A knowledge-based patient assessment system: conceptual and technical design.

PubMed Central

Reilly, C. A.; Zielstorff, R. D.; Fox, R. L.; O'Connell, E. M.; Carroll, D. L.; Conley, K. A.; Fitzgerald, P.; Eng, T. K.; Martin, A.; Zidik, C. M.; Segal, M.

2000-01-01

This paper describes the design of an inpatient patient assessment application that captures nursing assessment data using a wireless laptop computer. The primary aim of this system is to capture structured information for facilitating decision support and quality monitoring. The system also aims to improve efficiency of recording patient assessments, reduce costs, and improve discharge planning and early identification of patient learning needs. Object-oriented methods were used to elicit functional requirements and to model the proposed system. A tools-based development approach is being used to facilitate rapid development and easy modification of assessment items and rules for decision support. Criteria for evaluation include perceived utility by clinician users, validity of decision support rules, time spent recording assessments, and perceived utility of aggregate reports for quality monitoring. PMID:11079970

A knowledge-based patient assessment system: conceptual and technical design.

PubMed

Reilly, C A; Zielstorff, R D; Fox, R L; O'Connell, E M; Carroll, D L; Conley, K A; Fitzgerald, P; Eng, T K; Martin, A; Zidik, C M; Segal, M

2000-01-01

This paper describes the design of an inpatient patient assessment application that captures nursing assessment data using a wireless laptop computer. The primary aim of this system is to capture structured information for facilitating decision support and quality monitoring. The system also aims to improve efficiency of recording patient assessments, reduce costs, and improve discharge planning and early identification of patient learning needs. Object-oriented methods were used to elicit functional requirements and to model the proposed system. A tools-based development approach is being used to facilitate rapid development and easy modification of assessment items and rules for decision support. Criteria for evaluation include perceived utility by clinician users, validity of decision support rules, time spent recording assessments, and perceived utility of aggregate reports for quality monitoring.

Improving Emergency Department Triage Classification with Computerized Clinical Decision Support at a Pediatric Hospital

ERIC Educational Resources Information Center

Kunisch, Joseph Martin

2012-01-01

Background: The Emergency Severity Index (ESI) is an emergency department (ED) triage classification system based on estimated patient-specific resource utilization. Rules for a computerized clinical decision support (CDS) system based on a patient's chief complaint were developed and tested using a stochastic model for predicting ESI scores.…

A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record.

PubMed

Wright, Adam; Pang, Justine; Feblowitz, Joshua C; Maloney, Francine L; Wilcox, Allison R; Ramelson, Harley Z; Schneider, Louise I; Bates, David W

2011-01-01

Accurate knowledge of a patient's medical problems is critical for clinical decision making, quality measurement, research, billing and clinical decision support. Common structured sources of problem information include the patient problem list and billing data; however, these sources are often inaccurate or incomplete. To develop and validate methods of automatically inferring patient problems from clinical and billing data, and to provide a knowledge base for inferring problems. We identified 17 target conditions and designed and validated a set of rules for identifying patient problems based on medications, laboratory results, billing codes, and vital signs. A panel of physicians provided input on a preliminary set of rules. Based on this input, we tested candidate rules on a sample of 100,000 patient records to assess their performance compared to gold standard manual chart review. The physician panel selected a final rule for each condition, which was validated on an independent sample of 100,000 records to assess its accuracy. Seventeen rules were developed for inferring patient problems. Analysis using a validation set of 100,000 randomly selected patients showed high sensitivity (range: 62.8-100.0%) and positive predictive value (range: 79.8-99.6%) for most rules. Overall, the inference rules performed better than using either the problem list or billing data alone. We developed and validated a set of rules for inferring patient problems. These rules have a variety of applications, including clinical decision support, care improvement, augmentation of the problem list, and identification of patients for research cohorts.

Construction of a Clinical Decision Support System for Undergoing Surgery Based on Domain Ontology and Rules Reasoning

PubMed Central

Bau, Cho-Tsan; Huang, Chung-Yi

2014-01-01

Abstract Objective: To construct a clinical decision support system (CDSS) for undergoing surgery based on domain ontology and rules reasoning in the setting of hospitalized diabetic patients. Materials and Methods: The ontology was created with a modified ontology development method, including specification and conceptualization, formalization, implementation, and evaluation and maintenance. The Protégé–Web Ontology Language editor was used to implement the ontology. Embedded clinical knowledge was elicited to complement the domain ontology with formal concept analysis. The decision rules were translated into JENA format, which JENA can use to infer recommendations based on patient clinical situations. Results: The ontology includes 31 classes and 13 properties, plus 38 JENA rules that were built to generate recommendations. The evaluation studies confirmed the correctness of the ontology, acceptance of recommendations, satisfaction with the system, and usefulness of the ontology for glycemic management of diabetic patients undergoing surgery, especially for domain experts. Conclusions: The contribution of this research is to set up an evidence-based hybrid ontology and an evaluation method for CDSS. The system can help clinicians to achieve inpatient glycemic control in diabetic patients undergoing surgery while avoiding hypoglycemia. PMID:24730353

Construction of a clinical decision support system for undergoing surgery based on domain ontology and rules reasoning.

PubMed

Bau, Cho-Tsan; Chen, Rung-Ching; Huang, Chung-Yi

2014-05-01

To construct a clinical decision support system (CDSS) for undergoing surgery based on domain ontology and rules reasoning in the setting of hospitalized diabetic patients. The ontology was created with a modified ontology development method, including specification and conceptualization, formalization, implementation, and evaluation and maintenance. The Protégé-Web Ontology Language editor was used to implement the ontology. Embedded clinical knowledge was elicited to complement the domain ontology with formal concept analysis. The decision rules were translated into JENA format, which JENA can use to infer recommendations based on patient clinical situations. The ontology includes 31 classes and 13 properties, plus 38 JENA rules that were built to generate recommendations. The evaluation studies confirmed the correctness of the ontology, acceptance of recommendations, satisfaction with the system, and usefulness of the ontology for glycemic management of diabetic patients undergoing surgery, especially for domain experts. The contribution of this research is to set up an evidence-based hybrid ontology and an evaluation method for CDSS. The system can help clinicians to achieve inpatient glycemic control in diabetic patients undergoing surgery while avoiding hypoglycemia.

A programmable rules engine to provide clinical decision support using HTML forms.

PubMed Central

Heusinkveld, J.; Geissbuhler, A.; Sheshelidze, D.; Miller, R.

1999-01-01

The authors have developed a simple method for specifying rules to be applied to information on HTML forms. This approach allows clinical experts, who lack the programming expertise needed to write CGI scripts, to construct and maintain domain-specific knowledge and ordering capabilities within WizOrder, the order-entry and decision support system used at Vanderbilt Hospital. The clinical knowledge base maintainers use HTML editors to create forms and spreadsheet programs for rule entry. A test environment has been developed which uses Netscape to display forms; the production environment displays forms using an embedded browser. Images Figure 1 PMID:10566470

Application of decision rules for empowering of Indonesian telematics services SMEs

NASA Astrophysics Data System (ADS)

Tosida, E. T.; Hairlangga, O.; Amirudin, F.; Ridwanah, M.

