen J; Gilpin, Michelle; Willoughby, Colleen; Tucker, Mary Ann; Strachan, Deborah; Baxter, Donna

2007-01-01

To examine the clinical decision making of novice, intermediate, and expert pediatric rehabilitation therapists from various disciplines. Two qualitative studies were conducted. Thirteen therapists took part in a study using the critical incident interview technique and 11 therapists took part in a study using the 'think aloud' technique. Therapists were classified as novice, intermediate, or expert in developmental level based on a cluster analysis of data collected using a multifaceted battery of assessment tools. Data were analyzed using a grounded theory approach. Expert and intermediate therapists differed from novices with respect to content, self-, and procedural knowledge. With increasing expertise, therapists use a supportive, educational, holistic, functional, and strengths-based approach; have heightened humility yet increased self-confidence; and understand how to facilitate and support client change and adaptation by using principles of engagement, coherence, and manageability. Expert therapists use enabling and customizing strategies to ensure a successful therapeutic session, optimize the child's functioning in the mid-term, and ensure child and family adaptation and accommodation over the longer-term.

Aerodynamics of the EXPERT Re-Entry Ballistic Vehicle

NASA Astrophysics Data System (ADS)

Kharitonov, A. M.; Adamov, N. P.; Mazhul, I. I.; Vasenyov, L. G.; Zvegintsev, V. I.; Muylaert, J. M.

2009-01-01

Since 2002 till now, experimental studies of the EXPERT reentry capsule have been performed in ITAM SB RAS wind tunnels. These studies have been performed in consecutive ISTC project No. 2109, 3151, and currently ongoing project No. 3550. The results of earlier studies in ITAM wind tunnels can be found in [1-4]. The present paper describes new data obtained for the EXPERT model.

Proof-of-Concept Demonstrations for Computation-Based Human Reliability Analysis. Modeling Operator Performance During Flooding Scenarios

DOE Office of Scientific and Technical Information (OSTI.GOV)

Joe, Jeffrey Clark; Boring, Ronald Laurids; Herberger, Sarah Elizabeth Marie

The United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program has the overall objective to help sustain the existing commercial nuclear power plants (NPPs). To accomplish this program objective, there are multiple LWRS “pathways,” or research and development (R&D) focus areas. One LWRS focus area is called the Risk-Informed Safety Margin and Characterization (RISMC) pathway. Initial efforts under this pathway to combine probabilistic and plant multi-physics models to quantify safety margins and support business decisions also included HRA, but in a somewhat simplified manner. HRA experts at Idaho National Laboratory (INL) have been collaborating with othermore » experts to develop a computational HRA approach, called the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER), for inclusion into the RISMC framework. The basic premise of this research is to leverage applicable computational techniques, namely simulation and modeling, to develop and then, using RAVEN as a controller, seamlessly integrate virtual operator models (HUNTER) with 1) the dynamic computational MOOSE runtime environment that includes a full-scope plant model, and 2) the RISMC framework PRA models already in use. The HUNTER computational HRA approach is a hybrid approach that leverages past work from cognitive psychology, human performance modeling, and HRA, but it is also a significant departure from existing static and even dynamic HRA methods. This report is divided into five chapters that cover the development of an external flooding event test case and associated statistical modeling considerations.« less

The evaluation of a novel haptic-enabled virtual reality approach for computer-aided cephalometry.

PubMed

Medellín-Castillo, H I; Govea-Valladares, E H; Pérez-Guerrero, C N; Gil-Valladares, J; Lim, Theodore; Ritchie, James M

2016-07-01

In oral and maxillofacial surgery, conventional radiographic cephalometry is one of the standard auxiliary tools for diagnosis and surgical planning. While contemporary computer-assisted cephalometric systems and methodologies support cephalometric analysis, they tend neither to be practical nor intuitive for practitioners. This is particularly the case for 3D methods since the associated landmarking process is difficult and time consuming. In addition to this, there are no 3D cephalometry norms or standards defined; therefore new landmark selection methods are required which will help facilitate their establishment. This paper presents and evaluates a novel haptic-enabled landmarking approach to overcome some of the difficulties and disadvantages of the current landmarking processes used in 2D and 3D cephalometry. In order to evaluate this new system's feasibility and performance, 21 dental surgeons (comprising 7 Novices, 7 Semi-experts and 7 Experts) performed a range of case studies using a haptic-enabled 2D, 2½D and 3D digital cephalometric analyses. The results compared the 2D, 2½D and 3D cephalometric values, errors and standard deviations for each case study and associated group of participants and revealed that 3D cephalometry significantly reduced landmarking errors and variability compared to 2D methods. Through enhancing the process by providing a sense of touch, the haptic-enabled 3D digital cephalometric approach was found to be feasible and more intuitive than its counterparts as well effective at reducing errors, the variability of the measurements taken and associated task completion times. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

We’re only in it for the knowledge? A problem solving turn in environment and health expert elicitation

PubMed Central

2012-01-01

Background The FP6 EU HENVINET project aimed at synthesizing the scientific information available on a number of topics of high relevance to policy makers in environment and health. The goal of the current paper is to reflect on the methodology that was used in the project, in view of exploring the usefulness of this and similar methodologies to the policy process. The topics investigated included health impacts of the brominated flame retardants decabrominated diphenylether (decaBDE) and hexabromocyclododecane (HBCD), phthalates highlighting di(2-ethylhexyl)phthalate (DEHP), the pesticide chlorpyrifos (CPF), nanoparticles, the impacts of climate change on asthma and other respiratory disorders, and the influence of environment health stressors on cancer induction. Methods Initially the focus was on identifying knowledge gaps in the state of the art in scientific knowledge. Literature reviews covered all elements that compose the causal chain of the different environmental health issues from emissions to exposures, to effects and to health impacts. Through expert elicitation, knowledge gaps were highlighted by assessing expert confidence using calibrated confidence scales. During this work a complementary focus to that on knowledge gaps was developed through interdisciplinary reflections. By extending the scope of the endeavour from only a scientific perspective, to also include the more problem solving oriented policy perspective, the question of which kind of policy action experts consider justifiable was addressed. This was addressed by means of a questionnaire. In an expert workshop the results of both questionnaires were discussed as a basis for policy briefs. Results The expert elicitation, the application of the calibrated confidence levels and the problem solving approach were all experienced as being quite challenging for the experts involved, as these approaches did not easily relate to mainstream environment and health scientific practices. Even so, most experts were quite positive about it. In particular, the opportunity to widen one’s own horizon and to interactively exchange knowledge and debate with a diversity of experts seemed to be well appreciated in this approach. Different parts of the approach also helped in focussing on specific relevant aspects of scientific knowledge, and as such can be considered of reflective value. Conclusions The approach developed by HENVINET was part of a practice of learning by doing and of interdisciplinary cooperation and negotiation. Ambitions were challenged by unforeseen complexities and difference of opinion and as no Holy Grail approach was at hand to copy or follow, it was quite an interesting but also complicated endeavour. Perfection, if this could be defined, seemed out of reach all the time. Nevertheless, many involved were quite positive about it. It seems that many felt that it fitted some important needs in current science when addressing the needs of policy making on such important issues, without anyone really having a clue on how to actually do this. Challenging questions remain on the quality of such approach and its product. Practice tells us that there probably is no best method and that the best we can do is dependent on contextual negotiation and learning from experiences that we think are relevant. PMID:22759503

[Inguinal hernia repair: results of randomized clinical trials and meta-analyses].

PubMed

Slim, K; Vons, C

2008-01-01

This evidence-based review of the literature aims to answer two questions regarding inguinal hernia repair: 1. should a prosthetic patch be used routinely? 2. Which approach is better - laparoscopic or open surgery? After a comprehensive search of electronic databases we retained only meta-analyses (n=14) and/or randomised clinical trials (n=4). Review of this literature suggests with a good level of evidence that prosthetic hernia repair is the gold standard; the laparoscopic approach has very few proven benefits and may involve more serious complications when performed outside expert centers. The role of laparoscopy for the repair of bilateral or recurrent hernias needs better evaluation.

Cognitive Support During High-Consequence Episodes of Care in Cardiovascular Surgery.

PubMed

Conboy, Heather M; Avrunin, George S; Clarke, Lori A; Osterweil, Leon J; Christov, Stefan C; Goldman, Julian M; Yule, Steven J; Zenati, Marco A

2017-03-01

Despite significant efforts to reduce preventable adverse events in medical processes, such events continue to occur at unacceptable rates. This paper describes a computer science approach that uses formal process modeling to provide situationally aware monitoring and management support to medical professionals performing complex processes. These process models represent both normative and non-normative situations, and are validated by rigorous automated techniques such as model checking and fault tree analysis, in addition to careful review by experts. Context-aware Smart Checklists are then generated from the models, providing cognitive support during high-consequence surgical episodes. The approach is illustrated with a case study in cardiovascular surgery.

Intelligent systems for human resources.

PubMed

Kline, K B

1988-11-01

An intelligent system contains knowledge about some domain; it has sophisticated decision-making processes and the ability to explain its actions. The most important aspect of an intelligent system is its ability to effectively interact with humans to teach or assist complex information processing. Two intelligent systems are Intelligent Tutoring Systems (ITs) and Expert Systems. The ITSs provide instruction to a student similar to a human tutor. The ITSs capture individual performance and tutor deficiencies. These systems consist of an expert module, which contains the knowledge or material to be taught; the student module, which contains a representation of the knowledge the student knows and does not know about the domain; and the instructional or teaching module, which selects specific knowledge to teach, the instructional strategy, and provides assistance to the student to tutor deficiencies. Expert systems contain an expert's knowledge about some domain and perform specialized tasks or aid a novice in the performance of certain tasks. The most important part of an expert system is the knowledge base. This knowledge base contains all the specialized and technical knowledge an expert possesses. For an expert system to interact effectively with humans, it must have the ability to explain its actions. Use of intelligent systems can have a profound effect on human resources. The ITSs can provide better training by tutoring on an individual basis, and the expert systems can make better use of human resources through job aiding and performing complex tasks. With increasing training requirements and "doing more with less," intelligent systems can have a positive effect on human resources.

Report Briefs: Publications of the Energy Division, Oak Ridge National Laboratory, 1999

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moser, C.I.

The Bureau of Labor Statistics (BLS) is responsible for collecting data to estimate price indices such as the Consumer Price Index (CPI). BLS accomplishes this task by sending field staff to places of business to price actual products. The field staff are given product checklists to help them determine whether the products found are comparable to products priced the previous month. Prices for noncomparable products are not included in the current month's price index calculations. A serious problem facing BLS is developing product checklists for dynamic product areas, new industries, and the service sector. It is difficult to keep checklistsmore » up to date and quite often simply to develop checklists for service industry products. Some people estimate that more than 50% of U.S. economic activity is not accounted for in the CPI. The objective it to provide the results of tests on a method for helping BLS staff build new product checklists quickly and efficiently. The domain chosen for studying the method was the telecommunications industry. The method developed by ORNL is based on behavioral science and knowledge-engineering principles. The method has ten steps, which include developing a sample of domain experts, asking experts to list products in the domain, culling the list of products to a manageable number, asking experts to group the remaining products, identifying product clusters using multidimensional scaling and cluster analysis, asking experts to compare pairs of products within clusters, and, finally, developing checklists with the comparison data. The method performed as expected. Several prototype checklists for products in the telecommunications domain were developed, including checklists for paging services, digital cell phones, web browsers, routers, and LAN modems. It was particularly difficult, however, to find experts to participate in the project. Attending a professional meeting and contacting experts from the conference's mailing list proved to be the best approach for this domain. The method has performed well in two domains: the telecommunications industry, as demonstrated in this project, and the PC software industry, as demonstrated in a previous project. It is recommended that the method be further tested in additional service industries, such as the nursing home industry. In addition, further attention needs to be devoted to developing procedures for the method to improve its cost and time efficiency. For example, if automated methods were used to collect information from the experts and if the experts could be assembled at one time, it could be possible to create prototype checklists in one day.« less

Usability of virtual-reality simulation training in obstetric ultrasonography: a prospective cohort study.

PubMed

Burden, C; Preshaw, J; White, P; Draycott, T J; Grant, S; Fox, R

2013-08-01

To assess the usability of virtual-reality (VR) simulation for obstetric ultrasound trainees. Twenty-six participants were recruited: 18 obstetric ultrasound trainees (with little formal ultrasonography training) and eight certified experts. All performed five sequential VR-simulated crown-rump length (CRL) scans in a single session and three repetitions of biparietal diameter (BPD), occipitofrontal diameter (OFD) and femur length (FL) measurements. Outcome measures included mean percentage deviation from target for all measurements. Time taken to perform each type of scan was recorded. The mean percentage difference for the first scan was significantly greater for the trainee group than for the expert group for BPD (P = 0.035), OFD (P = 0.010) and FL (P = 0.008) and for time taken for the first CRL (P < 0.001) and fetal biometry (including BPD, OFD and FL measurements) scan (P < 0.001), demonstrating that trainees were initially significantly less accurate and less efficient. Over subsequent scans, the trainees became more accurate for all measurements with a significant improvement shown for OFD and FL (P < 0.05). The time taken for trainees to complete CRL and fetal biometry scans decreased significantly (all P < 0.05) with repetition, to near-expert efficiency. All participants were able to use the simulator and produce clinically meaningful biometry results. With repetition, beginners quickly approached near-expert levels of accuracy and speed. These data demonstrate that obstetricians with minimal experience can improve their ultrasonographic skills with short-phase VR-simulation training. The speed of improvement suggests that VR simulation might be useful as a warm-up exercise before clinical training sessions in order to reduce their impact on clinical service. Copyright © 2013 ISUOG. Published by John Wiley & Sons Ltd.

Automated eddy current analysis of materials

NASA Technical Reports Server (NTRS)

Workman, Gary L.

1991-01-01

The use of eddy current techniques for characterizing flaws in graphite-based filament-wound cylindrical structures is described. A major emphasis was also placed upon incorporating artificial intelligence techniques into the signal analysis portion of the inspection process. Developing an eddy current scanning system using a commercial robot for inspecting graphite structures (and others) was a goal in the overall concept and is essential for the final implementation for the expert systems interpretation. Manual scans, as performed in the preliminary work here, do not provide sufficiently reproducible eddy current signatures to be easily built into a real time expert system. The expert systems approach to eddy current signal analysis requires that a suitable knowledge base exist in which correct decisions as to the nature of a flaw can be performed. A robotic workcell using eddy current transducers for the inspection of carbon filament materials with improved sensitivity was developed. Improved coupling efficiencies achieved with the E-probes and horseshoe probes are exceptional for graphite fibers. The eddy current supervisory system and expert system was partially developed on a MacIvory system. Continued utilization of finite element models for predetermining eddy current signals was shown to be useful in this work, both for understanding how electromagnetic fields interact with graphite fibers, and also for use in determining how to develop the knowledge base. Sufficient data was taken to indicate that the E-probe and the horseshoe probe can be useful eddy current transducers for inspecting graphite fiber components. The lacking component at this time is a large enough probe to have sensitivity in both the far and near field of a thick graphite epoxy component.