2018-03-01

The independence of the field of telematics became one of Indonesia's vision in 2024. One effort to achieve it can be done by empowering SMEs in the field of telematics. Empowerment carried out need a practical mechanism by utilizing data centered, including through the National Economic Census database (Susenas). Based on the Susenas can be formulated the decision rules of determining the provision of assistance for SMEs in the field of telematics. The way it did by generating the rule base through the classification technique. The CART algorithm-based decision rule model performs better than C45 and ID3 models. The high level of performance model is also in line with the regulations applied by the government. This becomes one of the strengths of research, because the resulting model is consistent with the existing conditions in Indonesia. The rules base generated from the three classification techniques show different rules. The CART technique has pattern matching with the realization of activities in The Ministry of Cooperatives and SMEs. So far, the government has difficulty in referring data related to the empowerment of SMEs telematics services. Therefore, the findings resulting from this research can be used as an alternative decision support system related to the program of empowerment of SMEs in telematics.

From data mining rules to medical logical modules and medical advices.

PubMed

Gomoi, Valentin; Vida, Mihaela; Robu, Raul; Stoicu-Tivadar, Vasile; Bernad, Elena; Lupşe, Oana

2013-01-01

Using data mining in collaboration with Clinical Decision Support Systems adds new knowledge as support for medical diagnosis. The current work presents a tool which translates data mining rules supporting generation of medical advices to Arden Syntax formalism. The developed system was tested with data related to 2326 births that took place in 2010 at the Bega Obstetrics - Gynaecology Hospital, Timişoara. Based on processing these data, 14 medical rules regarding the Apgar score were generated and then translated in Arden Syntax language.

A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record

PubMed Central

Pang, Justine; Feblowitz, Joshua C; Maloney, Francine L; Wilcox, Allison R; Ramelson, Harley Z; Schneider, Louise I; Bates, David W

2011-01-01

Background Accurate knowledge of a patient's medical problems is critical for clinical decision making, quality measurement, research, billing and clinical decision support. Common structured sources of problem information include the patient problem list and billing data; however, these sources are often inaccurate or incomplete. Objective To develop and validate methods of automatically inferring patient problems from clinical and billing data, and to provide a knowledge base for inferring problems. Study design and methods We identified 17 target conditions and designed and validated a set of rules for identifying patient problems based on medications, laboratory results, billing codes, and vital signs. A panel of physicians provided input on a preliminary set of rules. Based on this input, we tested candidate rules on a sample of 100 000 patient records to assess their performance compared to gold standard manual chart review. The physician panel selected a final rule for each condition, which was validated on an independent sample of 100 000 records to assess its accuracy. Results Seventeen rules were developed for inferring patient problems. Analysis using a validation set of 100 000 randomly selected patients showed high sensitivity (range: 62.8–100.0%) and positive predictive value (range: 79.8–99.6%) for most rules. Overall, the inference rules performed better than using either the problem list or billing data alone. Conclusion We developed and validated a set of rules for inferring patient problems. These rules have a variety of applications, including clinical decision support, care improvement, augmentation of the problem list, and identification of patients for research cohorts. PMID:21613643

Web-based Weather Expert System (WES) for Space Shuttle Launch

NASA Technical Reports Server (NTRS)

Bardina, Jorge E.; Rajkumar, T.

2003-01-01

The Web-based Weather Expert System (WES) is a critical module of the Virtual Test Bed development to support 'go/no go' decisions for Space Shuttle operations in the Intelligent Launch and Range Operations program of NASA. The weather rules characterize certain aspects of the environment related to the launching or landing site, the time of the day or night, the pad or runway conditions, the mission durations, the runway equipment and landing type. Expert system rules are derived from weather contingency rules, which were developed over years by NASA. Backward chaining, a goal-directed inference method is adopted, because a particular consequence or goal clause is evaluated first, and then chained backward through the rules. Once a rule is satisfied or true, then that particular rule is fired and the decision is expressed. The expert system is continuously verifying the rules against the past one-hour weather conditions and the decisions are made. The normal procedure of operations requires a formal pre-launch weather briefing held on Launch minus 1 day, which is a specific weather briefing for all areas of Space Shuttle launch operations. In this paper, the Web-based Weather Expert System of the Intelligent Launch and range Operations program is presented.

Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling.

PubMed

Tsipouras, Markos G; Exarchos, Themis P; Fotiadis, Dimitrios I; Kotsia, Anna P; Vakalis, Konstantinos V; Naka, Katerina K; Michalis, Lampros K

2008-07-01

A fuzzy rule-based decision support system (DSS) is presented for the diagnosis of coronary artery disease (CAD). The system is automatically generated from an initial annotated dataset, using a four stage methodology: 1) induction of a decision tree from the data; 2) extraction of a set of rules from the decision tree, in disjunctive normal form and formulation of a crisp model; 3) transformation of the crisp set of rules into a fuzzy model; and 4) optimization of the parameters of the fuzzy model. The dataset used for the DSS generation and evaluation consists of 199 subjects, each one characterized by 19 features, including demographic and history data, as well as laboratory examinations. Tenfold cross validation is employed, and the average sensitivity and specificity obtained is 62% and 54%, respectively, using the set of rules extracted from the decision tree (first and second stages), while the average sensitivity and specificity increase to 80% and 65%, respectively, when the fuzzification and optimization stages are used. The system offers several advantages since it is automatically generated, it provides CAD diagnosis based on easily and noninvasively acquired features, and is able to provide interpretation for the decisions made.

Performance evaluation of the machine learning algorithms used in inference mechanism of a medical decision support system.

PubMed

Bal, Mert; Amasyali, M Fatih; Sever, Hayri; Kose, Guven; Demirhan, Ayse

2014-01-01

The importance of the decision support systems is increasingly supporting the decision making process in cases of uncertainty and the lack of information and they are widely used in various fields like engineering, finance, medicine, and so forth, Medical decision support systems help the healthcare personnel to select optimal method during the treatment of the patients. Decision support systems are intelligent software systems that support decision makers on their decisions. The design of decision support systems consists of four main subjects called inference mechanism, knowledge-base, explanation module, and active memory. Inference mechanism constitutes the basis of decision support systems. There are various methods that can be used in these mechanisms approaches. Some of these methods are decision trees, artificial neural networks, statistical methods, rule-based methods, and so forth. In decision support systems, those methods can be used separately or a hybrid system, and also combination of those methods. In this study, synthetic data with 10, 100, 1000, and 2000 records have been produced to reflect the probabilities on the ALARM network. The accuracy of 11 machine learning methods for the inference mechanism of medical decision support system is compared on various data sets.

Life insurance risk assessment using a fuzzy logic expert system

NASA Technical Reports Server (NTRS)

Carreno, Luis A.; Steel, Roy A.

1992-01-01

In this paper, we present a knowledge based system that combines fuzzy processing with rule-based processing to form an improved decision aid for evaluating risk for life insurance. This application illustrates the use of FuzzyCLIPS to build a knowledge based decision support system possessing fuzzy components to improve user interactions and KBS performance. The results employing FuzzyCLIPS are compared with the results obtained from the solution of the problem using traditional numerical equations. The design of the fuzzy solution consists of a CLIPS rule-based system for some factors combined with fuzzy logic rules for others. This paper describes the problem, proposes a solution, presents the results, and provides a sample output of the software product.

Characteristics of knowledge content in a curated online evidence library.

PubMed

Varada, Sowmya; Lacson, Ronilda; Raja, Ali S; Ip, Ivan K; Schneider, Louise; Osterbur, David; Bain, Paul; Vetrano, Nicole; Cellini, Jacqueline; Mita, Carol; Coletti, Margaret; Whelan, Julia; Khorasani, Ramin

2018-05-01

To describe types of recommendations represented in a curated online evidence library, report on the quality of evidence-based recommendations pertaining to diagnostic imaging exams, and assess underlying knowledge representation. The evidence library is populated with clinical decision rules, professional society guidelines, and locally developed best practice guidelines. Individual recommendations were graded based on a standard methodology and compared using chi-square test. Strength of evidence ranged from grade 1 (systematic review) through grade 5 (recommendations based on expert opinion). Finally, variations in the underlying representation of these recommendations were identified. The library contains 546 individual imaging-related recommendations. Only 15% (16/106) of recommendations from clinical decision rules were grade 5 vs 83% (526/636) from professional society practice guidelines and local best practice guidelines that cited grade 5 studies (P < .0001). Minor head trauma, pulmonary embolism, and appendicitis were topic areas supported by the highest quality of evidence. Three main variations in underlying representations of recommendations were "single-decision," "branching," and "score-based." Most recommendations were grade 5, largely because studies to test and validate many recommendations were absent. Recommendation types vary in amount and complexity and, accordingly, the structure and syntax of statements they generate. However, they can be represented in single-decision, branching, and score-based representations. In a curated evidence library with graded imaging-based recommendations, evidence quality varied widely, with decision rules providing the highest-quality recommendations. The library may be helpful in highlighting evidence gaps, comparing recommendations from varied sources on similar clinical topics, and prioritizing imaging recommendations to inform clinical decision support implementation.

Effect of clinical decision rules, patient cost and malpractice information on clinician brain CT image ordering: a randomized controlled trial.

PubMed

Gimbel, Ronald W; Pirrallo, Ronald G; Lowe, Steven C; Wright, David W; Zhang, Lu; Woo, Min-Jae; Fontelo, Paul; Liu, Fang; Connor, Zachary

2018-03-12

The frequency of head computed tomography (CT) imaging for mild head trauma patients has raised safety and cost concerns. Validated clinical decision rules exist in the published literature and on-line sources to guide medical image ordering but are often not used by emergency department (ED) clinicians. Using simulation, we explored whether the presentation of a clinical decision rule (i.e. Canadian CT Head Rule - CCHR), findings from malpractice cases related to clinicians not ordering CT imaging in mild head trauma cases, and estimated patient out-of-pocket cost might influence clinician brain CT ordering. Understanding what type and how information may influence clinical decision making in the ordering advanced medical imaging is important in shaping the optimal design and implementation of related clinical decision support systems. Multi-center, double-blinded simulation-based randomized controlled trial. Following standardized clinical vignette presentation, clinicians made an initial imaging decision for the patient. This was followed by additional information on decision support rules, malpractice outcome review, and patient cost; each with opportunity to modify their initial order. The malpractice and cost information differed by assigned group to test the any temporal relationship. The simulation closed with a second vignette and an imaging decision. One hundred sixteen of the 167 participants (66.9%) initially ordered a brain CT scan. After CCHR presentation, the number of clinicians ordering a CT dropped to 76 (45.8%), representing a 21.1% reduction in CT ordering (P = 0.002). This reduction in CT ordering was maintained, in comparison to initial imaging orders, when presented with malpractice review information (p = 0.002) and patient cost information (p = 0.002). About 57% of clinicians changed their order during study, while 43% never modified their imaging order. This study suggests that ED clinician brain CT imaging decisions may be influenced by clinical decision support rules, patient out-of-pocket cost information and findings from malpractice case review. NCT03449862 , February 27, 2018, Retrospectively registered.

Rule-based optimization and multicriteria decision support for packaging a truck chassis

NASA Astrophysics Data System (ADS)

Berger, Martin; Lindroth, Peter; Welke, Richard

2017-06-01

Trucks are highly individualized products where exchangeable parts are flexibly combined to suit different customer requirements, this leading to a great complexity in product development. Therefore, an optimization approach based on constraint programming is proposed for automatically packaging parts of a truck chassis by following packaging rules expressed as constraints. A multicriteria decision support system is developed where a database of truck layouts is computed, among which interactive navigation then can be performed. The work has been performed in cooperation with Volvo Group Trucks Technology (GTT), from which specific rules have been used. Several scenarios are described where the methods developed can be successfully applied and lead to less time-consuming manual work, fewer mistakes, and greater flexibility in configuring trucks. A numerical evaluation is also presented showing the efficiency and practical relevance of the methods, which are implemented in a software tool.

From assessment to improvement of elderly care in general practice using decision support to increase adherence to ACOVE quality indicators: study protocol for randomized control trial

PubMed Central

2014-01-01

Background Previous efforts such as Assessing Care of Vulnerable Elders (ACOVE) provide quality indicators for assessing the care of elderly patients, but thus far little has been done to leverage this knowledge to improve care for these patients. We describe a clinical decision support system to improve general practitioner (GP) adherence to ACOVE quality indicators and a protocol for investigating impact on GPs’ adherence to the rules. Design We propose two randomized controlled trials among a group of Dutch GP teams on adherence to ACOVE quality indicators. In both trials a clinical decision support system provides un-intrusive feedback appearing as a color-coded, dynamically updated, list of items needing attention. The first trial pertains to real-time automatically verifiable rules. The second trial concerns non-automatically verifiable rules (adherence cannot be established by the clinical decision support system itself, but the GPs report whether they will adhere to the rules). In both trials we will randomize teams of GPs caring for the same patients into two groups, A and B. For the automatically verifiable rules, group A GPs receive support only for a specific inter-related subset of rules, and group B GPs receive support only for the remainder of the rules. For non-automatically verifiable rules, group A GPs receive feedback framed as actions with positive consequences, and group B GPs receive feedback framed as inaction with negative consequences. GPs indicate whether they adhere to non-automatically verifiable rules. In both trials, the main outcome measure is mean adherence, automatically derived or self-reported, to the rules. Discussion We relied on active end-user involvement in selecting the rules to support, and on a model for providing feedback displayed as color-coded real-time messages concerning the patient visiting the GP at that time, without interrupting the GP’s workflow with pop-ups. While these aspects are believed to increase clinical decision support system acceptance and its impact on adherence to the selected clinical rules, systems with these properties have not yet been evaluated. Trial registration Controlled Trials NTR3566 PMID:24642339

A Fuzzy Rule Based Decision Support System for Identifying Location of Water Harvesting Technologies in Rainfed Agricultural Regions

NASA Astrophysics Data System (ADS)

Chaubey, I.; Vema, V. K.; Sudheer, K.