Determining clinical practice of expert physiotherapy for patients undergoing lumbar spinal fusion: a cross-sectional survey study.

PubMed

Janssen, Esther R C; Scheijen, Elle E M; van Meeteren, Nico L U; de Bie, Rob A; Lenssen, Anton F; Willems, Paul C; Hoogeboom, Thomas J

2016-05-01

To determine the content of current Dutch expert hospital physiotherapy practice for patients undergoing lumbar spinal fusion (LSF), to gain insight into expert-based clinical practice. At each hospital where LSF is performed, one expert physiotherapist received an e-mailed questionnaire, about pre- and postoperative physiotherapy and discharge after LSF. The level of uniformity in goals and interventions was graded on a scale from no uniformity (50-60 %) to very strong uniformity (91-100 %). LSF was performed at 34 of the 67 contacted hospitals. From those 34 hospitals, 28 (82 %) expert physiotherapists completed the survey. Twenty-one percent of the respondents saw patients preoperatively, generally to provide information. Stated postoperative goals and administered interventions focused mainly on performing transfers safely and keeping the patient informed. Outcome measures were scarcely used. There was no uniformity regarding advice on the activities of daily living. Dutch perioperative expert physiotherapy for patients undergoing LSF is variable and lacks structural outcome assessment. Studies evaluating the effectiveness of best-practice physiotherapy are warranted.

Construct validity and expert benchmarking of the haptic virtual reality dental simulator.

PubMed

Suebnukarn, Siriwan; Chaisombat, Monthalee; Kongpunwijit, Thanapohn; Rhienmora, Phattanapon

2014-10-01

The aim of this study was to demonstrate construct validation of the haptic virtual reality (VR) dental simulator and to define expert benchmarking criteria for skills assessment. Thirty-four self-selected participants (fourteen novices, fourteen intermediates, and six experts in endodontics) at one dental school performed ten repetitions of three mode tasks of endodontic cavity preparation: easy (mandibular premolar with one canal), medium (maxillary premolar with two canals), and hard (mandibular molar with three canals). The virtual instrument's path length was registered by the simulator. The outcomes were assessed by an expert. The error scores in easy and medium modes accurately distinguished the experts from novices and intermediates at the onset of training, when there was a significant difference between groups (ANOVA, p<0.05). The trend was consistent until trial 5. From trial 6 on, the three groups achieved similar scores. No significant difference was found between groups at the end of training. Error score analysis was not able to distinguish any group at the hard level of training. Instrument path length showed a difference in performance according to groups at the onset of training (ANOVA, p<0.05). This study established construct validity for the haptic VR dental simulator by demonstrating its discriminant capabilities between that of experts and non-experts. The experts' error scores and path length were used to define benchmarking criteria for optimal performance.

Understanding causality and uncertainty in volcanic observations: An example of forecasting eruptive activity on Soufrière Hills Volcano, Montserrat

NASA Astrophysics Data System (ADS)

Sheldrake, T. E.; Aspinall, W. P.; Odbert, H. M.; Wadge, G.; Sparks, R. S. J.

2017-07-01

Following a cessation in eruptive activity it is important to understand how a volcano will behave in the future and when it may next erupt. Such an assessment can be based on the volcano's long-term pattern of behaviour and insights into its current state via monitoring observations. We present a Bayesian network that integrates these two strands of evidence to forecast future eruptive scenarios using expert elicitation. The Bayesian approach provides a framework to quantify the magmatic causes in terms of volcanic effects (i.e., eruption and unrest). In October 2013, an expert elicitation was performed to populate a Bayesian network designed to help forecast future eruptive (in-)activity at Soufrière Hills Volcano. The Bayesian network was devised to assess the state of the shallow magmatic system, as a means to forecast the future eruptive activity in the context of the long-term behaviour at similar dome-building volcanoes. The findings highlight coherence amongst experts when interpreting the current behaviour of the volcano, but reveal considerable ambiguity when relating this to longer patterns of volcanism at dome-building volcanoes, as a class. By asking questions in terms of magmatic causes, the Bayesian approach highlights the importance of using short-term unrest indicators from monitoring data as evidence in long-term forecasts at volcanoes. Furthermore, it highlights potential biases in the judgements of volcanologists and identifies sources of uncertainty in terms of magmatic causes rather than scenario-based outcomes.

Expert Systems: Implications for the Diagnosis and Treatment of Learning Disabilities.

ERIC Educational Resources Information Center

Hofmeister, Alan M.; Lubke, Margaret M.

1988-01-01

The article examines characteristics and present or potential applications of expert systems technology for diagnosis and treatment of learning disabilities. Preliminary findings indicate that expert systems can perform as well as humans in specific areas, and that the process of organizing knowledge bases for expert systems helps clarify existing…

Toward Improved Maintenance Training Programs: The Potentials for Training and Aiding the Technician.

DTIC Science & Technology

1981-07-01

conditional, fault-isolation approach of the con- Data Base Requirements tent expert, photographs of normal and abnormal symp- The content-expert may...59 THE AUTOMATED INTERGRATION OF TRAINING AND AIDING INFORMATION FOR THE OPERATOR/TECHNICIAN Dr. Douglas Towne...Subsystem approach devel- until this Third Biennial Conference oped by the Air Force in the 1960’s for us to call a meeting devoted to integrate Human

An Iterative, Mixed Usability Approach Applied to the Telekit System from the Danish TeleCare North Trial

PubMed Central

Schaarup, Clara; Hejlesen, Ole Kristian

2016-01-01

Objective. The aim of the present study is to evaluate the usability of the telehealth system, coined Telekit, by using an iterative, mixed usability approach. Materials and Methods. Ten double experts participated in two heuristic evaluations (HE1, HE2), and 11 COPD patients attended two think-aloud tests. The double experts identified usability violations and classified them into Jakob Nielsen's heuristics. These violations were then translated into measurable values on a scale of 0 to 4 indicating degree of severity. In the think-aloud tests, COPD participants were invited to verbalise their thoughts. Results. The double experts identified 86 usability violations in HE1 and 101 usability violations in HE2. The majority of the violations were rated in the 0–2 range. The findings from the think-aloud tests resulted in 12 themes and associated examples regarding the usability of the Telekit system. The use of the iterative, mixed usability approach produced both quantitative and qualitative results. Conclusion. The iterative, mixed usability approach yields a strong result owing to the high number of problems identified in the tests because the double experts and the COPD participants focus on different aspects of Telekit's usability. This trial is registered with Clinicaltrials.gov, NCT01984840, November 14, 2013. PMID:27974888

An Iterative, Mixed Usability Approach Applied to the Telekit System from the Danish TeleCare North Trial.

PubMed

Lilholt, Pernille Heyckendorff; Schaarup, Clara; Hejlesen, Ole Kristian

2016-01-01

Objective . The aim of the present study is to evaluate the usability of the telehealth system, coined Telekit, by using an iterative, mixed usability approach. Materials and Methods . Ten double experts participated in two heuristic evaluations (HE1, HE2), and 11 COPD patients attended two think-aloud tests. The double experts identified usability violations and classified them into Jakob Nielsen's heuristics. These violations were then translated into measurable values on a scale of 0 to 4 indicating degree of severity. In the think-aloud tests, COPD participants were invited to verbalise their thoughts. Results . The double experts identified 86 usability violations in HE1 and 101 usability violations in HE2. The majority of the violations were rated in the 0-2 range. The findings from the think-aloud tests resulted in 12 themes and associated examples regarding the usability of the Telekit system. The use of the iterative, mixed usability approach produced both quantitative and qualitative results. Conclusion . The iterative, mixed usability approach yields a strong result owing to the high number of problems identified in the tests because the double experts and the COPD participants focus on different aspects of Telekit's usability. This trial is registered with Clinicaltrials.gov, NCT01984840, November 14, 2013.

A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series.

PubMed

Chambon, Stanislas; Galtier, Mathieu N; Arnal, Pierrick J; Wainrib, Gilles; Gramfort, Alexandre

2018-04-01

Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of the signal of a sleep stage, based on the visual inspection of signals such as electroencephalograms (EEGs), electrooculograms (EOGs), electrocardiograms, and electromyograms (EMGs). We introduce here the first deep learning approach for sleep stage classification that learns end-to-end without computing spectrograms or extracting handcrafted features, that exploits all multivariate and multimodal polysomnography (PSG) signals (EEG, EMG, and EOG), and that can exploit the temporal context of each 30-s window of data. For each modality, the first layer learns linear spatial filters that exploit the array of sensors to increase the signal-to-noise ratio, and the last layer feeds the learnt representation to a softmax classifier. Our model is compared to alternative automatic approaches based on convolutional networks or decisions trees. Results obtained on 61 publicly available PSG records with up to 20 EEG channels demonstrate that our network architecture yields the state-of-the-art performance. Our study reveals a number of insights on the spatiotemporal distribution of the signal of interest: a good tradeoff for optimal classification performance measured with balanced accuracy is to use 6 EEG with 2 EOG (left and right) and 3 EMG chin channels. Also exploiting 1 min of data before and after each data segment offers the strongest improvement when a limited number of channels are available. As sleep experts, our system exploits the multivariate and multimodal nature of PSG signals in order to deliver the state-of-the-art classification performance with a small computational cost.

TROUBLE 3: A fault diagnostic expert system for Space Station Freedom's power system

NASA Technical Reports Server (NTRS)

Manner, David B.

1990-01-01

Designing Space Station Freedom has given NASA many opportunities to develop expert systems that automate onboard operations of space based systems. One such development, TROUBLE 3, an expert system that was designed to automate the fault diagnostics of Space Station Freedom's electric power system is described. TROUBLE 3's design is complicated by the fact that Space Station Freedom's power system is evolving and changing. TROUBLE 3 has to be made flexible enough to handle changes with minimal changes to the program. Three types of expert systems were studied: rule-based, set-covering, and model-based. A set-covering approach was selected for TROUBLE 3 because if offered the needed flexibility that was missing from the other approaches. With this flexibility, TROUBLE 3 is not limited to Space Station Freedom applications, it can easily be adapted to handle any diagnostic system.

Structuring expert input for a knowledge-based approach to watershed condition assessment for the Northwest Forest Plan, USA

Treesearch

Sean N. Gordon; Gallo Kirsten

2011-01-01

Assessments of watershed condition for aquatic and riparian species often have to rely on expert opinion because of the complexity of establishing statistical relationships among the many factors involved. Such expert-based assessments can be difficult to document and apply consistently over time and space. We describe and reflect on the process of developing a...

An Approach for All in Pharmacy Informatics Education.

PubMed

Fox, Brent I; Flynn, Allen; Clauson, Kevin A; Seaton, Terry L; Breeden, Elizabeth

2017-03-25

Computerization is transforming health care. All clinicians are users of health information technology (HIT). Understanding fundamental principles of informatics, the field focused on information needs and uses, is essential if HIT is going to support improved patient outcomes. Informatics education for clinicians is a national priority. Additionally, some informatics experts are needed to bring about innovations in HIT. A common approach to pharmacy informatics education has been slow to develop. Meanwhile, accreditation standards for informatics in pharmacy education continue to evolve. A gap remains in the implementation of informatics education for all pharmacy students and it is unclear what expert informatics training should cover. In this article, we propose the first of two complementary approaches to informatics education in pharmacy: to incorporate fundamental informatics education into pharmacy curricula for all students. The second approach, to train those students interested in becoming informatics experts to design, develop, implement, and evaluate HIT, will be presented in a subsequent issue of the Journal .

Developing Wave Encyclopaedia based on Scientific Approach

NASA Astrophysics Data System (ADS)

Nurafifah, A.; Budi, A. S.; Siahaan, B. Z.

2017-09-01

Students have many difficulties in understanding to wave propagation. Such difficulties lead to misconceptions also in understanding sound, light, and electromagnetic wave. Meanwhile, students only use the text book as the learning resources. Whereas students need a more varied and interesting learning resources. This study aims to develop a wave encyclopaedia based on scientific approach as the learning resources that tested the feasibility and superiority. The method used is research by design. The steps are (1) analysing learner characteristic, (2) state objective, (3) select media and materials, (4) utilize materials, (5) requires learner participation, (6) evaluation and revision. The wave encyclopaedia is developed by applying the 5 components of a scientific approach that is, observing, questioning, experimenting, associating, and communicating. In this encyclopaedia also includes fun science activities and exciting recommended websites. The encyclopaedia has been validated by material experts, media experts, and learning experts. And then field trials are conducted to assess an impact on use. Overall the development of encyclopaedia based on scientific approach can enhance learning outcomes of students in high school.

An Approach for All in Pharmacy Informatics Education

PubMed Central

Flynn, Allen; Clauson, Kevin A.; Seaton, Terry L.; Breeden, Elizabeth

2017-01-01

Computerization is transforming health care. All clinicians are users of health information technology (HIT). Understanding fundamental principles of informatics, the field focused on information needs and uses, is essential if HIT is going to support improved patient outcomes. Informatics education for clinicians is a national priority. Additionally, some informatics experts are needed to bring about innovations in HIT. A common approach to pharmacy informatics education has been slow to develop. Meanwhile, accreditation standards for informatics in pharmacy education continue to evolve. A gap remains in the implementation of informatics education for all pharmacy students and it is unclear what expert informatics training should cover. In this article, we propose the first of two complementary approaches to informatics education in pharmacy: to incorporate fundamental informatics education into pharmacy curricula for all students. The second approach, to train those students interested in becoming informatics experts to design, develop, implement, and evaluate HIT, will be presented in a subsequent issue of the Journal. PMID:28381898

Knowledge-based fault diagnosis system for refuse collection vehicle

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tan, CheeFai; Juffrizal, K.; Khalil, S. N.