2016-12-01

Site suitability evaluation of water conservation structures in water scarce rainfed agricultural areas consist of assessment of various landscape characteristics and various criterion. Many of these landscape characteristic attributes are conveyed through linguistic terms rather than precise numeric values. Fuzzy rule based system are capable of incorporating uncertainty and vagueness, when various decision making criteria expressed in linguistic terms are expressed as fuzzy rules. In this study a fuzzy rule based decision support system is developed, for optimal site selection of water harvesting technologies. Water conservation technologies like farm ponds, Check dams, Rock filled dams and percolation ponds aid in conserving water for irrigation and recharging aquifers and development of such a system will aid in improving the efficiency of the structures. Attributes and criteria involved in decision making are classified into different groups to estimate the suitability of the particular technology. The developed model is applied and tested on an Indian watershed. The input attributes are prepared in raster format in ArcGIS software and suitability of each raster cell is calculated and output is generated in the form of a thematic map showing the suitability of the cells pertaining to different technologies. The output of the developed model is compared against the already existing structures and results are satisfactory. This developed model will aid in improving the sustainability and efficiency of the watershed management programs aimed at enhancing in situ moisture content.

System for selecting relevant information for decision support.

PubMed

Kalina, Jan; Seidl, Libor; Zvára, Karel; Grünfeldová, Hana; Slovák, Dalibor; Zvárová, Jana

2013-01-01

We implemented a prototype of a decision support system called SIR which has a form of a web-based classification service for diagnostic decision support. The system has the ability to select the most relevant variables and to learn a classification rule, which is guaranteed to be suitable also for high-dimensional measurements. The classification system can be useful for clinicians in primary care to support their decision-making tasks with relevant information extracted from any available clinical study. The implemented prototype was tested on a sample of patients in a cardiological study and performs an information extraction from a high-dimensional set containing both clinical and gene expression data.

On Decision-Making Among Multiple Rule-Bases in Fuzzy Control Systems

NASA Technical Reports Server (NTRS)

Tunstel, Edward; Jamshidi, Mo

1997-01-01

Intelligent control of complex multi-variable systems can be a challenge for single fuzzy rule-based controllers. This class of problems cam often be managed with less difficulty by distributing intelligent decision-making amongst a collection of rule-bases. Such an approach requires that a mechanism be chosen to ensure goal-oriented interaction between the multiple rule-bases. In this paper, a hierarchical rule-based approach is described. Decision-making mechanisms based on generalized concepts from single-rule-based fuzzy control are described. Finally, the effects of different aggregation operators on multi-rule-base decision-making are examined in a navigation control problem for mobile robots.

Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

PubMed

Parodi, Stefano; Manneschi, Chiara; Verda, Damiano; Ferrari, Enrico; Muselli, Marco

2018-03-01

This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms ( k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene ( XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin's lymphoma patients.

Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network.

PubMed

Yang, Zhongliang; Huang, Yongfeng; Jiang, Yiran; Sun, Yuxi; Zhang, Yu-Jin; Luo, Pengcheng

2018-04-20

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.

Intelligent Case Based Decision Support System for Online Diagnosis of Automated Production System

NASA Astrophysics Data System (ADS)

Ben Rabah, N.; Saddem, R.; Ben Hmida, F.; Carre-Menetrier, V.; Tagina, M.

2017-01-01

Diagnosis of Automated Production System (APS) is a decision-making process designed to detect, locate and identify a particular failure caused by the control law. In the literature, there are three major types of reasoning for industrial diagnosis: the first is model-based, the second is rule-based and the third is case-based. The common and major limitation of the first and the second reasonings is that they do not have automated learning ability. This paper presents an interactive and effective Case Based Decision Support System for online Diagnosis (CB-DSSD) of an APS. It offers a synergy between the Case Based Reasoning (CBR) and the Decision Support System (DSS) in order to support and assist Human Operator of Supervision (HOS) in his/her decision process. Indeed, the experimental evaluation performed on an Interactive Training System for PLC (ITS PLC) that allows the control of a Programmable Logic Controller (PLC), simulating sensors or/and actuators failures and validating the control algorithm through a real time interactive experience, showed the efficiency of our approach.

An HL7-CDA wrapper for facilitating semantic interoperability to rule-based Clinical Decision Support Systems.

PubMed

Sáez, Carlos; Bresó, Adrián; Vicente, Javier; Robles, Montserrat; García-Gómez, Juan Miguel

2013-03-01

The success of Clinical Decision Support Systems (CDSS) greatly depends on its capability of being integrated in Health Information Systems (HIS). Several proposals have been published up to date to permit CDSS gathering patient data from HIS. Some base the CDSS data input on the HL7 reference model, however, they are tailored to specific CDSS or clinical guidelines technologies, or do not focus on standardizing the CDSS resultant knowledge. We propose a solution for facilitating semantic interoperability to rule-based CDSS focusing on standardized input and output documents conforming an HL7-CDA wrapper. We define the HL7-CDA restrictions in a HL7-CDA implementation guide. Patient data and rule inference results are mapped respectively to and from the CDSS by means of a binding method based on an XML binding file. As an independent clinical document, the results of a CDSS can present clinical and legal validity. The proposed solution is being applied in a CDSS for providing patient-specific recommendations for the care management of outpatients with diabetes mellitus. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Analytic and rule-based decision support tool for VDT workstation adjustment and computer accessories arrangement.

PubMed

Rurkhamet, Busagarin; Nanthavanij, Suebsak

2004-12-01

One important factor that leads to the development of musculoskeletal disorders (MSD) and cumulative trauma disorders (CTD) among visual display terminal (VDT) users is their work posture. While operating a VDT, a user's body posture is strongly influenced by the task, VDT workstation settings, and layout of computer accessories. This paper presents an analytic and rule-based decision support tool called EQ-DeX (an ergonomics and quantitative design expert system) that is developed to provide valid and practical recommendations regarding the adjustment of a VDT workstation and the arrangement of computer accessories. The paper explains the structure and components of EQ-DeX, input data, rules, and adjustment and arrangement algorithms. From input information such as gender, age, body height, task, etc., EQ-DeX uses analytic and rule-based algorithms to estimate quantitative settings of a computer table and a chair, as well as locations of computer accessories such as monitor, document holder, keyboard, and mouse. With the input and output screens that are designed using the concept of usability, the interactions between the user and EQ-DeX are convenient. Examples are also presented to demonstrate the recommendations generated by EQ-DeX.

Rule acquisition in formal decision contexts based on formal, object-oriented and property-oriented concept lattices.

PubMed

Ren, Yue; Li, Jinhai; Aswani Kumar, Cherukuri; Liu, Wenqi

2014-01-01

Rule acquisition is one of the main purposes in the analysis of formal decision contexts. Up to now, there have been several types of rules in formal decision contexts such as decision rules, decision implications, and granular rules, which can be viewed as ∧-rules since all of them have the following form: "if conditions 1,2,…, and m hold, then decisions hold." In order to enrich the existing rule acquisition theory in formal decision contexts, this study puts forward two new types of rules which are called ∨-rules and ∨-∧ mixed rules based on formal, object-oriented, and property-oriented concept lattices. Moreover, a comparison of ∨-rules, ∨-∧ mixed rules, and ∧-rules is made from the perspectives of inclusion and inference relationships. Finally, some real examples and numerical experiments are conducted to compare the proposed rule acquisition algorithms with the existing one in terms of the running efficiency.

Rule Acquisition in Formal Decision Contexts Based on Formal, Object-Oriented and Property-Oriented Concept Lattices

PubMed Central

Ren, Yue; Aswani Kumar, Cherukuri; Liu, Wenqi

2014-01-01

Rule acquisition is one of the main purposes in the analysis of formal decision contexts. Up to now, there have been several types of rules in formal decision contexts such as decision rules, decision implications, and granular rules, which can be viewed as ∧-rules since all of them have the following form: “if conditions 1,2,…, and m hold, then decisions hold.” In order to enrich the existing rule acquisition theory in formal decision contexts, this study puts forward two new types of rules which are called ∨-rules and ∨-∧ mixed rules based on formal, object-oriented, and property-oriented concept lattices. Moreover, a comparison of ∨-rules, ∨-∧ mixed rules, and ∧-rules is made from the perspectives of inclusion and inference relationships. Finally, some real examples and numerical experiments are conducted to compare the proposed rule acquisition algorithms with the existing one in terms of the running efficiency. PMID:25165744

Implementing a Commercial Rule Base as a Medication Order Safety Net

PubMed Central

Reichley, Richard M.; Seaton, Terry L.; Resetar, Ervina; Micek, Scott T.; Scott, Karen L.; Fraser, Victoria J.; Dunagan, W. Claiborne; Bailey, Thomas C.

2005-01-01

A commercial rule base (Cerner Multum) was used to identify medication orders exceeding recommended dosage limits at five hospitals within BJC HealthCare, an integrated health care system. During initial testing, clinical pharmacists determined that there was an excessive number of nuisance and clinically insignificant alerts, with an overall alert rate of 9.2%. A method for customizing the commercial rule base was implemented to increase rule specificity for problematic rules. The system was subsequently deployed at two facilities and achieved alert rates of less than 1%. Pharmacists screened these alerts and contacted ordering physicians in 21% of cases. Physicians made therapeutic changes in response to 38% of alerts presented to them. By applying simple techniques to customize rules, commercial rule bases can be used to rapidly deploy a safety net to screen drug orders for excessive dosages, while preserving the rule architecture for later implementations of more finely tuned clinical decision support. PMID:15802481

A study of diverse clinical decision support rule authoring environments and requirements for integration

PubMed Central

2012-01-01

Background Efficient rule authoring tools are critical to allow clinical Knowledge Engineers (KEs), Software Engineers (SEs), and Subject Matter Experts (SMEs) to convert medical knowledge into machine executable clinical decision support rules. The goal of this analysis was to identify the critical success factors and challenges of a fully functioning Rule Authoring Environment (RAE) in order to define requirements for a scalable, comprehensive tool to manage enterprise level rules. Methods The authors evaluated RAEs in active use across Partners Healthcare, including enterprise wide, ambulatory only, and system specific tools, with a focus on rule editors for reminder and medication rules. We conducted meetings with users of these RAEs to discuss their general experience and perceived advantages and limitations of these tools. Results While the overall rule authoring process is similar across the 10 separate RAEs, the system capabilities and architecture vary widely. Most current RAEs limit the ability of the clinical decision support (CDS) interventions to be standardized, sharable, interoperable, and extensible. No existing system meets all requirements defined by knowledge management users. Conclusions A successful, scalable, integrated rule authoring environment will need to support a number of key requirements and functions in the areas of knowledge representation, metadata, terminology, authoring collaboration, user interface, integration with electronic health record (EHR) systems, testing, and reporting. PMID:23145874

Combining the Generic Entity-Attribute-Value Model and Terminological Models into a Common Ontology to Enable Data Integration and Decision Support.

PubMed

Bouaud, Jacques; Guézennec, Gilles; Séroussi, Brigitte

2018-01-01

The integration of clinical information models and termino-ontological models into a unique ontological framework is highly desirable for it facilitates data integration and management using the same formal mechanisms for both data concepts and information model components. This is particularly true for knowledge-based decision support tools that aim to take advantage of all facets of semantic web technologies in merging ontological reasoning, concept classification, and rule-based inferences. We present an ontology template that combines generic data model components with (parts of) existing termino-ontological resources. The approach is developed for the guideline-based decision support module on breast cancer management within the DESIREE European project. The approach is based on the entity attribute value model and could be extended to other domains.

Assessing an AI knowledge-base for asymptomatic liver diseases.

PubMed

Babic, A; Mathiesen, U; Hedin, K; Bodemar, G; Wigertz, O

1998-01-01

Discovering not yet seen knowledge from clinical data is of importance in the field of asymptomatic liver diseases. Avoidance of liver biopsy which is used as the ultimate confirmation of diagnosis by making the decision based on relevant laboratory findings only, would be considered an essential support. The system based on Quinlan's ID3 algorithm was simple and efficient in extracting the sought knowledge. Basic principles of applying the AI systems are therefore described and complemented with medical evaluation. Some of the diagnostic rules were found to be useful as decision algorithms i.e. they could be directly applied in clinical work and made a part of the knowledge-base of the Liver Guide, an automated decision support system.

Employing clinical decision support to attain our strategic goal: the safe care of the surgical patient.

PubMed

Magid, Steven K; Pancoast, Paul E; Fields, Theodore; Bradley, Diane G; Williams, Robert B

2007-01-01

Clinical decision support can be employed to increase patient safety and improve workflow efficiencies for physicians and other healthcare providers. Physician input into the design and deployment of clinical decision support systems can increase the utility of the alerts and reduce the likelihood of "alert fatigue." The Hospital for Special Surgery is a 146-bed orthopedic facility that performs approximately 18,000 surgeries a year Efficient work processes are a necessity. The facility began implementing a new electronic health record system in June 2005 and plan to go live in summer 2007. This article reports on some of the clinical decision support rules and alerts being incorporated into the facility's system in the following categories--high-risk, high-frequency scenarios, rules that provide efficiencies and value from the presciber perspective, and rules that relate to patient safety.

Minimum Equipment Lists, Flight Rules and ... Past, Present and Future of Safety Pre-Determined Decisions for Operations

NASA Astrophysics Data System (ADS)

Herd, A.; Wolff, M.

2012-01-01

Extended mission operations, such as human spaceflight to Mars provide an opportunity for take current human exploration beyond Low Earth Orbit, such as the operations undertaken on the International Space Station (ISS). This opportunity also presents a challenge in terms of extending what we currently understand as "remote operations" performed on ISS, offering learning beyond that gained from the successful moon- lander expeditions. As such there is a need to assess how the existing operations concept of ground support teams directing (and supporting) on-orbit ISS operations can be applied in the extended mission concept. The current mission support concept involves three interacting operations products - a short term plan, crew procedures and flight rules. Flight rules (for ISS operations) currently provide overall planning, engineering and operations constraints (including those derived from a safety perspective) in the form of a rule book. This paper will focus specifically on flight rules, and describe the current use of them, and assess the future role of flight rules to support exploration, including the deployment of decision support tools (DSTs) to ensure flight rule compliancy for missions with minimal ground support. Taking consideration of the historical development of pre-planned decisions, and their manifestation within the operations environment, combined with the extended remoteness of human exploration missions, we will propose a future development of this product and a platform on which it could be presented.