The refuse collection vehicle is manufactured by local vehicle body manufacturer. Currently; the company supplied six model of the waste compactor truck to the local authority as well as waste management company. The company is facing difficulty to acquire the knowledge from the expert when the expert is absence. To solve the problem, the knowledge from the expert can be stored in the expert system. The expert system is able to provide necessary support to the company when the expert is not available. The implementation of the process and tool is able to be standardize and more accurate. The knowledgemore » that input to the expert system is based on design guidelines and experience from the expert. This project highlighted another application on knowledge-based system (KBS) approached in trouble shooting of the refuse collection vehicle production process. The main aim of the research is to develop a novel expert fault diagnosis system framework for the refuse collection vehicle.« less

Pedagogical applications of cognitive research on musical improvisation

PubMed Central

Biasutti, Michele

2015-01-01

This paper presents a model for the implementation of educational activities involving musical improvisation that is based on a review of the literature on the psychology of music. Psychology of music is a complex field of research in which quantitative and qualitative methods have been employed involving participants ranging from novices to expert performers. The cognitive research has been analyzed to propose a pedagogical approach to the development of processes rather than products that focus on an expert’s use of improvisation. The intention is to delineate a reflective approach that goes beyond the mere instruction of some current practices of teaching improvisation in jazz pedagogy. The review highlights that improvisation is a complex, multidimensional act that involves creative and performance behaviors in real-time in addition to processes such as sensory and perceptual encoding, motor control, performance monitoring, and memory storage and recall. Educational applications for the following processes are outlined: anticipation, use of repertoire, emotive communication, feedback, and flow. These characteristics are discussed in relation to the design of a pedagogical approach to musical improvisation based on reflection and metacognition development. PMID:26029147

An expert system prototype for aiding in the development of software functional requirements for NASA Goddard's command management system: A case study and lessons learned

NASA Technical Reports Server (NTRS)

Liebowitz, Jay

1986-01-01

At NASA Goddard, the role of the command management system (CMS) is to transform general requests for spacecraft opeerations into detailed operational plans to be uplinked to the spacecraft. The CMS is part of the NASA Data System which entails the downlink of science and engineering data from NASA near-earth satellites to the user, and the uplink of command and control data to the spacecraft. Presently, it takes one to three years, with meetings once or twice a week, to determine functional requirements for CMS software design. As an alternative approach to the present technique of developing CMS software functional requirements, an expert system prototype was developed to aid in this function. Specifically, the knowledge base was formulated through interactions with domain experts, and was then linked to an existing expert system application generator called 'Knowledge Engineering System (Version 1.3).' Knowledge base development focused on four major steps: (1) develop the problem-oriented attribute hierachy; (2) determine the knowledge management approach; (3) encode the knowledge base; and (4) validate, test, certify, and evaluate the knowledge base and the expert system prototype as a whole. Backcasting was accomplished for validating and testing the expert system prototype. Knowledge refinement, evaluation, and implementation procedures of the expert system prototype were then transacted.

Inquiry-based problem solving in introductory physics

NASA Astrophysics Data System (ADS)

Koleci, Carolann

What makes problem solving in physics difficult? How do students solve physics problems, and how does this compare to an expert physicist's strategy? Over the past twenty years, physics education research has revealed several differences between novice and expert problem solving. The work of Chi, Feltovich, and Glaser demonstrates that novices tend to categorize problems based on surface features, while experts categorize according to theory, principles, or concepts1. If there are differences between how problems are categorized, then are there differences between how physics problems are solved? Learning more about the problem solving process, including how students like to learn and what is most effective, requires both qualitative and quantitative analysis. In an effort to learn how novices and experts solve introductory electricity problems, a series of in-depth interviews were conducted, transcribed, and analyzed, using both qualitative and quantitative methods. One-way ANOVA tests were performed in order to learn if there are any significant problem solving differences between: (a) novices and experts, (b) genders, (c) students who like to answer questions in class and those who don't, (d) students who like to ask questions in class and those who don't, (e) students employing an interrogative approach to problem solving and those who don't, and (f) those who like physics and those who dislike it. The results of both the qualitative and quantitative methods reveal that inquiry-based problem solving is prevalent among novices and experts, and frequently leads to the correct physics. These findings serve as impetus for the third dimension of this work: the development of Choose Your Own Adventure Physics(c) (CYOAP), an innovative teaching tool in physics which encourages inquiry-based problem solving. 1Chi, M., P. Feltovich, R. Glaser, "Categorization and Representation of Physics Problems by Experts and Novices", Cognitive Science, 5, 121--152 (1981).

A principled approach to the measurement of situation awareness in commercial aviation

NASA Technical Reports Server (NTRS)

Tenney, Yvette J.; Adams, Marilyn Jager; Pew, Richard W.; Huggins, A. W. F.; Rogers, William H.

1992-01-01

The issue of how to support situation awareness among crews of modern commercial aircraft is becoming especially important with the introduction of automation in the form of sophisticated flight management computers and expert systems designed to assist the crew. In this paper, cognitive theories are discussed that have relevance for the definition and measurement of situation awareness. These theories suggest that comprehension of the flow of events is an active process that is limited by the modularity of attention and memory constraints, but can be enhanced by expert knowledge and strategies. Three implications of this perspective for assessing and improving situation awareness are considered: (1) Scenario variations are proposed that tax awareness by placing demands on attention; (2) Experimental tasks and probes are described for assessing the cognitive processes that underlie situation awareness; and (3) The use of computer-based human performance models to augment the measures of situation awareness derived from performance data is explored. Finally, two potential example applications of the proposed assessment techniques are described, one concerning spatial awareness using wide field of view displays and the other emphasizing fault management in aircraft systems.

Motivation and emotion predict medical students' attention to computer-based feedback.

PubMed

Naismith, Laura M; Lajoie, Susanne P

2017-12-14

Students cannot learn from feedback unless they pay attention to it. This study investigated relationships between the personal factors of achievement goal orientations, achievement emotions, and attention to feedback in BioWorld, a computer environment for learning clinical reasoning. Novice medical students (N = 28) completed questionnaires to measure their achievement goal orientations and then thought aloud while solving three endocrinology patient cases and reviewing corresponding expert solutions. Questionnaires administered after each case measured participants' experiences of five feedback emotions: pride, relief, joy, shame, and anger. Attention to individual text segments of the expert solutions was modelled using logistic regression and the method of generalized estimating equations. Participants did not attend to all of the feedback that was available to them. Performance-avoidance goals and shame positively predicted attention to feedback, and performance-approach goals and relief negatively predicted attention to feedback. Aspects of how the feedback was displayed also influenced participants' attention. Findings are discussed in terms of their implications for educational theory as well as the design and use of computer learning environments in medical education.

A methodology for uncertainty quantification in quantitative technology valuation based on expert elicitation

NASA Astrophysics Data System (ADS)

Akram, Muhammad Farooq Bin

The management of technology portfolios is an important element of aerospace system design. New technologies are often applied to new product designs to ensure their competitiveness at the time they are introduced to market. The future performance of yet-to- be designed components is inherently uncertain, necessitating subject matter expert knowledge, statistical methods and financial forecasting. Estimates of the appropriate parameter settings often come from disciplinary experts, who may disagree with each other because of varying experience and background. Due to inherent uncertain nature of expert elicitation in technology valuation process, appropriate uncertainty quantification and propagation is very critical. The uncertainty in defining the impact of an input on performance parameters of a system makes it difficult to use traditional probability theory. Often the available information is not enough to assign the appropriate probability distributions to uncertain inputs. Another problem faced during technology elicitation pertains to technology interactions in a portfolio. When multiple technologies are applied simultaneously on a system, often their cumulative impact is non-linear. Current methods assume that technologies are either incompatible or linearly independent. It is observed that in case of lack of knowledge about the problem, epistemic uncertainty is the most suitable representation of the process. It reduces the number of assumptions during the elicitation process, when experts are forced to assign probability distributions to their opinions without sufficient knowledge. Epistemic uncertainty can be quantified by many techniques. In present research it is proposed that interval analysis and Dempster-Shafer theory of evidence are better suited for quantification of epistemic uncertainty in technology valuation process. Proposed technique seeks to offset some of the problems faced by using deterministic or traditional probabilistic approaches for uncertainty propagation. Non-linear behavior in technology interactions is captured through expert elicitation based technology synergy matrices (TSM). Proposed TSMs increase the fidelity of current technology forecasting methods by including higher order technology interactions. A test case for quantification of epistemic uncertainty on a large scale problem of combined cycle power generation system was selected. A detailed multidisciplinary modeling and simulation environment was adopted for this problem. Results have shown that evidence theory based technique provides more insight on the uncertainties arising from incomplete information or lack of knowledge as compared to deterministic or probability theory methods. Margin analysis was also carried out for both the techniques. A detailed description of TSMs and their usage in conjunction with technology impact matrices and technology compatibility matrices is discussed. Various combination methods are also proposed for higher order interactions, which can be applied according to the expert opinion or historical data. The introduction of technology synergy matrix enabled capturing the higher order technology interactions, and improvement in predicted system performance.

Comparison of display enhancement with intelligent decision-aiding

NASA Technical Reports Server (NTRS)

Kirlik, Alex; Markert, Wendy J.; Kossack, Merrick

1992-01-01

Currently, two main approaches exist for improving the human-machine interface component of a system in order to improve overall system performance, display enhancement and intelligent decision aiding. Each of these two approaches has its own set of advantages and disadvantages, as well as introduce its own set of additional performance problems. These characteristics should help identify which types of problem situations and domains are better aided by which type of strategy. The characteristic issues are described of these two decision aiding strategies. Then differences in expert and novice decision making are described in order to help determine whether a particular strategy may be better for a particular type of user. Finally, research is outlined to compare and contrast the two technologies, as well as to examine the interaction effects introduced by the different skill levels and the different methods for training operators.

Constructing a strategy map for banking institutions with key performance indicators of the balanced scorecard.

PubMed

Wu, Hung-Yi

2012-08-01

This study presents a structural evaluation methodology to link key performance indicators (KPIs) into a strategy map of the balanced scorecard (BSC) for banking institutions. Corresponding with the four BSC perspectives (finance, customer, internal business process, and learning and growth), the most important evaluation indicators of banking performance are synthesized from the relevant literature and screened by a committee of experts. The Decision Making Trial and Evaluation Laboratory (DEMATEL) method, a multiple criteria analysis tool, is then employed to determine the causal relationships between the KPIs, to identify the critical central and influential factors, and to establish a visualized strategy map with logical links to improve banking performance. An empirical application is provided as an example. According to the expert evaluations, the three most essential KPIs for banking performance are customer satisfaction, sales performance, and customer retention rate. The DEMATEL results demonstrate a clear road map to assist management in prioritizing the performance indicators and in focusing attention on the strategy-related activities of the crucial indicators. According to the constructed strategy map, management could better invest limited resources in the areas that need improvement most. Although these strategy maps of the BSC are not universal, the research results show that the presented approach is an objective and feasible way to construct strategy maps more justifiably. The proposed framework can be applicable to institutions in other industries as well. Copyright © 2011 Elsevier Ltd. All rights reserved.

[Significance of expert-guided groups for relatives in psychiatry].

PubMed

Plessen, U; Postzich, M; Wilkmann, M

1985-03-01

Psychiatric interest in relatives of patients was concentrated in the past on their pathogenetic and etiological influence on mental illness. The medical paradigma of mental illness did not account for relatives affliction in psychic disturbance of their family member. Against this a community care oriented approach involves relatives into psychiatric care, particularly under the aspects of coping strategies and rehabilitative sources. Practicability and effects of this approach were explored in expert-guided relative groups at the Psychiatric Hospital Gütersloh (FRG). Results indicated that relatives are concerned with a series of problems. Participating in relative groups facilitates coping with these problems. Expert-guided and relative centered groups were found helpful, discharging and encouraging for relatives.

Threat expert system technology advisor

NASA Technical Reports Server (NTRS)

Kurrasch, E. R.; Tripp, L. R.

1987-01-01

A prototype expert system was developed to determine the feasibility of using expert system technology to enhance the performance and survivability of helicopter pilots in a combat threat environment while flying NOE (Nap of the Earth) missions. The basis for the concept is the potential of using an Expert System Advisor to reduce the extreme overloading of the pilot who flies NOE mission below treetop level at approximately 40 knots while performing several other functions. The ultimate goal is to develop a Threat Expert System Advisor which provides threat information and advice that are better than even a highly experienced copilot. The results clearly show that the NOE pilot needs all the help in decision aiding and threat situation awareness that he can get. It clearly shows that heuristics are important and that an expert system for combat NOE helicopter missions can be of great help to the pilot in complex threat situations and in making decisions.

EMMA: The expert system for munition maintenance

NASA Technical Reports Server (NTRS)

Mullins, Barry E.

1988-01-01

Expert Missile Maintenance Aid (EMMA) is a first attempt to enhance maintenance of the tactical munition at the field and depot level by using artificial intelligence (AI) techniques. The ultimate goal of EMMA is to help a novice maintenance technician isolate and diagnose electronic, electromechanical, and mechanical equipment faults to the board/chassis level more quickly and consistently than the best human expert using the best currently available automatic test equipment (ATE). To this end, EMMA augments existing ATE with an expert system that captures the knowledge of design and maintenance experts. The EMMA program is described, including the evaluation of field-level expert system prototypes, the description of several study tasks performed during EMMA, and future plans for a follow-on program. This paper will briefly address several study tasks performed during EMMA. The paper concludes with a discussion of future plans for a follow-on program and other areas of concern.

Short report: the effect of expertise in hiking on recognition memory for mountain scenes.

PubMed

Kawamura, Satoru; Suzuki, Sae; Morikawa, Kazunori

2007-10-01

The nature of an expert memory advantage that does not depend on stimulus structure or chunking was examined, using more ecologically valid stimuli in the context of a more natural activity than previously studied domains. Do expert hikers and novice hikers see and remember mountain scenes differently? In the present experiment, 18 novice hikers and 17 expert hikers were presented with 60 photographs of scenes from hiking trails. These scenes differed in the degree of functional aspects that implied some action possibilities or dangers. The recognition test revealed that the memory performance of experts was significantly superior to that of novices for scenes with highly functional aspects. The memory performance for the scenes with few functional aspects did not differ between novices and experts. These results suggest that experts pay more attention to, and thus remember better, scenes with functional meanings than do novices.

Teaching community program clinicians motivational interviewing using expert and train-the-trainer strategies.

PubMed

Martino, Steve; Ball, Samuel A; Nich, Charla; Canning-Ball, Monica; Rounsaville, Bruce J; Carroll, Kathleen M

2011-02-01

The effectiveness of expert-led (EX) and train-the-trainer (TT) strategies was compared to a self-study approach (SS) for teaching clinicians motivational interviewing (MI). Twelve community treatment programs were assigned randomly to the three conditions. EX and TT conditions used skill-building workshops and three monthly supervision sessions guided by treatment integrity ratings, performance feedback and coaching techniques. Trainers in TT were first trained and certified in MI and then prepared carefully to deliver the workshops and supervise MI at their programs. Clinicians in SS only received the training materials. Licensed out-patient and residential addiction and mental health treatment programs in the US state of Connecticut were involved in the study. Ninety-two clinicians who provided addiction treatment within these programs and had limited experience with MI participated in the study. Primary outcomes were the clinicians' MI adherence and competence and the percentage of clinicians meeting clinical trial standards of MI performance. Assessments occurred at baseline, post-workshop, post-supervision and at 12-week follow-up. The study found EX and TT, in comparison to SS, improved clinicians' adherence and competence significantly, with higher percentages of clinicians reaching clinical trial standards of MI performance and few differences between EX and TT. This study supports the combined use of workshops and supervision to teach community program clinicians MI and suggests the train-the-trainer approach may be a feasible and effective strategy for disseminating empirically supported treatments. © 2010 The Authors, Addiction © 2010 Society for the Study of Addiction.

Aquifer Hydrogeologic Layer Zonation at the Hanford Site

DOE Office of Scientific and Technical Information (OSTI.GOV)

Savelieva-Trofimova, Elena A.; Kanevski, Mikhail; timonin, v.