Emergency physicians' attitudes toward and use of clinical decision rules for radiography.

PubMed

Graham, I D; Stiell, I G; Laupacis, A; O'Connor, A M; Wells, G A

1998-02-01

1) To assess Canadian emergency physicians' (EPs') use of and attitudes toward 2 radiographic clinical decision rules that have recently been developed and to identify physician characteristics associated with decision rule use; 2) to determine the use of CT head and cervical spine radiography by EPs and their beliefs about the appropriateness of expert recommendations supporting the routine use of these radiographic procedures; and 3) to determine the potential acceptance of clinical decision rules for CT scan in patients with minor head injury and cervical spine radiography in trauma patients. A cross-sectional anonymous mail survey of a random sample of 300 members of the Canadian Association of Emergency Physicians using Dillman's Total Design Method for mail surveys. Of 288 eligible physicians, 232 (81%) responded. More than 95% of the respondents stated they currently used the Ottawa Ankle Rules and were willing to consider using the newly developed Ottawa Knee Rule. Physician characteristics related to frequent use of the Ottawa Ankle Rules were younger age, fewer years since graduating from medical school, part time or resident employment status, working in a hospital without a CT scanner, and believing that decision rules are not oversimplified cookbook medicine or too rigid to apply. Eighty-five percent did not agree that all patients with minor head injuries should receive a CT head scan and only 3.5% stated they always refer such patients for CT scan. Similarly, 78.5% of the respondents did not agree that all trauma patients should receive cervical spine radiography and only 13.2% said they always refer such patients for cervical spine radiography. Ninety-seven and 98% stated they would be willing to consider using well-validated decision rules for CT scan of the head and cervical spine radiography, respectively. Fifty-two percent and 67% of the respondents required the proposed CT and C-spine to be 100% sensitive for identifying serious injuries, respectively. Canadian EPs are generally supportive of clinical decision rules and, in particular, have very positive attitudes toward the Ottawa Ankle and Knee Rules. Furthermore, EPs disagree with recommendations for routine use of CT head and cervical spine radiography and strongly support the development of well-validated decision rules for the use of CT head and cervical spine radiography. Most EPs expected the latter rules to be 100% sensitive for acute clinically significant lesions.

SAMS--a systems architecture for developing intelligent health information systems.

PubMed

Yılmaz, Özgün; Erdur, Rıza Cenk; Türksever, Mustafa

2013-12-01

In this paper, SAMS, a novel health information system architecture for developing intelligent health information systems is proposed and also some strategies for developing such systems are discussed. The systems fulfilling this architecture will be able to store electronic health records of the patients using OWL ontologies, share patient records among different hospitals and provide physicians expertise to assist them in making decisions. The system is intelligent because it is rule-based, makes use of rule-based reasoning and has the ability to learn and evolve itself. The learning capability is provided by extracting rules from previously given decisions by the physicians and then adding the extracted rules to the system. The proposed system is novel and original in all of these aspects. As a case study, a system is implemented conforming to SAMS architecture for use by dentists in the dental domain. The use of the developed system is described with a scenario. For evaluation, the developed dental information system will be used and tried by a group of dentists. The development of this system proves the applicability of SAMS architecture. By getting decision support from a system derived from this architecture, the cognitive gap between experienced and inexperienced physicians can be compensated. Thus, patient satisfaction can be achieved, inexperienced physicians are supported in decision making and the personnel can improve their knowledge. A physician can diagnose a case, which he/she has never diagnosed before, using this system. With the help of this system, it will be possible to store general domain knowledge in this system and the personnel's need to medical guideline documents will be reduced.

Optimal operating rules definition in complex water resource systems combining fuzzy logic, expert criteria and stochastic programming

NASA Astrophysics Data System (ADS)

Macian-Sorribes, Hector; Pulido-Velazquez, Manuel

2016-04-01

This contribution presents a methodology for defining optimal seasonal operating rules in multireservoir systems coupling expert criteria and stochastic optimization. Both sources of information are combined using fuzzy logic. The structure of the operating rules is defined based on expert criteria, via a joint expert-technician framework consisting in a series of meetings, workshops and surveys carried out between reservoir managers and modelers. As a result, the decision-making process used by managers can be assessed and expressed using fuzzy logic: fuzzy rule-based systems are employed to represent the operating rules and fuzzy regression procedures are used for forecasting future inflows. Once done that, a stochastic optimization algorithm can be used to define optimal decisions and transform them into fuzzy rules. Finally, the optimal fuzzy rules and the inflow prediction scheme are combined into a Decision Support System for making seasonal forecasts and simulate the effect of different alternatives in response to the initial system state and the foreseen inflows. The approach presented has been applied to the Jucar River Basin (Spain). Reservoir managers explained how the system is operated, taking into account the reservoirs' states at the beginning of the irrigation season and the inflows previewed during that season. According to the information given by them, the Jucar River Basin operating policies were expressed via two fuzzy rule-based (FRB) systems that estimate the amount of water to be allocated to the users and how the reservoir storages should be balanced to guarantee those deliveries. A stochastic optimization model using Stochastic Dual Dynamic Programming (SDDP) was developed to define optimal decisions, which are transformed into optimal operating rules embedding them into the two FRBs previously created. As a benchmark, historical records are used to develop alternative operating rules. A fuzzy linear regression procedure was employed to foresee future inflows depending on present and past hydrological and meteorological variables actually used by the reservoir managers to define likely inflow scenarios. A Decision Support System (DSS) was created coupling the FRB systems and the inflow prediction scheme in order to give the user a set of possible optimal releases in response to the reservoir states at the beginning of the irrigation season and the fuzzy inflow projections made using hydrological and meteorological information. The results show that the optimal DSS created using the FRB operating policies are able to increase the amount of water allocated to the users in 20 to 50 Mm3 per irrigation season with respect to the current policies. Consequently, the mechanism used to define optimal operating rules and transform them into a DSS is able to increase the water deliveries in the Jucar River Basin, combining expert criteria and optimization algorithms in an efficient way. This study has been partially supported by the IMPADAPT project (CGL2013-48424-C2-1-R) with Spanish MINECO (Ministerio de Economía y Competitividad) and FEDER funds. It also has received funding from the European Union's Horizon 2020 research and innovation programme under the IMPREX project (grant agreement no: 641.811).

Symbolic rule-based classification of lung cancer stages from free-text pathology reports.