2003-09-10

Sedimentary aquifer layers are characterized by spatial variability of hydraulic properties. Nevertheless, zones with similar values of hydraulic parameters (parameter zones) can be distinguished. This parameter zonation approach is an alternative to the analysis of spatial variation of the continuous hydraulic parameters. The parameter zonation approach is primarily motivated by the lack of measurements that would be needed for direct spatial modeling of the hydraulic properties. The current work is devoted to the problem of zonation of the Hanford formation, the uppermost sedimentary aquifer unit (U1) included in hydrogeologic models at the Hanford site. U1 is characterized by 5 zonesmore » with different hydraulic properties. Each sampled location is ascribed to a parameter zone by an expert. This initial classification is accompanied by a measure of quality (also indicated by an expert) that addresses the level of classification confidence. In the current study, the coneptual zonation map developed by an expert geologist was used as an a priori model. The parameter zonation problem was formulated as a multiclass classification task. Different geostatistical and machine learning algorithms were adapted and applied to solve this problem, including: indicator kriging, conditional simulations, neural networks of different architectures, and support vector machines. All methods were trained using additional soft information based on expert estimates. Regularization methods were used to overcome possible overfitting. The zonation problem was complicated because there were few samples for some zones (classes) and by the spatial non-stationarity of the data. Special approaches were developed to overcome these complications. The comparison of different methods was performed using qualitative and quantitative statistical methods and image analysis. We examined the correspondence of the results with the geologically based interpretation, including the reproduction of the spatial orientation of the different classes and the spatial correlation structure of the classes. The uncertainty of the classification task was examined using both probabilistic interpretation of the estimators and by examining the results of a set of stochastic realizations. Characterization of the classification uncertainty is the main advantage of the proposed methods.« less

Using collective expert judgements to evaluate quality measures of mass spectrometry images.

PubMed

Palmer, Andrew; Ovchinnikova, Ekaterina; Thuné, Mikael; Lavigne, Régis; Guével, Blandine; Dyatlov, Andrey; Vitek, Olga; Pineau, Charles; Borén, Mats; Alexandrov, Theodore

2015-06-15

Imaging mass spectrometry (IMS) is a maturating technique of molecular imaging. Confidence in the reproducible quality of IMS data is essential for its integration into routine use. However, the predominant method for assessing quality is visual examination, a time consuming, unstandardized and non-scalable approach. So far, the problem of assessing the quality has only been marginally addressed and existing measures do not account for the spatial information of IMS data. Importantly, no approach exists for unbiased evaluation of potential quality measures. We propose a novel approach for evaluating potential measures by creating a gold-standard set using collective expert judgements upon which we evaluated image-based measures. To produce a gold standard, we engaged 80 IMS experts, each to rate the relative quality between 52 pairs of ion images from MALDI-TOF IMS datasets of rat brain coronal sections. Experts' optional feedback on their expertise, the task and the survey showed that (i) they had diverse backgrounds and sufficient expertise, (ii) the task was properly understood, and (iii) the survey was comprehensible. A moderate inter-rater agreement was achieved with Krippendorff's alpha of 0.5. A gold-standard set of 634 pairs of images with accompanying ratings was constructed and showed a high agreement of 0.85. Eight families of potential measures with a range of parameters and statistical descriptors, giving 143 in total, were evaluated. Both signal-to-noise and spatial chaos-based measures performed highly with a correlation of 0.7 to 0.9 with the gold standard ratings. Moreover, we showed that a composite measure with the linear coefficients (trained on the gold standard with regularized least squares optimization and lasso) showed a strong linear correlation of 0.94 and an accuracy of 0.98 in predicting which image in a pair was of higher quality. The anonymized data collected from the survey and the Matlab source code for data processing can be found at: https://github.com/alexandrovteam/IMS_quality. © The Author 2015. Published by Oxford University Press.

Harvesting the Experts'"Secret Sauce" To Close the Performance Gap.

ERIC Educational Resources Information Center

Seidman, William; McCauley, Michael

2003-01-01

Explores the "secret sauce" that makes the difference between experts and less successful personnel. Indicates that the successful use of this plan can improve planning time, training time, and task performance time. (Author/LRW)

Incorporating expert judgments in utility evaluation of bacteroidales qPCR assays for microbial source tracking in a drinking water source.

PubMed

Åström, Johan; Pettersson, Thomas J R; Reischer, Georg H; Norberg, Tommy; Hermansson, Malte

2015-02-03

Several assays for the detection of host-specific genetic markers of the order Bacteroidales have been developed and used for microbial source tracking (MST) in environmental waters. It is recognized that the source-sensitivity and source-specificity are unknown and variable when introducing these assays in new geographic regions, which reduces their reliability and use. A Bayesian approach was developed to incorporate expert judgments with regional assay sensitivity and specificity assessments in a utility evaluation of a human and a ruminant-specific qPCR assay for MST in a drinking water source. Water samples from Lake Rådasjön were analyzed for E. coli, intestinal enterococci and somatic coliphages through cultivation and for human (BacH) and ruminant-specific (BacR) markers through qPCR assays. Expert judgments were collected regarding the probability of human and ruminant fecal contamination based on fecal indicator organism data and subjective information. Using Bayes formula, the conditional probability of a true human or ruminant fecal contamination given the presence of BacH or BacR was determined stochastically from expert judgments and regional qPCR assay performance, using Beta distributions to represent uncertainties. A web-based computational tool was developed for the procedure, which provides a measure of confidence to findings of host-specific markers and demonstrates the information value from these assays.

Incorporating Expert Judgments in Utility Evaluation of Bacteroidales qPCR Assays for Microbial Source Tracking in a Drinking Water Source

PubMed Central

Åström, Johan; Pettersson, Thomas J. R.; Reischer, Georg H.; Norberg, Tommy; Hermansson, Malte

2017-01-01

Several assays for the detection of host-specific genetic markers of the order Bacteroidales have been developed and used for microbial source tracking (MST) in environmental waters. It is recognized that the source-sensitivity and source-specificity are unknown and variable when introducing these assays in new geographic regions, which reduces their reliability and use. A Bayesian approach was developed to incorporate expert judgments with regional assay sensitivity and specificity assessments in a utility evaluation of a human and a ruminant-specific qPCR assay for MST in a drinking water source. Water samples from Lake Rådasjön were analyzed for E. coli, intestinal enterococci and somatic coliphages through cultivation and for human (BacH) and ruminant-specific (BacR) markers through qPCR assays. Expert judgments were collected regarding the probability of human and ruminant fecal contamination based on fecal indicator organism data and subjective information. Using Bayes formula, the conditional probability of a true human or ruminant fecal contamination given the presence of BacH or BacR was determined stochastically from expert judgments and regional qPCR assay performance, using Beta distributions to represent uncertainties. A web-based computational tool was developed for the procedure, which provides a measure of confidence to findings of host-specific markers and demonstrates the information value from these assays. PMID:25545113

Cost estimation: An expert-opinion approach. [cost analysis of research projects using the Delphi method (forecasting)

NASA Technical Reports Server (NTRS)

Buffalano, C.; Fogleman, S.; Gielecki, M.

1976-01-01

A methodology is outlined which can be used to estimate the costs of research and development projects. The approach uses the Delphi technique a method developed by the Rand Corporation for systematically eliciting and evaluating group judgments in an objective manner. The use of the Delphi allows for the integration of expert opinion into the cost-estimating process in a consistent and rigorous fashion. This approach can also signal potential cost-problem areas. This result can be a useful tool in planning additional cost analysis or in estimating contingency funds. A Monte Carlo approach is also examined.

A Computer Program which Uses an Expert Systems Approach to Identifying Minerals.

ERIC Educational Resources Information Center

Hart, Allan Bruce; And Others

1988-01-01

Described is a mineral identification program which uses a shell system for creating expert systems of a classification nature. Discusses identification of minerals in hand specimens, thin sections, and polished sections of rocks. (Author/CW)

Relative risk analysis of the use of radiation-emitting medical devices: A preliminary application

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jones, E.D.

This report describes the development of a risk analysis approach for evaluating the use of radiation-emitting medial devices. This effort was performed by Lawrence Livermore National Laboratory for the US Nuclear Regulatory Commission (NRC). The assessment approach has bee applied to understand the risks in using the Gamma Knife, a gamma irradiation therapy device. This effort represents an initial step to evaluate the potential role of risk analysis for developing regulations and quality assurance requirements in the use of nuclear medical devices. The risk approach identifies and assesses the most likely risk contributors and their relative importance for the medicalmore » system. The approach uses expert screening techniques and relative risk profiling to incorporate the type, quality, and quantity of data available and to present results in an easily understood form.« less

The Impact of Moderate and High Intensity Total Body Fatigue on Passing Accuracy in Expert and Novice Basketball Players

PubMed Central

Lyons, Mark; Al-Nakeeb, Yahya; Nevill, Alan

2006-01-01

Despite the acknowledged importance of fatigue on performance in sport, ecologically sound studies investigating fatigue and its effects on sport-specific skills are surprisingly rare. The aim of this study was to investigate the effect of moderate and high intensity total body fatigue on passing accuracy in expert and novice basketball players. Ten novice basketball players (age: 23.30 ± 1.05 yrs) and ten expert basketball players (age: 22.50 ± 0.41 yrs) volunteered to participate in the study. Both groups performed the modified AAHPERD Basketball Passing Test under three different testing conditions: rest, moderate intensity and high intensity total body fatigue. Fatigue intensity was established using a percentage of the maximal number of squat thrusts performed by the participant in one minute. ANOVA with repeated measures revealed a significant (F 2,36 = 5.252, p = 0.01) level of fatigue by level of skill interaction. On examination of the mean scores it is clear that following high intensity total body fatigue there is a significant detriment in the passing performance of both novice and expert basketball players when compared to their resting scores. Fundamentally however, the detrimental impact of fatigue on passing performance is not as steep in the expert players compared to the novice players. The results suggest that expert or skilled players are better able to cope with both moderate and high intensity fatigue conditions and maintain a higher level of performance when compared to novice players. The findings of this research therefore, suggest the need for trainers and conditioning coaches in basketball to include moderate, but particularly high intensity exercise into their skills sessions. This specific training may enable players at all levels of the game to better cope with the demands of the game on court and maintain a higher standard of play. Key Points Aim: to investigate the effect of moderate and high intensity total body fatigue on basketball-passing accuracy in expert and novice basketball players. Fatigue intensity was set as a percentage of the maximal number of squat thrusts performed by the participant in one minute. ANOVA with repeated measures revealed a significant level of fatigue by level of skill interaction. Despite a significant detriment in passing-performance in both novice and expert players following high intensity total body fatigue, this detriment was not as steep in the expert players when compared to the novice players PMID:24259994

Individual and Joint Expert Judgments as Reference Standards in Artifact Detection

PubMed Central

Verduijn, Marion; Peek, Niels; de Keizer, Nicolette F.; van Lieshout, Erik-Jan; de Pont, Anne-Cornelie J.M.; Schultz, Marcus J.; de Jonge, Evert; de Mol, Bas A.J.M.

2008-01-01

Objective To investigate the agreement among clinical experts in their judgments of monitoring data with respect to artifacts, and to examine the effect of reference standards that consist of individual and joint expert judgments on the performance of artifact filters. Design Individual judgments of four physicians, a majority vote judgment, and a consensus judgment were obtained for 30 time series of three monitoring variables: mean arterial blood pressure (ABPm), central venous pressure (CVP), and heart rate (HR). The individual and joint judgments were used to tune three existing automated filtering methods and to evaluate the performance of the resulting filters. Measurements The interrater agreement was calculated in terms of positive specific agreement (PSA). The performance of the artifact filters was quantified in terms of sensitivity and positive predictive value (PPV). Results PSA values between 0.33 and 0.85 were observed among clinical experts in their selection of artifacts, with relatively high values for CVP data. Artifact filters developed using judgments of individual experts were found to moderately generalize to new time series and other experts; sensitivity values ranged from 0.40 to 0.60 for ABPm and HR filters (PPV: 0.57–0.84), and from 0.63 to 0.80 for CVP filters (PPV: 0.71–0.86). A higher performance value for the filters was found for the three variable types when joint judgments were used for tuning the filtering methods. Conclusion Given the disagreement among experts in their individual judgment of monitoring data with respect to artifacts, the use of joint reference standards obtained from multiple experts is recommended for development of automatic artifact filters. PMID:18096912

Real Time Data System (RTDS)

NASA Technical Reports Server (NTRS)

Muratore, John F.

1991-01-01

Lessons learned from operational real time expert systems are examined. The basic system architecture is discussed. An expert system is any software that performs tasks to a standard that would normally require a human expert. An expert system implies knowledge contained in data rather than code. And an expert system implies the use of heuristics as well as algorithms. The 15 top lessons learned by the operation of a real time data system are presented.

The importance of expert feedback during endovascular simulator training.

PubMed

Boyle, Emily; O'Keeffe, Dara A; Naughton, Peter A; Hill, Arnold D K; McDonnell, Ciaran O; Moneley, Daragh

2011-07-01

Complex endovascular skills are difficult to obtain in the clinical environment. Virtual reality (VR) simulator training is a valuable addition to current training curricula, but is there a benefit in the absence of expert trainers? Eighteen endovascular novices performed a renal artery angioplasty/stenting (RAS) on the Vascular Interventional Surgical Trainer simulator. They were randomized into three groups: Group A (n = 6, control), no performance feedback; Group B (n = 6, nonexpert feedback), feedback after every procedure from a nonexpert facilitator; and Group C (n = 6, expert feedback), feedback after every procedure from a consultant vascular surgeon. Each trainee completed RAS six times. Simulator-measured performance metrics included procedural and fluoroscopy time, contrast volume, accuracy of balloon placement, and handling errors. Clinical errors were also measured by blinded video assessment. Data were analyzed using SPSS version 15. A clear learning curve was observed across the six trials. There were no significant differences between the three groups for the general performance metrics, but Group C made fewer errors than Groups A (P = .009) or B (P = .004). Video-based error assessment showed that Groups B and C performed better than Group A (P = .002 and P = .000, respectively). VR simulator training for novices can significantly improve general performance in the absence of expert trainers. Procedure-specific qualitative metrics are improved with expert feedback, but nonexpert facilitators can also enhance the quality of training and may represent a valuable alternative to expert clinical faculty. Copyright © 2011 Society for Vascular Surgery. Published by Mosby, Inc. All rights reserved.

Development of a component centered fault monitoring and diagnosis knowledge based system for space power system

NASA Technical Reports Server (NTRS)

Lee, S. C.; Lollar, Louis F.

1988-01-01

The overall approach currently being taken in the development of AMPERES (Autonomously Managed Power System Extendable Real-time Expert System), a knowledge-based expert system for fault monitoring and diagnosis of space power systems, is discussed. The system architecture, knowledge representation, and fault monitoring and diagnosis strategy are examined. A 'component-centered' approach developed in this project is described. Critical issues requiring further study are identified.

Expert systems for MSFC power systems

NASA Technical Reports Server (NTRS)

Weeks, David J.

1988-01-01

Future space vehicles and platforms including Space Station will possess complex power systems. These systems will require a high level of autonomous operation to allow the crew to concentrate on mission activities and to limit the number of ground support personnel to a reasonable number. The Electrical Power Branch at NASA-Marshall is developing advanced automation approaches which will enable the necessary levels of autonomy. These approaches include the utilization of knowledge based or expert systems.

Implementing a Sleep Health Education and Sleep Disorders Screening Program in Fire Departments

PubMed Central

Barger, Laura K.; O’Brien, Conor S.; Rajaratnam, Shantha M.W.; Qadri, Salim; Sullivan, Jason P.; Wang, Wei; Czeisler, Charles A.; Lockley, Steven W.

2016-01-01

Objective: The objective of this study is to compare three methods of administering a sleep health program (SHP) in fire departments. Methods: An SHP, comprising sleep health education and screening for common sleep disorders, was implemented in eight fire departments using three approaches: expert-led, train-the-trainer, and online. Participation rates, knowledge assessments, surveys, and focus group interviews were analyzed to assess the reach and effectiveness of the methodologies. Results: The Expert-led SHP had the highest participation rate, greatest improvement in knowledge scores, and prompted more firefighters to seek clinical sleep disorder evaluations (41%) than the other approaches (20 to 25%). Forty-two percent of focus group participants reported changing their sleep behaviors. Conclusion: All approaches yielded reasonable participation rates, but expert-led programs had the greatest reach and effectiveness in educating and screening firefighters for sleep disorders. PMID:27035103

Implementing a Sleep Health Education and Sleep Disorders Screening Program in Fire Departments: A Comparison of Methodology.

PubMed

Barger, Laura K; O'Brien, Conor S; Rajaratnam, Shantha M W; Qadri, Salim; Sullivan, Jason P; Wang, Wei; Czeisler, Charles A; Lockley, Steven W

2016-06-01

The objective of this study is to compare three methods of administering a sleep health program (SHP) in fire departments. An SHP, comprising sleep health education and screening for common sleep disorders, was implemented in eight fire departments using three approaches: expert-led, train-the-trainer, and online. Participation rates, knowledge assessments, surveys, and focus group interviews were analyzed to assess the reach and effectiveness of the methodologies. The Expert-led SHP had the highest participation rate, greatest improvement in knowledge scores, and prompted more firefighters to seek clinical sleep disorder evaluations (41%) than the other approaches (20 to 25%). Forty-two percent of focus group participants reported changing their sleep behaviors. All approaches yielded reasonable participation rates, but expert-led programs had the greatest reach and effectiveness in educating and screening firefighters for sleep disorders.

USE OF EXPERT RATINGS AS SAMPLING STRATA FOR A MORE COST-EFFECTIVE PROBABILITY SAMPLE OF A RARE POPULATION

PubMed Central

McCaffrey, Daniel; Perlman, Judith; Marshall, Grant N.; Hambarsoomians, Katrin

2010-01-01

We consider situations in which externally observable characteristics allow experts to quickly categorize individual households as likely or unlikely to contain a member of a rare target population. This classification can form the basis of disproportionate stratified sampling such that households classified as “unlikely” are sampled at a lower rate than those classified as “likely,” thereby reducing screening costs. Design weights account for this approach and allow unbiased estimates for the target population. We demonstrate that with sensitivity and specificity of expert classification at least 70%, and ideally at least 80%, our approach can economically increase effective sample size for a rare population. We develop heuristics for implementing this approach and demonstrate that sensitivity drives design effects and screening costs whereas specificity only drives the latter. We demonstrate that the potential gains from this approach increase as the target population becomes rarer. We further show that for most applications, unlikely strata should be sampled at 1/6 to ½ the rate of likely strata. This approach was applied to a survey of Cambodian immigrants in which the 82% of households rated “unlikely” were sampled at ¼ the rate as “likely” households, reducing screening from 9.4 to 4.0 approaches per complete. Sensitivity and specificity were 86% and 91% respectively. Weighted estimation had a design effect of 1.26 so screening costs per effective sample size were reduced 47%. We also note that in this instance, expert classification appeared to be uncorrelated with survey outcomes of interest among eligibles. PMID:20936050

The development of an intelligent user interface for NASA's scientific databases

NASA Technical Reports Server (NTRS)

Campbell, William J.; Roelofs, Larry H.

1986-01-01

The National Space Science Data Center (NSSDC) has initiated an Intelligent Data Management (IDM) research effort which has as one of its components, the development of an Intelligent User Interface (IUI). The intent of the IUI effort is to develop a friendly and intelligent user interface service that is based on expert systems and natural language processing technologies. This paper presents the design concepts, development approach and evaluation of performance of a prototype Intelligent User Interface Subsystem (IUIS) supporting an operational database.

ANALYTiC: An Active Learning System for Trajectory Classification.

PubMed

Soares Junior, Amilcar; Renso, Chiara; Matwin, Stan

2017-01-01

The increasing availability and use of positioning devices has resulted in large volumes of trajectory data. However, semantic annotations for such data are typically added by domain experts, which is a time-consuming task. Machine-learning algorithms can help infer semantic annotations from trajectory data by learning from sets of labeled data. Specifically, active learning approaches can minimize the set of trajectories to be annotated while preserving good performance measures. The ANALYTiC web-based interactive tool visually guides users through this annotation process.

Development of an expert system for the simulation model for casting metal substructure of a metal-ceramic crown design.

PubMed

Matin, Ivan; Hadzistevic, Miodrag; Vukelic, Djordje; Potran, Michal; Brajlih, Tomaz

2017-07-01

Nowadays, the integrated CAD/CAE systems are favored solutions for the design of simulation models for casting metal substructures of metal-ceramic crowns. The worldwide authors have used different approaches to solve the problems using an expert system. Despite substantial research progress in the design of experts systems for the simulation model design and manufacturing have insufficiently considered the specifics of casting in dentistry, especially the need for further CAD, RE, CAE for the estimation of casting parameters and the control of the casting machine. The novel expert system performs the following: CAD modeling of the simulation model for casting, fast modeling of gate design, CAD eligibility and cast ability check of the model, estimation and running of the program code for the casting machine, as well as manufacturing time reduction of the metal substructure. The authors propose an integration method using common data model approach, blackboard architecture, rule-based reasoning and iterative redesign method. Arithmetic mean roughness values was determinated with constant Gauss low-pass filter (cut-off length of 2.5mm) according to ISO 4287 using Mahr MARSURF PS1. Dimensional deviation between the designed model and manufactured cast was determined using the coordinate measuring machine Zeiss Contura G2 and GOM Inspect software. The ES allows for obtaining the castings derived roughness grade number N7. The dimensional deviation between the simulation model of the metal substructure and the manufactured cast is 0.018mm. The arithmetic mean roughness values measured on the casting substructure are from 1.935µm to 2.778µm. The realized developed expert system with the integrated database is fully applicable for the observed hardware and software. Values of the arithmetic mean roughness and dimensional deviation indicate that casting substructures are surface quality, which is more than enough and useful for direct porcelain veneering. The manufacture of the substructure shows that the proposed ES allows the improvement of the design process while reducing the manufacturing time. Copyright © 2017 Elsevier B.V. All rights reserved.

Evidential Reasoning in Expert Systems for Image Analysis.

DTIC Science & Technology

1985-02-01

techniques to image analysis (IA). There is growing evidence that these techniques offer significant improvements in image analysis , particularly in the...2) to provide a common framework for analysis, (3) to structure the ER process for major expert-system tasks in image analysis , and (4) to identify...approaches to three important tasks for expert systems in the domain of image analysis . This segment concluded with an assessment of the strengths

A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification

PubMed Central

Wang, Jing; Webb-Robertson, Bobbie-Jo M.; Matzke, Melissa M.; Varnum, Susan M.; Brown, Joseph N.; Riensche, Roderick M.; Adkins, Joshua N.; Jacobs, Jon M.; Hoidal, John R.; Scholand, Mary Beth; Pounds, Joel G.; Blackburn, Michael R.; Rodland, Karin D.; McDermott, Jason E.

2013-01-01

Background. The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. Objective. To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme. Methods. The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expert knowledge was integrated into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection. Results. The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort. Conclusions. Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification. PMID:24223463

A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jing; Webb-Robertson, Bobbie-Jo M.; Matzke, Melissa M.

2013-10-01

Background. The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. Objective. To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme. Methods. The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expert knowledge was integratedmore » into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection. Results. The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort. Conclusions. Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification.« less

Adapting the bioblitz to meet conservation needs.

PubMed

Parker, Sophie S; Pauly, Gregory B; Moore, James; Fraga, Naomi S; Knapp, John J; Principe, Zachary; Brown, Brian V; Randall, John M; Cohen, Brian S; Wake, Thomas A

2018-03-01

When conservation strategies require new, field-based information, practitioners must find the best ways to rapidly deliver high-quality survey data. To address this challenge, several rapid-assessment approaches have been developed since the early 1990s. These typically involve large areas, take many months to complete, and are not appropriate when conservation-relevant survey data are urgently needed for a specific locale. In contrast, bioblitzes are designed for quick collection of site-specific survey data. Although bioblitzes are commonly used to achieve educational or public-engagement goals, conservation practitioners are increasingly using a modified bioblitz approach to generate conservation-relevant data while simultaneously enhancing research capacity and building working partnerships focused on conservation concerns. We term these modified events expert bioblitzes. Several expert bioblitzes have taken place on lands of conservation concern in Southern California and have involved collaborative efforts of government agencies, nonprofit organizations, botanic gardens, museums, and universities. The results of expert bioblitzes directly informed on-the-ground conservation and decision-making; increased capacity for rapid deployment of expert bioblitzes in the future; and fostered collaboration and communication among taxonomically and institutionally diverse experts. As research and conservation funding becomes increasingly scarce, expert bioblitzes can play an increasingly important role in biodiversity conservation. © 2018 The Authors. Conservation Biology published by Wiley Periodicals, Inc. on behalf of Society for Conservation Biology.

Nicotine replacement therapy decision based on fuzzy multi-criteria analysis

NASA Astrophysics Data System (ADS)

Tarmudi, Zamali; Matmali, Norfazillah; Abdullah, Mohd Lazim

2017-08-01

It has been observed that Nicotine Replacement Therapy (NRT) is one of the alternatives to control and reduce smoking addiction among smokers. Since the decision to choose the best NRT alternative involves uncertainty, ambiguity factors and diverse input datasets, thus, this paper proposes a fuzzy multi-criteria analysis (FMA) to overcome these issues. It focuses on how the fuzzy approach can unify the diversity of datasets based on NRT's decision-making problem. The analysis done employed the advantage of the cost-benefit criterion to unify the mixture of dataset input. The performance matrix was utilised to derive the performance scores. An empirical example regarding the NRT's decision-making problem was employed to illustrate the proposed approach. Based on the calculations, this analytical approach was found to be highly beneficial in terms of usability. It was also very applicable and efficient in dealing with the mixture of input datasets. Hence, the decision-making process can easily be used by experts and patients who are interested to join the therapy/cessation program.

Ethical Expert Systems

PubMed Central

Victoroff, Michael S.

1985-01-01

The title is a double entendre. The discussion approaches expert systems from two directions: “What ethical hazards are created by expert systems in medicine?” and “Would it be ethical to design an expert system for solving problems in bioethics?” Computers present new ethical problems to society, some of which are unprecedented. These can be categorized under several rubrics. The paper describes a rudimentary scheme for understanding ethical issues raised by computers, in general, and medical expert systems, in particular. It focuses on bioethical implications of AI in medicine; explores norms, assumptions and taboos; and highlights certain ethical pitfalls. Principles are elucidated, for building ethically sound systems. Finally, a proposal is discussed, for the design of an expert system for moral problem solving, and the ethical implications of this notion are analyzed.

Outcomes of Percutaneous Coronary Interventions for Chronic Total Occlusion Performed by Highly Experienced Japanese Specialists: The First Report From the Japanese CTO-PCI Expert Registry.

PubMed

Suzuki, Yoriyasu; Tsuchikane, Etsuo; Katoh, Osamu; Muramatsu, Toshiya; Muto, Makoto; Kishi, Koichi; Hamazaki, Yuji; Oikawa, Yuji; Kawasaki, Tomohiro; Okamura, Atsunori

2017-11-13

This report describes the registry and presents an initial analysis of outcomes for the different PCI approaches taken by the specialists. Strategies for percutaneous coronary intervention (PCI) for chronic total occlusion (CTO) are complex. The Japanese Board of CTO Interventional Specialists has developed a prospective, nonrandomized registry of patients undergoing CTO-PCIs performed by 41 highly experienced Japanese specialists. Over the study period of January 2014 to December 2015, the registry included 2,846 consecutive CTO-PCI cases undertaken in Japan. The authors compared clinical outcomes between the different PCI approaches, following the intention-to-treat principle. The overall technical success rate of the procedures was 89.9%. The specialists frequently chose a retrograde approach as the primary CTO-PCI strategy (in 27.8% of cases). The technical success rate of the primary antegrade approach was significantly better than that of the primary retrograde approach (91.0% vs. 87.3%; p < 0.0001). The technical success rate decreased to 78.0% with the rescue retrograde approach. Parallel guidewire crossing and intravascular ultrasound-guided wire crossing were performed after guidewire escalation during antegrade CTO-PCI with a high technical success rate (75.0% to 88.9%). Severe lesion calcification was a strong predictor of failed CTO-PCI. CTO-PCI performed by highly experienced specialists achieved a high technical success rate. Copyright © 2017 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Fuzzy ontologies for semantic interpretation of remotely sensed images

NASA Astrophysics Data System (ADS)

Djerriri, Khelifa; Malki, Mimoun

2015-10-01

Object-based image classification consists in the assignment of object that share similar attributes to object categories. To perform such a task the remote sensing expert uses its personal knowledge, which is rarely formalized. Ontologies have been proposed as solution to represent domain knowledge agreed by domain experts in a formal and machine readable language. Classical ontology languages are not appropriate to deal with imprecision or vagueness in knowledge. Fortunately, Description Logics for the semantic web has been enhanced by various approaches to handle such knowledge. This paper presents the extension of the traditional ontology-based interpretation with fuzzy ontology of main land-cover classes in Landsat8-OLI scenes (vegetation, built-up areas, water bodies, shadow, clouds, forests) objects. A good classification of image objects was obtained and the results highlight the potential of the method to be replicated over time and space in the perspective of transferability of the procedure.

Semi-Supervised Learning to Identify UMLS Semantic Relations.

PubMed

Luo, Yuan; Uzuner, Ozlem

2014-01-01

The UMLS Semantic Network is constructed by experts and requires periodic expert review to update. We propose and implement a semi-supervised approach for automatically identifying UMLS semantic relations from narrative text in PubMed. Our method analyzes biomedical narrative text to collect semantic entity pairs, and extracts multiple semantic, syntactic and orthographic features for the collected pairs. We experiment with seeded k-means clustering with various distance metrics. We create and annotate a ground truth corpus according to the top two levels of the UMLS semantic relation hierarchy. We evaluate our system on this corpus and characterize the learning curves of different clustering configuration. Using KL divergence consistently performs the best on the held-out test data. With full seeding, we obtain macro-averaged F-measures above 70% for clustering the top level UMLS relations (2-way), and above 50% for clustering the second level relations (7-way).