PubMed

Nguyen, Anthony N; Lawley, Michael J; Hansen, David P; Bowman, Rayleen V; Clarke, Belinda E; Duhig, Edwina E; Colquist, Shoni

2010-01-01

To classify automatically lung tumor-node-metastases (TNM) cancer stages from free-text pathology reports using symbolic rule-based classification. By exploiting report substructure and the symbolic manipulation of systematized nomenclature of medicine-clinical terms (SNOMED CT) concepts in reports, statements in free text can be evaluated for relevance against factors relating to the staging guidelines. Post-coordinated SNOMED CT expressions based on templates were defined and populated by concepts in reports, and tested for subsumption by staging factors. The subsumption results were used to build logic according to the staging guidelines to calculate the TNM stage. The accuracy measure and confusion matrices were used to evaluate the TNM stages classified by the symbolic rule-based system. The system was evaluated against a database of multidisciplinary team staging decisions and a machine learning-based text classification system using support vector machines. Overall accuracy on a corpus of pathology reports for 718 lung cancer patients against a database of pathological TNM staging decisions were 72%, 78%, and 94% for T, N, and M staging, respectively. The system's performance was also comparable to support vector machine classification approaches. A system to classify lung TNM stages from free-text pathology reports was developed, and it was verified that the symbolic rule-based approach using SNOMED CT can be used for the extraction of key lung cancer characteristics from free-text reports. Future work will investigate the applicability of using the proposed methodology for extracting other cancer characteristics and types.

Comparison of Computer-based Clinical Decision Support Systems and Content for Diabetes Mellitus.

PubMed

Kantor, M; Wright, A; Burton, M; Fraser, G; Krall, M; Maviglia, S; Mohammed-Rajput, N; Simonaitis, L; Sonnenberg, F; Middleton, B

2011-01-01

Computer-based clinical decision support (CDS) systems have been shown to improve quality of care and workflow efficiency, and health care reform legislation relies on electronic health records and CDS systems to improve the cost and quality of health care in the United States; however, the heterogeneity of CDS content and infrastructure of CDS systems across sites is not well known. We aimed to determine the scope of CDS content in diabetes care at six sites, assess the capabilities of CDS in use at these sites, characterize the scope of CDS infrastructure at these sites, and determine how the sites use CDS beyond individual patient care in order to identify characteristics of CDS systems and content that have been successfully implemented in diabetes care. We compared CDS systems in six collaborating sites of the Clinical Decision Support Consortium. We gathered CDS content on care for patients with diabetes mellitus and surveyed institutions on characteristics of their site, the infrastructure of CDS at these sites, and the capabilities of CDS at these sites. The approach to CDS and the characteristics of CDS content varied among sites. Some commonalities included providing customizability by role or user, applying sophisticated exclusion criteria, and using CDS automatically at the time of decision-making. Many messages were actionable recommendations. Most sites had monitoring rules (e.g. assessing hemoglobin A1c), but few had rules to diagnose diabetes or suggest specific treatments. All sites had numerous prevention rules including reminders for providing eye examinations, influenza vaccines, lipid screenings, nephropathy screenings, and pneumococcal vaccines. Computer-based CDS systems vary widely across sites in content and scope, but both institution-created and purchased systems had many similar features and functionality, such as integration of alerts and reminders into the decision-making workflow of the provider and providing messages that are actionable recommendations.

Clinical decision rules for termination of resuscitation in out-of-hospital cardiac arrest.

PubMed

Sherbino, Jonathan; Keim, Samuel M; Davis, Daniel P

2010-01-01

Out-of-hospital cardiac arrest (OHCA) has a low probability of survival to hospital discharge. Four clinical decision rules (CDRs) have been validated to identify patients with no probability of survival. Three of these rules focus on exclusive prehospital basic life support care for OHCA, and two of these rules focus on prehospital advanced life support care for OHCA. Can a CDR for the termination of resuscitation identify a patient with no probability of survival in the setting of OHCA? Six validation studies were selected from a PubMed search. A structured review of each of the studies is presented. In OHCA receiving basic life support care, the BLS-TOR (basic life support termination of resuscitation) rule has a positive predictive value for death of 99.5% (95% confidence interval 98.9-99.8%), and decreases the transportation of all patients by 62.6%. This rule has been appropriately validated for widespread use. In OHCA receiving advanced life support care, no current rule has been appropriately validated for widespread use. The BLS-TOR rule is a simple rule that identifies patients who will not survive OHCA. Further research is required to identify similarly robust CDRs for patients receiving advanced life support care in the setting of OHCA. Copyright 2010 Elsevier Inc. All rights reserved.

Heart failure analysis dashboard for patient's remote monitoring combining multiple artificial intelligence technologies.

PubMed

Guidi, G; Pettenati, M C; Miniati, R; Iadanza, E

2012-01-01

In this paper we describe an Heart Failure analysis Dashboard that, combined with a handy device for the automatic acquisition of a set of patient's clinical parameters, allows to support telemonitoring functions. The Dashboard's intelligent core is a Computer Decision Support System designed to assist the clinical decision of non-specialist caring personnel, and it is based on three functional parts: Diagnosis, Prognosis, and Follow-up management. Four Artificial Intelligence-based techniques are compared for providing diagnosis function: a Neural Network, a Support Vector Machine, a Classification Tree and a Fuzzy Expert System whose rules are produced by a Genetic Algorithm. State of the art algorithms are used to support a score-based prognosis function. The patient's Follow-up is used to refine the diagnosis.

Estimating Classification Accuracy for Complex Decision Rules Based on Multiple Scores

ERIC Educational Resources Information Center

Douglas, Karen M.; Mislevy, Robert J.

2010-01-01

Important decisions about students are made by combining multiple measures using complex decision rules. Although methods for characterizing the accuracy of decisions based on a single measure have been suggested by numerous researchers, such methods are not useful for estimating the accuracy of decisions based on multiple measures. This study…

A Review of Decision Support Systems for Smart Homes in the Health Care System.

PubMed

Baumgärtel, Diana; Mielke, Corinna; Haux, Reinhold

2018-01-01

The use of decision support systems for smart homes can provide attractive solutions for challenges that have arisen in the Health Care System due to ageing of society. In order to provide an overview of current research projects in this field, a systematic literature review was performed according to the PRISMA approach. The aims of this work are to provide an overview of current research projects and to update a similar study from 2012. The literature search engines IEEE Xplore and PubMed were used. 23 papers were included. Most of the systems presented are developed for monitoring the patient regardless of their illness. For decision support, mainly rule-based approaches are used.

Role of Big Data and Machine Learning in Diagnostic Decision Support in Radiology.

PubMed

Syeda-Mahmood, Tanveer

2018-03-01

The field of diagnostic decision support in radiology is undergoing rapid transformation with the availability of large amounts of patient data and the development of new artificial intelligence methods of machine learning such as deep learning. They hold the promise of providing imaging specialists with tools for improving the accuracy and efficiency of diagnosis and treatment. In this article, we will describe the growth of this field for radiology and outline general trends highlighting progress in the field of diagnostic decision support from the early days of rule-based expert systems to cognitive assistants of the modern era. Copyright © 2018 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Towards a Decision Support Tool for 3d Visualisation: Application to Selectivity Purpose of Single Object in a 3d City Scene

NASA Astrophysics Data System (ADS)

Neuville, R.; Pouliot, J.; Poux, F.; Hallot, P.; De Rudder, L.; Billen, R.