Genetic algorithm based fuzzy control of spacecraft autonomous rendezvous

NASA Technical Reports Server (NTRS)

Karr, C. L.; Freeman, L. M.; Meredith, D. L.

1990-01-01

The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy logic based expert systems have been used successfully for controlling a number of physical systems, the selection of acceptable fuzzy membership functions has generally been a subjective decision. High performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, genetic algorithms are potentially an effective and structured approach for learning fuzzy membership functions.

Confirmatory Factor Analysis Alternative: Free, Accessible CBID Software.

PubMed

Bott, Marjorie; Karanevich, Alex G; Garrard, Lili; Price, Larry R; Mudaranthakam, Dinesh Pal; Gajewski, Byron

2018-02-01

New software that performs Classical and Bayesian Instrument Development (CBID) is reported that seamlessly integrates expert (content validity) and participant data (construct validity) to produce entire reliability estimates with smaller sample requirements. The free CBID software can be accessed through a website and used by clinical investigators in new instrument development. Demonstrations are presented of the three approaches using the CBID software: (a) traditional confirmatory factor analysis (CFA), (b) Bayesian CFA using flat uninformative prior, and (c) Bayesian CFA using content expert data (informative prior). Outcomes of usability testing demonstrate the need to make the user-friendly, free CBID software available to interdisciplinary researchers. CBID has the potential to be a new and expeditious method for instrument development, adding to our current measurement toolbox. This allows for the development of new instruments for measuring determinants of health in smaller diverse populations or populations of rare diseases.

[Revision of Quality Indicators for the Endoscopy Quality Improvement Program of the National Cancer Screening Program in Korea].

PubMed

Min, Jun Ki; Cha, Jae Myung; Cho, Yu Kyung; Kim, Jie Hyun; Yoon, Soon Man; Im, Jong Pil; Jung, Yunho; Moon, Jeong Seop; Kim, Jin Oh; Jeen, Yoon Tae

2018-05-25

Gastroscopy and colonoscopy are widely used for the early diagnosis of stomach and colorectal cancer. The present revision integrates recent data regarding previous quality indicators and novel indicators suggested for gastroscopy and colonoscopy procedures for the National Cancer Screening Program in Korea. The new indicators, developed by the Quality Improvement Committee of the Korean Society for Gastrointestinal Endoscopy, vary in the level of supporting evidence, and most are based solely on expert opinion. Updated indicators validated by clinical research were prioritized, but were chosen by expert consensus when such studies were absent. The resultant quality indicators were graded according to the levels of consensus and recommendations. The updated indicators will provide a relevant guideline for high-quality endoscopy. The future direction of quality indicator development should include relevant outcome measures and an evidence-based approach to support proposed performance targets.

Traffic simulation in regional modeling : application to interstate infrastructure near the Toledo sea port.

DOT National Transportation Integrated Search

2012-06-01

A small team of university-based transportation system experts and simulation experts has been : assembled to develop, test, and apply an approach to assessing road infrastructure capacity using : micro traffic simulation supported by publically avai...

Employee assistance programs: history and program description.

PubMed

Gilbert, B

1994-10-01

1. The history and development of Employee Assistance Programs (EAPs) can be traced back to the 1800s. There are currently over 10,000 EAPs in the United States. 2. Standards for program accreditation and counselor certification have been established for EAPs. The "core technology of Employee Assistance Programs" includes identification of behavioural problems based on job performance issues, expert consultation with supervisors, appropriate use of constructive confrontation, microlinkages with treatment providers and resources, macrolinkages between providers, resources, and work organizations, focus on substance abuse, and evaluation of employee success based on job performance. 3. Some EAPs take a broad brush approach, and incorporate health promotion and managed care functions.

Perceptual expertise in forensic facial image comparison

PubMed Central

White, David; Phillips, P. Jonathon; Hahn, Carina A.; Hill, Matthew; O'Toole, Alice J.

2015-01-01

Forensic facial identification examiners are required to match the identity of faces in images that vary substantially, owing to changes in viewing conditions and in a person's appearance. These identifications affect the course and outcome of criminal investigations and convictions. Despite calls for research on sources of human error in forensic examination, existing scientific knowledge of face matching accuracy is based, almost exclusively, on people without formal training. Here, we administered three challenging face matching tests to a group of forensic examiners with many years' experience of comparing face images for law enforcement and government agencies. Examiners outperformed untrained participants and computer algorithms, thereby providing the first evidence that these examiners are experts at this task. Notably, computationally fusing responses of multiple experts produced near-perfect performance. Results also revealed qualitative differences between expert and non-expert performance. First, examiners' superiority was greatest at longer exposure durations, suggestive of more entailed comparison in forensic examiners. Second, experts were less impaired by image inversion than non-expert students, contrasting with face memory studies that show larger face inversion effects in high performers. We conclude that expertise in matching identity across unfamiliar face images is supported by processes that differ qualitatively from those supporting memory for individual faces. PMID:26336174

Text Mining and Natural Language Processing Approaches for Automatic Categorization of Lay Requests to Web-Based Expert Forums

PubMed Central

Reincke, Ulrich; Michelmann, Hans Wilhelm

2009-01-01

Background Both healthy and sick people increasingly use electronic media to obtain medical information and advice. For example, Internet users may send requests to Web-based expert forums, or so-called “ask the doctor” services. Objective To automatically classify lay requests to an Internet medical expert forum using a combination of different text-mining strategies. Methods We first manually classified a sample of 988 requests directed to a involuntary childlessness forum on the German website “Rund ums Baby” (“Everything about Babies”) into one or more of 38 categories belonging to two dimensions (“subject matter” and “expectations”). After creating start and synonym lists, we calculated the average Cramer’s V statistic for the association of each word with each category. We also used principle component analysis and singular value decomposition as further text-mining strategies. With these measures we trained regression models and determined, on the basis of best regression models, for any request the probability of belonging to each of the 38 different categories, with a cutoff of 50%. Recall and precision of a test sample were calculated as a measure of quality for the automatic classification. Results According to the manual classification of 988 documents, 102 (10%) documents fell into the category “in vitro fertilization (IVF),” 81 (8%) into the category “ovulation,” 79 (8%) into “cycle,” and 57 (6%) into “semen analysis.” These were the four most frequent categories in the subject matter dimension (consisting of 32 categories). The expectation dimension comprised six categories; we classified 533 documents (54%) as “general information” and 351 (36%) as a wish for “treatment recommendations.” The generation of indicator variables based on the chi-square analysis and Cramer’s V proved to be the best approach for automatic classification in about half of the categories. In combination with the two other approaches, 100% precision and 100% recall were realized in 18 (47%) out of the 38 categories in the test sample. For 35 (92%) categories, precision and recall were better than 80%. For some categories, the input variables (ie, “words”) also included variables from other categories, most often with a negative sign. For example, absence of words predictive for “menstruation” was a strong indicator for the category “pregnancy test.” Conclusions Our approach suggests a way of automatically classifying and analyzing unstructured information in Internet expert forums. The technique can perform a preliminary categorization of new requests and help Internet medical experts to better handle the mass of information and to give professional feedback. PMID:19632978

PhyloPythiaS+: a self-training method for the rapid reconstruction of low-ranking taxonomic bins from metagenomes.

PubMed

Gregor, Ivan; Dröge, Johannes; Schirmer, Melanie; Quince, Christopher; McHardy, Alice C

2016-01-01

Background. Metagenomics is an approach for characterizing environmental microbial communities in situ, it allows their functional and taxonomic characterization and to recover sequences from uncultured taxa. This is often achieved by a combination of sequence assembly and binning, where sequences are grouped into 'bins' representing taxa of the underlying microbial community. Assignment to low-ranking taxonomic bins is an important challenge for binning methods as is scalability to Gb-sized datasets generated with deep sequencing techniques. One of the best available methods for species bins recovery from deep-branching phyla is the expert-trained PhyloPythiaS package, where a human expert decides on the taxa to incorporate in the model and identifies 'training' sequences based on marker genes directly from the sample. Due to the manual effort involved, this approach does not scale to multiple metagenome samples and requires substantial expertise, which researchers who are new to the area do not have. Results. We have developed PhyloPythiaS+, a successor to our PhyloPythia(S) software. The new (+) component performs the work previously done by the human expert. PhyloPythiaS+ also includes a new k-mer counting algorithm, which accelerated the simultaneous counting of 4-6-mers used for taxonomic binning 100-fold and reduced the overall execution time of the software by a factor of three. Our software allows to analyze Gb-sized metagenomes with inexpensive hardware, and to recover species or genera-level bins with low error rates in a fully automated fashion. PhyloPythiaS+ was compared to MEGAN, taxator-tk, Kraken and the generic PhyloPythiaS model. The results showed that PhyloPythiaS+ performs especially well for samples originating from novel environments in comparison to the other methods. Availability. PhyloPythiaS+ in a virtual machine is available for installation under Windows, Unix systems or OS X on: https://github.com/algbioi/ppsp/wiki.

Fast-ball sports experts depend on an inhibitory strategy to reprogram their movement timing.

PubMed

Nakamoto, Hiroki; Ikudome, Sachi; Yotani, Kengo; Maruyama, Atsuo; Mori, Shiro

2013-07-01

The purpose of our study was to clarify whether an inhibitory strategy is used for reprogramming of movement timing by experts in fast-ball sports when they correct their movement timing due to unexpected environmental changes. We evaluated the influence of disruption of inhibitory function of the right inferior frontal gyrus (rIFG) on reprogramming of movement timing of experts and non-experts in fast-ball sports. The task was to manually press a button to coincide with the arrival of a moving target. The target moved at a constant velocity, and its velocity was suddenly either increased or decreased in some trials. The task was performed either with or without transcranial magnetic stimulation (TMS), which was delivered to the region of the rIFG. Under velocity change conditions without TMS, the experts showed significantly smaller timing errors and a higher rate of reprogramming of movement timing than the non-experts. Moreover, TMS application during the task significantly diminished the expert group's performance, but not the control group, particularly in the condition where the target velocity decreases. These results suggest that experts use an inhibitory strategy for reprogramming of movement timing. In addition, the rIFG inhibitory function contributes to the superior movement correction of experts in fast-ball sports.

Searching in clutter : visual attention strategies of expert pilots

DOT National Transportation Integrated Search

2012-10-22

Clutter can slow visual search. However, experts may develop attention strategies that alleviate the effects of clutter on search performance. In the current study we examined the effects of global and local clutter on visual search performance and a...

Self-guided training for deep brain stimulation planning using objective assessment.

PubMed

Holden, Matthew S; Zhao, Yulong; Haegelen, Claire; Essert, Caroline; Fernandez-Vidal, Sara; Bardinet, Eric; Ungi, Tamas; Fichtinger, Gabor; Jannin, Pierre

2018-04-04

Deep brain stimulation (DBS) is an increasingly common treatment for neurodegenerative diseases. Neurosurgeons must have thorough procedural, anatomical, and functional knowledge to plan electrode trajectories and thus ensure treatment efficacy and patient safety. Developing this knowledge requires extensive training. We propose a training approach with objective assessment of neurosurgeon proficiency in DBS planning. To assess proficiency, we propose analyzing both the viability of the planned trajectory and the manner in which the operator arrived at the trajectory. To improve understanding, we suggest a self-guided training course for DBS planning using real-time feedback. To validate the proposed measures of proficiency and training course, two experts and six novices followed the training course, and we monitored their proficiency measures throughout. At baseline, experts planned higher quality trajectories and did so more efficiently. As novices progressed through the training course, their proficiency measures increased significantly, trending toward expert measures. We developed and validated measures which reliably discriminate proficiency levels. These measures are integrated into a training course, which quantitatively improves trainee performance. The proposed training course can be used to improve trainees' proficiency, and the quantitative measures allow trainees' progress to be monitored.

Gender and the experience of mental health expert witness testimony.

PubMed

Kaempf, Aimee C; Baxter, Prudence; Packer, Ira K; Pinals, Debra A

2015-03-01

Mental health expert witness testimony involves complex tasks, and the capacity to perform under pressure is a fundamental skill of a forensic professional. In this context, it is important to understand the nuances of the provision of expert witness testimony. There have been several efforts to examine gender bias across legal and medical systems. Despite these reviews, little is known about how men and women differ or are similar with regard to performing expert witness functions. The purpose of this pilot study was to examine whether the testimony experiences of psychiatry and psychology experts vary by gender. Differences across certain domains, such as the sense of never experiencing anxiety and the sense of one's impact on case outcome were seen across genders. Few other gender-based differences in the experience of providing expert witness testimony were seen. Although the findings of this study raise further questions, they highlight some of the important subtleties noted in forensic practice and the work of the expert witness. In future studies, researchers should continue to explore these findings on the influence of gender and expand to consider culture and race as additional factors in the experience of expert witness testimony. As forensic professional practice evolves, it is important to understand unique aspects of forensic practice, to improve training of forensic experts, and to assist forensic experts in anticipating what they may experience related to the provision of expert testimony. © 2015 American Academy of Psychiatry and the Law.

Error management training and simulation education.

PubMed

Gardner, Aimee; Rich, Michelle

2014-12-01

The integration of simulation into the training of health care professionals provides context for decision making and procedural skills in a high-fidelity environment, without risk to actual patients. It was hypothesised that a novel approach to simulation-based education - error management training - would produce higher performance ratings compared with traditional step-by-step instruction. Radiology technology students were randomly assigned to participate in traditional procedural-based instruction (n = 11) or vicarious error management training (n = 11). All watched an instructional video and discussed how well each incident was handled (traditional instruction group) or identified where the errors were made (vicarious error management training). Students then participated in a 30-minute case-based simulation. Simulations were videotaped for performance analysis. Blinded experts evaluated performance using a predefined evaluation tool created specifically for the scenario. Blinded experts evaluated performance using a predefined evaluation tool created specifically for the scenario The vicarious error management group scored higher on observer-rated performance (Mean = 9.49) than students in the traditional instruction group (Mean = 9.02; p < 0.01). These findings suggest that incorporating the discussion of errors and how to handle errors during the learning session will better equip students when performing hands-on procedures and skills. This pilot study provides preliminary evidence for integrating error management skills into medical curricula and for the design of learning goals in simulation-based education. © 2014 John Wiley & Sons Ltd.

Learning to change taxonomies

NASA Astrophysics Data System (ADS)

Eneva, Elena; Petrushin, Valery A.

2002-03-01

Taxonomies are valuable tools for structuring and representing our knowledge about the world. They are widely used in many domains, where information about species, products, customers, publications, etc. needs to be organized. In the absence of standards, many taxonomies of the same entities can co-exist. A problem arises when data categorized in a particular taxonomy needs to be used by a procedure (methodology or algorithm) that uses a different taxonomy. Usually, a labor-intensive manual approach is used to solve this problem. This paper describes a machine learning approach which aids domain experts in changing taxonomies. It allows learning relationships between two taxonomies and mapping the data from one taxonomy into another. The proposed approach uses decision trees and bootstrapping for learning mappings of instances from the source to the target taxonomies. A C4.5 decision tree classifier is trained on a small manually labeled training set and applied to a randomly selected sample from the unlabeled data. The classification results are analyzed and the misclassified items are corrected and all items are added to the training set. This procedure is iterated until unlabeled data is available or an acceptable error rate is reached. In the latter case the last classifier is used to label all the remaining data. We test our approach on a database of products obtained from as grocery store chain and find that it performs well, reaching 92.6% accuracy while requiring the human expert to explicitly label only 18% of the entire data.

Auditory memory function in expert chess players.

PubMed

Fattahi, Fariba; Geshani, Ahmad; Jafari, Zahra; Jalaie, Shohreh; Salman Mahini, Mona

2015-01-01

Chess is a game that involves many aspects of high level cognition such as memory, attention, focus and problem solving. Long term practice of chess can improve cognition performances and behavioral skills. Auditory memory, as a kind of memory, can be influenced by strengthening processes following long term chess playing like other behavioral skills because of common processing pathways in the brain. The purpose of this study was to evaluate the auditory memory function of expert chess players using the Persian version of dichotic auditory-verbal memory test. The Persian version of dichotic auditory-verbal memory test was performed for 30 expert chess players aged 20-35 years and 30 non chess players who were matched by different conditions; the participants in both groups were randomly selected. The performance of the two groups was compared by independent samples t-test using SPSS version 21. The mean score of dichotic auditory-verbal memory test between the two groups, expert chess players and non-chess players, revealed a significant difference (p≤ 0.001). The difference between the ears scores for expert chess players (p= 0.023) and non-chess players (p= 0.013) was significant. Gender had no effect on the test results. Auditory memory function in expert chess players was significantly better compared to non-chess players. It seems that increased auditory memory function is related to strengthening cognitive performances due to playing chess for a long time.

Reliable assessment of laparoscopic performance in the operating room using videotape analysis.

PubMed

Chang, Lily; Hogle, Nancy J; Moore, Brianna B; Graham, Mark J; Sinanan, Mika N; Bailey, Robert; Fowler, Dennis L

2007-06-01

The Global Operative Assessment of Laparoscopic Skills (GOALS) is a valid assessment tool for objectively evaluating the technical performance of laparoscopic skills in surgery residents. We hypothesized that GOALS would reliably differentiate between an experienced (expert) and an inexperienced (novice) laparoscopic surgeon (construct validity) based on a blinded videotape review of a laparoscopic cholecystectomy procedure. Ten board-certified surgeons actively engaged in the practice and teaching of laparoscopy reviewed and evaluated the videotaped operative performance of one novice and one expert laparoscopic surgeon using GOALS. Each reviewer recorded a score for both the expert and the novice videotape reviews in each of the 5 domains in GOALS (depth perception, bimanual dexterity, efficiency, tissue handling, and overall competence). The scores for the expert and the novice were compared and statistically analyzed using single-factor analysis of variance (ANOVA). The expert scored significantly higher than the novice did in the domains of depth perception (p = .005), bimanual dexterity (p = .001), efficiency (p = .001), and overall competence ( p = .001). Interrater reliability for the reviewers of the novice tape was Cronbach alpha = .93 and the expert tape was Cronbach alpha = .87. There was no difference between the two for tissue handling. The Global Operative Assessment of Laparoscopic Skills is a valid, objective assessment tool for evaluating technical surgical performance when used to blindly evaluate an intraoperative videotape recording of a laparoscopic procedure.

Performance of Vascular Exposure and Fasciotomy Among Surgical Residents Before and After Training Compared With Experts.

PubMed

Mackenzie, Colin F; Garofalo, Evan; Puche, Adam; Chen, Hegang; Pugh, Kristy; Shackelford, Stacy; Tisherman, Samuel; Henry, Sharon; Bowyer, Mark W

2017-06-01

Surgical patient outcomes are related to surgeon skills. To measure resident surgeon technical and nontechnical skills for trauma core competencies before and after training and up to 18 months later and to compare resident performance with the performance of expert traumatologists. This longitudinal study performed from May 1, 2013, through February 29, 2016, at Maryland State Anatomy Board cadaver laboratories included 40 surgical residents and 10 expert traumatologists. Performance was measured during extremity vascular exposures and lower extremity fasciotomy in fresh cadavers before and after taking the Advanced Surgical Skills for Exposure in Trauma (ASSET) course. The primary outcome variable was individual procedure score (IPS), with secondary outcomes of IPSs on 5 components of technical and nontechnical skills, Global Rating Scale scores, errors, and time to complete the procedure. Two trained evaluators located in the same laboratory evaluated performance with a standardized script and mobile touch-screen data collection. Thirty-eight (95%) of 40 surgical residents (mean [SD] age, 31 [2.9] years) who were evaluated before and within 4 weeks of ASSET training completed follow-up evaluations 12 to 18 months later (mean [SD], 14 [2.7] months). The experts (mean [SD] age, 52 [10.0] years) were significantly older and had a longer (mean [SD], 46 [16.3] months) interval since taking the ASSET course (both P < .001). Overall resident cohort performance improved with increased anatomy knowledge, correct procedural steps, and decreased errors from 60% to 19% after the ASSET course regardless of clinical year of training (P < .001). For 21 of 40 residents (52%), correct vascular procedural steps plotted against anatomy knowledge (the 2 IPS components most improved with training) indicates the resident's performance was within 1 nearest-neighbor classifier of experts after ASSET training. Five residents had no improvement with training. The Trauma Readiness Index for experts (mean [SD], 74 [4]) was significantly different compared with the trained residents (mean [SD], 48 [7] before training vs 63 [7] after training [P = .004] and vs 64 [6] 14 months later [P = .002]). Critical errors that might lead to patient death were identified by pretraining IPS decile of less than 0.5. At follow-up, frequency of resident critical errors was no different from experts. The IPSs ranged from 31.6% to 76.9% among residents for core trauma competency procedures. Modeling revealed that interval experience, rather than time since training, affected skill retention up to 18 months later. Only 4 experts and 16 residents (40%) adequately decompressed and confirmed entry into all 4 lower extremity compartments. This study found that ASSET training improved resident procedural skills for up to 18 months. Performance was highly variable. Interval experience after training affected performance. Pretraining skill identified competency of residents vs experts. Extremity vascular and fasciotomy performance evaluations suggest the need for specific anatomical training interventions in residents with IPS deciles less than 0.5.

Human matching performance of genuine crime scene latent fingerprints.

PubMed

Thompson, Matthew B; Tangen, Jason M; McCarthy, Duncan J

2014-02-01

There has been very little research into the nature and development of fingerprint matching expertise. Here we present the results of an experiment testing the claimed matching expertise of fingerprint examiners. Expert (n = 37), intermediate trainee (n = 8), new trainee (n = 9), and novice (n = 37) participants performed a fingerprint discrimination task involving genuine crime scene latent fingerprints, their matches, and highly similar distractors, in a signal detection paradigm. Results show that qualified, court-practicing fingerprint experts were exceedingly accurate compared with novices. Experts showed a conservative response bias, tending to err on the side of caution by making more errors of the sort that could allow a guilty person to escape detection than errors of the sort that could falsely incriminate an innocent person. The superior performance of experts was not simply a function of their ability to match prints, per se, but a result of their ability to identify the highly similar, but nonmatching fingerprints as such. Comparing these results with previous experiments, experts were even more conservative in their decision making when dealing with these genuine crime scene prints than when dealing with simulated crime scene prints, and this conservatism made them relatively less accurate overall. Intermediate trainees-despite their lack of qualification and average 3.5 years experience-performed about as accurately as qualified experts who had an average 17.5 years experience. New trainees-despite their 5-week, full-time training course or their 6 months experience-were not any better than novices at discriminating matching and similar nonmatching prints, they were just more conservative. Further research is required to determine the precise nature of fingerprint matching expertise and the factors that influence performance. The findings of this representative, lab-based experiment may have implications for the way fingerprint examiners testify in court, but what the findings mean for reasoning about expert performance in the wild is an open, empirical, and epistemological question.

Mobile augmented reality for computer-assisted percutaneous nephrolithotomy.

PubMed

Müller, Michael; Rassweiler, Marie-Claire; Klein, Jan; Seitel, Alexander; Gondan, Matthias; Baumhauer, Matthias; Teber, Dogu; Rassweiler, Jens J; Meinzer, Hans-Peter; Maier-Hein, Lena

2013-07-01

Percutaneous nephrolithotomy (PCNL) plays an integral role in treatment of renal stones. Creating percutaneous renal access is the most important and challenging step in the procedure. To facilitate this step, we evaluated our novel mobile augmented reality (AR) system for its feasibility of use for PCNL. A tablet computer, such as an iPad[Formula: see text], is positioned above the patient with its camera pointing toward the field of intervention. The images of the tablet camera are registered with the CT image by means of fiducial markers. Structures of interest can be superimposed semi-transparently on the video images. We present a systematic evaluation by means of a phantom study. An urological trainee and two experts conducted 53 punctures on kidney phantoms. The trainee performed best with the proposed AR system in terms of puncturing time (mean: 99 s), whereas the experts performed best with fluoroscopy (mean: 59 s). iPad assistance lowered radiation exposure by a factor of 3 for the inexperienced physician and by a factor of 1.8 for the experts in comparison with fluoroscopy usage. We achieve a mean visualization accuracy of 2.5 mm. The proposed tablet computer-based AR system has proven helpful in assisting percutaneous interventions such as PCNL and shows benefits compared to other state-of-the-art assistance systems. A drawback of the system in its current state is the lack of depth information. Despite that, the simple integration into the clinical workflow highlights the potential impact of this approach to such interventions.

A parallel expert system for the control of a robotic air vehicle

NASA Technical Reports Server (NTRS)

Shakley, Donald; Lamont, Gary B.

1988-01-01

Expert systems can be used to govern the intelligent control of vehicles, for example the Robotic Air Vehicle (RAV). Due to the nature of the RAV system the associated expert system needs to perform in a demanding real-time environment. The use of a parallel processing capability to support the associated expert system's computational requirement is critical in this application. Thus, algorithms for parallel real-time expert systems must be designed, analyzed, and synthesized. The design process incorporates a consideration of the rule-set/face-set size along with representation issues. These issues are looked at in reference to information movement and various inference mechanisms. Also examined is the process involved with transporting the RAV expert system functions from the TI Explorer, where they are implemented in the Automated Reasoning Tool (ART), to the iPSC Hypercube, where the system is synthesized using Concurrent Common LISP (CCLISP). The transformation process for the ART to CCLISP conversion is described. The performance characteristics of the parallel implementation of these expert systems on the iPSC Hypercube are compared to the TI Explorer implementation.

Cognitions of Expert Supervisors in Academe: A Concept Mapping Approach

ERIC Educational Resources Information Center

Kemer, Gülsah; Borders, L. DiAnne; Willse, John

2014-01-01

Eighteen expert supervisors reported their thoughts while preparing for, conducting, and evaluating their supervision sessions. Concept mapping (Kane & Trochim, [Kane, M., 2007]) yielded 195 cognitions classified into 25 cognitive categories organized into 5 supervision areas: conceptualization of supervision, supervisee assessment,…

Improved Estimation of Cardiac Function Parameters Using a Combination of Independent Automated Segmentation Results in Cardiovascular Magnetic Resonance Imaging.

PubMed

Lebenberg, Jessica; Lalande, Alain; Clarysse, Patrick; Buvat, Irene; Casta, Christopher; Cochet, Alexandre; Constantinidès, Constantin; Cousty, Jean; de Cesare, Alain; Jehan-Besson, Stephanie; Lefort, Muriel; Najman, Laurent; Roullot, Elodie; Sarry, Laurent; Tilmant, Christophe; Frouin, Frederique; Garreau, Mireille

2015-01-01

This work aimed at combining different segmentation approaches to produce a robust and accurate segmentation result. Three to five segmentation results of the left ventricle were combined using the STAPLE algorithm and the reliability of the resulting segmentation was evaluated in comparison with the result of each individual segmentation method. This comparison was performed using a supervised approach based on a reference method. Then, we used an unsupervised statistical evaluation, the extended Regression Without Truth (eRWT) that ranks different methods according to their accuracy in estimating a specific biomarker in a population. The segmentation accuracy was evaluated by estimating six cardiac function parameters resulting from the left ventricle contour delineation using a public cardiac cine MRI database. Eight different segmentation methods, including three expert delineations and five automated methods, were considered, and sixteen combinations of the automated methods using STAPLE were investigated. The supervised and unsupervised evaluations demonstrated that in most cases, STAPLE results provided better estimates than individual automated segmentation methods. Overall, combining different automated segmentation methods improved the reliability of the segmentation result compared to that obtained using an individual method and could achieve the accuracy of an expert.

Improved Estimation of Cardiac Function Parameters Using a Combination of Independent Automated Segmentation Results in Cardiovascular Magnetic Resonance Imaging

PubMed Central

Lebenberg, Jessica; Lalande, Alain; Clarysse, Patrick; Buvat, Irene; Casta, Christopher; Cochet, Alexandre; Constantinidès, Constantin; Cousty, Jean; de Cesare, Alain; Jehan-Besson, Stephanie; Lefort, Muriel; Najman, Laurent; Roullot, Elodie; Sarry, Laurent; Tilmant, Christophe

2015-01-01

This work aimed at combining different segmentation approaches to produce a robust and accurate segmentation result. Three to five segmentation results of the left ventricle were combined using the STAPLE algorithm and the reliability of the resulting segmentation was evaluated in comparison with the result of each individual segmentation method. This comparison was performed using a supervised approach based on a reference method. Then, we used an unsupervised statistical evaluation, the extended Regression Without Truth (eRWT) that ranks different methods according to their accuracy in estimating a specific biomarker in a population. The segmentation accuracy was evaluated by estimating six cardiac function parameters resulting from the left ventricle contour delineation using a public cardiac cine MRI database. Eight different segmentation methods, including three expert delineations and five automated methods, were considered, and sixteen combinations of the automated methods using STAPLE were investigated. The supervised and unsupervised evaluations demonstrated that in most cases, STAPLE results provided better estimates than individual automated segmentation methods. Overall, combining different automated segmentation methods improved the reliability of the segmentation result compared to that obtained using an individual method and could achieve the accuracy of an expert. PMID:26287691

Coordination as a function of skill level in the gymnastics longswing.

PubMed

Williams, Genevieve K R; Irwin, Gareth; Kerwin, David G; Hamill, Joseph; Van Emmerik, Richard E A; Newell, Karl M

2016-01-01

The purpose of this study was to investigate the nature of inter-joint coordination at different levels of skilled performance to: (1) distinguish learners who were successful versus unsuccessful in terms of their task performance; (2) investigate the pathways of change during the learning of a new coordination pattern and (3) examine how the learner's coordination patterns relate to those of experts in the longswing gymnastics skill. Continuous relative phase of hip and shoulder joint motions was examined for longswings performed by two groups of novices, successful (n = 4) and unsuccessful (n = 4) over five practice sessions, and two expert gymnasts. Principal component analysis showed that during longswing positions where least continuous relative phase variability occurred for expert gymnasts, high variability distinguished the successful from the unsuccessful novice group. Continuous relative phase profiles of successful novices became more out-of-phase over practice and less similar to the closely in-phase coupling of the expert gymnasts. Collectively, the findings support the proposition that at the level in inter-joint coordination a technique emerges that facilitates successful performance but is not more like an expert's movement coordination. This finding questions the appropriateness of inferring development towards a "gold champion" movement coordination.

Estimating Classifier Accuracy Using Noisy Expert Labels

DTIC Science & Technology

estimators to real -world problems is limited. We applythe estimators to labels simulated from three models of the expert labeling process and also four real ...thatconditional dependence between experts negatively impacts estimator performance. On two of the real datasets, the estimatorsclearly outperformed the

Expert AIV: Study and Prototyping of an Expert System, To Support the Conceptual AIV Phases Of Space Programs

NASA Astrophysics Data System (ADS)

Andrina, G.; Basso, V.; Saitta, L.

2004-08-01

The effort in optimising the AIV process has been mainly focused in the recent years on the standardisation of approaches and on the application of new methodologies. But the earlier the intervention, the greater the benefits in terms of cost and schedule. Early phases of AIV process relied up to now on standards that need to be tailored through company and personal expertise. A study has then been conducted in order to exploit the possibility to develop an expert system helping in making choices in the early, conceptual phase of Assembly, Integration and Verification, namely the Model Philosophy and the test definition. The work focused on a hybrid approach, allowing interaction between historical data and human expertise. The expert system that has been prototyped exploits both information elicited from domain experts and results of a Data Mining activity on the existent data bases of completed projects verification data. The Data Mining algorithms allow the extraction of past experience resident on ESA/ MATD data base, which contains information in the form of statistical summaries, costs, frequencies of on-ground and in flight failures. Finding non-trivial associations could then be utilised by the experts to manage new decisions in a controlled way (Standards driven) at the beginning or during the AIV Process Moreover, the Expert AIV could allow compilation of a set of feasible AIV schedules to support further programmatic-driven choices.

Towards a precise test for malaria diagnosis in the Brazilian Amazon: comparison among field microscopy, a rapid diagnostic test, nested PCR, and a computational expert system based on artificial neural networks

PubMed Central

2010-01-01

Background Accurate malaria diagnosis is mandatory for the treatment and management of severe cases. Moreover, individuals with asymptomatic malaria are not usually screened by health care facilities, which further complicates disease control efforts. The present study compared the performances of a malaria rapid diagnosis test (RDT), the thick blood smear method and nested PCR for the diagnosis of symptomatic malaria in the Brazilian Amazon. In addition, an innovative computational approach was tested for the diagnosis of asymptomatic malaria. Methods The study was divided in two parts. For the first part, passive case detection was performed in 311 individuals with malaria-related symptoms from a recently urbanized community in the Brazilian Amazon. A cross-sectional investigation compared the diagnostic performance of the RDT Optimal-IT, nested PCR and light microscopy. The second part of the study involved active case detection of asymptomatic malaria in 380 individuals from riverine communities in Rondônia, Brazil. The performances of microscopy, nested PCR and an expert computational system based on artificial neural networks (MalDANN) using epidemiological data were compared. Results Nested PCR was shown to be the gold standard for diagnosis of both symptomatic and asymptomatic malaria because it detected the major number of cases and presented the maximum specificity. Surprisingly, the RDT was superior to microscopy in the diagnosis of cases with low parasitaemia. Nevertheless, RDT could not discriminate the Plasmodium species in 12 cases of mixed infections (Plasmodium vivax + Plasmodium falciparum). Moreover, the microscopy presented low performance in the detection of asymptomatic cases (61.25% of correct diagnoses). The MalDANN system using epidemiological data was worse that the light microscopy (56% of correct diagnoses). However, when information regarding plasma levels of interleukin-10 and interferon-gamma were inputted, the MalDANN performance sensibly increased (80% correct diagnoses). Conclusions An RDT for malaria diagnosis may find a promising use in the Brazilian Amazon integrating a rational diagnostic approach. Despite the low performance of the MalDANN test using solely epidemiological data, an approach based on neural networks may be feasible in cases where simpler methods for discriminating individuals below and above threshold cytokine levels are available. PMID:20459613

Towards a precise test for malaria diagnosis in the Brazilian Amazon: comparison among field microscopy, a rapid diagnostic test, nested PCR, and a computational expert system based on artificial neural networks.

PubMed

Andrade, Bruno B; Reis-Filho, Antonio; Barros, Austeclino M; Souza-Neto, Sebastião M; Nogueira, Lucas L; Fukutani, Kiyoshi F; Camargo, Erney P; Camargo, Luís M A; Barral, Aldina; Duarte, Angelo; Barral-Netto, Manoel

2010-05-06

Accurate malaria diagnosis is mandatory for the treatment and management of severe cases. Moreover, individuals with asymptomatic malaria are not usually screened by health care facilities, which further complicates disease control efforts. The present study compared the performances of a malaria rapid diagnosis test (RDT), the thick blood smear method and nested PCR for the diagnosis of symptomatic malaria in the Brazilian Amazon. In addition, an innovative computational approach was tested for the diagnosis of asymptomatic malaria. The study was divided in two parts. For the first part, passive case detection was performed in 311 individuals with malaria-related symptoms from a recently urbanized community in the Brazilian Amazon. A cross-sectional investigation compared the diagnostic performance of the RDT Optimal-IT, nested PCR and light microscopy. The second part of the study involved active case detection of asymptomatic malaria in 380 individuals from riverine communities in Rondônia, Brazil. The performances of microscopy, nested PCR and an expert computational system based on artificial neural networks (MalDANN) using epidemiological data were compared. Nested PCR was shown to be the gold standard for diagnosis of both symptomatic and asymptomatic malaria because it detected the major number of cases and presented the maximum specificity. Surprisingly, the RDT was superior to microscopy in the diagnosis of cases with low parasitaemia. Nevertheless, RDT could not discriminate the Plasmodium species in 12 cases of mixed infections (Plasmodium vivax + Plasmodium falciparum). Moreover, the microscopy presented low performance in the detection of asymptomatic cases (61.25% of correct diagnoses). The MalDANN system using epidemiological data was worse that the light microscopy (56% of correct diagnoses). However, when information regarding plasma levels of interleukin-10 and interferon-gamma were inputted, the MalDANN performance sensibly increased (80% correct diagnoses). An RDT for malaria diagnosis may find a promising use in the Brazilian Amazon integrating a rational diagnostic approach. Despite the low performance of the MalDANN test using solely epidemiological data, an approach based on neural networks may be feasible in cases where simpler methods for discriminating individuals below and above threshold cytokine levels are available.

An Approach towards Ultrasound Kidney Cysts Detection using Vector Graphic Image Analysis

NASA Astrophysics Data System (ADS)

Mahmud, Wan Mahani Hafizah Wan; Supriyanto, Eko

2017-08-01

This study develops new approach towards detection of kidney ultrasound image for both with single cyst as well as multiple cysts. 50 single cyst images and 25 multiple cysts images were used to test the developed algorithm. Steps involved in developing this algorithm were vector graphic image formation and analysis, thresholding, binarization, filtering as well as roundness test. Performance evaluation to 50 single cyst images gave accuracy of 92%, while for multiple cysts images, the accuracy was about 86.89% when tested to 25 multiple cysts images. This developed algorithm may be used in developing a computerized system such as computer aided diagnosis system to help medical experts in diagnosis of kidney cysts.

Systematic methods for knowledge acquisition and expert system development

NASA Technical Reports Server (NTRS)

Belkin, Brenda L.; Stengel, Robert F.

1991-01-01

Nine cooperating rule-based systems, collectively called AUTOCREW, were designed to automate functions and decisions associated with a combat aircraft's subsystem. The organization of tasks within each system is described; performance metrics were developed to evaluate the workload of each rule base, and to assess the cooperation between the rule-bases. Each AUTOCREW subsystem is composed of several expert systems that perform specific tasks. AUTOCREW's NAVIGATOR was analyzed in detail to understand the difficulties involved in designing the system and to identify tools and methodologies that ease development. The NAVIGATOR determines optimal navigation strategies from a set of available sensors. A Navigation Sensor Management (NSM) expert system was systematically designed from Kalman filter covariance data; four ground-based, a satellite-based, and two on-board INS-aiding sensors were modeled and simulated to aid an INS. The NSM Expert was developed using the Analysis of Variance (ANOVA) and the ID3 algorithm. Navigation strategy selection is based on an RSS position error decision metric, which is computed from the covariance data. Results show that the NSM Expert predicts position error correctly between 45 and 100 percent of the time for a specified navaid configuration and aircraft trajectory. The NSM Expert adapts to new situations, and provides reasonable estimates of hybrid performance. The systematic nature of the ANOVA/ID3 method makes it broadly applicable to expert system design when experimental or simulation data is available.

Expert system technology

NASA Technical Reports Server (NTRS)

Prince, Mary Ellen

1987-01-01

The expert system is a computer program which attempts to reproduce the problem-solving behavior of an expert, who is able to view problems from a broad perspective and arrive at conclusions rapidly, using intuition, shortcuts, and analogies to previous situations. Expert systems are a departure from the usual artificial intelligence approach to problem solving. Researchers have traditionally tried to develop general modes of human intelligence that could be applied to many different situations. Expert systems, on the other hand, tend to rely on large quantities of domain specific knowledge, much of it heuristic. The reasoning component of the system is relatively simple and straightforward. For this reason, expert systems are often called knowledge based systems. The report expands on the foregoing. Section 1 discusses the architecture of a typical expert system. Section 2 deals with the characteristics that make a problem a suitable candidate for expert system solution. Section 3 surveys current technology, describing some of the software aids available for expert system development. Section 4 discusses the limitations of the latter. The concluding section makes predictions of future trends.

Performance Engineering as an Expert System.

ERIC Educational Resources Information Center

Harmon, Paul

1984-01-01

Considers three powerful techniques--heuristics, context trees, and search via backward chaining--that a knowledge engineer might employ to develop an expert system to automate performance engineering, i.e., the branch of instructional technology that focuses on the problems of business and industry. (MBR)

A Study on Feature of Eye Tracking in Difference of Skill Level during Observational Learning of Movement

NASA Astrophysics Data System (ADS)

Nuruki, Atsuo; Shimozono, Tomoyuki; Kawabata, Takuro; Yamada, Masafumi; Yunokuchi, Kazutomo; Maruyama, Atsuo

Recently, it often said that it is one of the means that the observational learning promotes the acquisition of sports and athletic skills. We think that the inexperienced person can efficiently acquire athletic skills by using the observational method of the expert as an index of the observational method in the observational learning. Then, in the present study, the expert and inexperienced person's glance characteristic were compared, and it was examined whether the observational method of the expert was able to be used as an index of the observational method of the inexperienced person. The glance characteristics are a glance transition, glance total moved distance, the gazing duration, moreover glance moved distance and radial velocity between each gaze points. Additionally, we investigated whether there was a change in physical performance before and after the observational learning, and two different observational learning groups (the expert's observational method group, the free observation group). In result, it was clarified that the expert concentrated, observed a constant part of the movement, and the inexperienced person was observing the entire movement. Moreover, the result that glance total moved distance was shorter than the inexperienced person, and expert's gazing duration was longer than the inexperienced person. It was clarified that the expert was efficiently emphatically observing the point of the movement from these results. In addition, the inexperienced persons have advanced physical performance through the observational learning. Then the expert's observational method group advanced physical performance better than the free observation group. Therefore we suggested that the observational method of the expert be able to be used as an index of the method of observing the inexperienced person.

Development and assessment of national performance indicators for infection prevention and control and antimicrobial stewardship in European long-term care facilities.

PubMed

Cookson, B; Mackenzie, D; Kafatos, G; Jans, B; Latour, K; Moro, M L; Ricchizzi, E; Van de Mortel, M; Suetens, C; Fabry, J

2013-09-01

Healthcare-associated infections in long-term care facilities (LTCFs) are of increasing importance. To develop consensus national performance indicators (NPIs) for infection control (ICPI) and antimicrobial stewardship (ASPI) in LTCFs, and assess the performance of 32 European countries against these NPIs. Previously established European standards were the basis for consensus and the same iterative approach with national representatives from the 32 countries. A World Health Organization scoring system recorded how close each country was to implementing each standard. The 42 agreed component indicators were grouped into six NPI categories: 'national programme', 'guidelines', 'expert advice', 'IC structure' (not present in the ASPI), 'surveillance' and 'composite'. 'Guidelines' scored the highest mean total possible score (60%, range 20-100%), followed by 'composite' (53%, range 30-100%), 'expert advice' (48%, range 20-100%), 'surveillance' (47%, range 20-83%), 'national programme' (42%, range 20-100%) and 'IC structure' (39%, range 20-100%). Although several scores were low, some countries were able to implement all NPIs, indicating that this was feasible. Most NPIs were very significantly related, indicating that they were considered to be important by the countries. 'Guidelines' and 'IC structure' were significantly related to European region (P ≤ 0.05). Accreditation/inspection was not evident in seven (22%) countries, nine (28%) countries had accreditation/inspection that included IC assessments, and seven (22%) countries had accreditation/inspection that included IC and antimicrobial stewardship assessments. Multi-variable analysis found that only the NPI and the ICPI 'expert advice' were associated with accreditation/inspection which included IC and antimicrobial stewardship. The identified gaps represent significant potential patient safety issues. The NPIs should serve as a basis for monitoring improvements over the coming years. Copyright © 2013 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.

Can linear regression modeling help clinicians in the interpretation of genotypic resistance data? An application to derive a lopinavir-score.

PubMed

Cozzi-Lepri, Alessandro; Prosperi, Mattia C F; Kjær, Jesper; Dunn, David; Paredes, Roger; Sabin, Caroline A; Lundgren, Jens D; Phillips, Andrew N; Pillay, Deenan

2011-01-01

The question of whether a score for a specific antiretroviral (e.g. lopinavir/r in this analysis) that improves prediction of viral load response given by existing expert-based interpretation systems (IS) could be derived from analyzing the correlation between genotypic data and virological response using statistical methods remains largely unanswered. We used the data of the patients from the UK Collaborative HIV Cohort (UK CHIC) Study for whom genotypic data were stored in the UK HIV Drug Resistance Database (UK HDRD) to construct a training/validation dataset of treatment change episodes (TCE). We used the average square error (ASE) on a 10-fold cross-validation and on a test dataset (the EuroSIDA TCE database) to compare the performance of a newly derived lopinavir/r score with that of the 3 most widely used expert-based interpretation rules (ANRS, HIVDB and Rega). Our analysis identified mutations V82A, I54V, K20I and I62V, which were associated with reduced viral response and mutations I15V and V91S which determined lopinavir/r hypersensitivity. All models performed equally well (ASE on test ranging between 1.1 and 1.3, p = 0.34). We fully explored the potential of linear regression to construct a simple predictive model for lopinavir/r-based TCE. Although, the performance of our proposed score was similar to that of already existing IS, previously unrecognized lopinavir/r-associated mutations were identified. The analysis illustrates an approach of validation of expert-based IS that could be used in the future for other antiretrovirals and in other settings outside HIV research.