2017-10-01

This paper deals with the establishment of a comprehensive methodological framework that defines 3D visualisation rules and its application in a decision support tool. Whilst the use of 3D models grows in many application fields, their visualisation remains challenging from the point of view of mapping and rendering aspects to be applied to suitability support the decision making process. Indeed, there exists a great number of 3D visualisation techniques but as far as we know, a decision support tool that facilitates the production of an efficient 3D visualisation is still missing. This is why a comprehensive methodological framework is proposed in order to build decision tables for specific data, tasks and contexts. Based on the second-order logic formalism, we define a set of functions and propositions among and between two collections of entities: on one hand static retinal variables (hue, size, shape…) and 3D environment parameters (directional lighting, shadow, haze…) and on the other hand their effect(s) regarding specific visual tasks. It enables to define 3D visualisation rules according to four categories: consequence, compatibility, potential incompatibility and incompatibility. In this paper, the application of the methodological framework is demonstrated for an urban visualisation at high density considering a specific set of entities. On the basis of our analysis and the results of many studies conducted in the 3D semiotics, which refers to the study of symbols and how they relay information, the truth values of propositions are determined. 3D visualisation rules are then extracted for the considered context and set of entities and are presented into a decision table with a colour coding. Finally, the decision table is implemented into a plugin developed with three.js, a cross-browser JavaScript library. The plugin consists of a sidebar and warning windows that help the designer in the use of a set of static retinal variables and 3D environment parameters.

Towards a decision support system for hand dermatology.

PubMed

Mazzola, Luca; Cavazzina, Alice; Pinciroli, Francesco; Bonacina, Stefano; Pigatto, Paolo; Ayala, Fabio; De Pità, Ornella; Marceglia, Sara

2014-01-01

The complexity of the medical diagnosis is faced by practitioners relying mainly on their experiences. This can be acquired during daily practices and on-the-job training. Given the complexity and extensiveness of the subject, supporting tools that include knowledge extracted by highly specialized practitioners can be valuable. In the present work, a Decision Support System (DSS) for hand dermatology was developed based on data coming from a Visit Report Form (VRF). Using a Bayesian approach and factors significance difference over the population average for the case, we demonstrated the potentiality of creating an enhanced VRF that include a diagnoses distribution probability based on the DSS rules applied for the specific patient situation.

Knowledge Translation of the PERC Rule for Suspected Pulmonary Embolism: A Blueprint for Reducing the Number of CT Pulmonary Angiograms.

PubMed

Drescher, Michael J; Fried, Jeremy; Brass, Ryan; Medoro, Amanda; Murphy, Timothy; Delgado, João

2017-10-01

Computerized decision support decreases the number of computed tomography pulmonary angiograms (CTPA) for pulmonary embolism (PE) ordered in emergency departments, but it is not always well accepted by emergency physicians. We studied a department-endorsed, evidence-based clinical protocol that included the PE rule-out criteria (PERC) rule, multi-modal education using principles of knowledge translation (KT), and clinical decision support embedded in our order entry system, to decrease the number of unnecessary CTPA ordered. We performed a historically controlled observational before-after study for one year pre- and post-implementation of a departmentally-endorsed protocol. We included patients > 18 in whom providers suspected PE and who did not have a contraindication to CTPA. Providers entered clinical information into a diagnostic pathway via computerized order entry. Prior to protocol implementation, we provided education to ordering providers. The primary outcome measure was the number of CTPA ordered per 1,000 visits one year before vs. after implementation. CTPA declined from 1,033 scans for 98,028 annual visits (10.53 per 1,000 patient visits (95% CI [9.9-11.2]) to 892 scans for 101,172 annual visits (8.81 per 1,000 patient visits (95% CI [8.3-9.4]) p<0.001. The absolute reduction in PACT ordered was 1.72 per 1,000 visits (a 16% reduction). Patient characteristics were similar for both periods. Knowledge translation clinical decision support using the PERC rule significantly reduced the number of CTPA ordered.

General practitioner diagnosis and management of acute knee injuries: summary of an evidence-based guideline.

PubMed

Robb, Gillian; Reid, Duncan; Arroll, Bruce; Jackson, Rod T; Goodyear-Smith, Felicity

2007-02-16

To summarise evidence and key recommendations for general practitioner diagnosis and management of acute soft-tissue knee injuries, based on the New Zealand guideline. A multidisciplinary team developed the guideline by critically appraising and grading retrieved literature using the Graphic Appraisal Tools for Epidemiology, Clinical decision rules and the Scottish Intercollegiate Guideline Network. Recommendations were derived from resulting evidence tables. For both diagnosis and management there is a paucity of good evidence to support diagnosis and treatment of internal derangements of the knee, hence some aspects of the guideline are guideline team consensus. Good evidence supports the use of the Ottawa Knee rules to guide decisions about the use of X-ray, and the Lachman test in diagnosing anterior cruciate ligament (ACL) tears. Evidence supports inclusion of proprioceptive training in rehabilitation programmes following ACL reconstruction and in people with ACL-deficient knees. There is good evidence that ultrasound is of little benefit, and there is no evidence that physiotherapy be routinely advocated following meniscectomy. This guideline provides an evidence-based framework for diagnosis and management of internal derangements of the knee following acute injury. Moreover, its development highlights significant gaps in the evidence base and identifies priorities for new research.

Automated rule-base creation via CLIPS-Induce

NASA Technical Reports Server (NTRS)

Murphy, Patrick M.

1994-01-01

Many CLIPS rule-bases contain one or more rule groups that perform classification. In this paper we describe CLIPS-Induce, an automated system for the creation of a CLIPS classification rule-base from a set of test cases. CLIPS-Induce consists of two components, a decision tree induction component and a CLIPS production extraction component. ID3, a popular decision tree induction algorithm, is used to induce a decision tree from the test cases. CLIPS production extraction is accomplished through a top-down traversal of the decision tree. Nodes of the tree are used to construct query rules, and branches of the tree are used to construct classification rules. The learned CLIPS productions may easily be incorporated into a large CLIPS system that perform tasks such as accessing a database or displaying information.

Clinical decision rules, spinal pain classification and prediction of treatment outcome: A discussion of recent reports in the rehabilitation literature

PubMed Central

2012-01-01

Clinical decision rules are an increasingly common presence in the biomedical literature and represent one strategy of enhancing clinical-decision making with the goal of improving the efficiency and effectiveness of healthcare delivery. In the context of rehabilitation research, clinical decision rules have been predominantly aimed at classifying patients by predicting their treatment response to specific therapies. Traditionally, recommendations for developing clinical decision rules propose a multistep process (derivation, validation, impact analysis) using defined methodology. Research efforts aimed at developing a “diagnosis-based clinical decision rule” have departed from this convention. Recent publications in this line of research have used the modified terminology “diagnosis-based clinical decision guide.” Modifications to terminology and methodology surrounding clinical decision rules can make it more difficult for clinicians to recognize the level of evidence associated with a decision rule and understand how this evidence should be implemented to inform patient care. We provide a brief overview of clinical decision rule development in the context of the rehabilitation literature and two specific papers recently published in Chiropractic and Manual Therapies. PMID:22726639

Hybrid modeling of nitrate fate in large catchments using fuzzy-rules

NASA Astrophysics Data System (ADS)

van der Heijden, Sven; Haberlandt, Uwe

2010-05-01

Especially for nutrient balance simulations, physically based ecohydrological modeling needs an abundance of measured data and model parameters, which for large catchments all too often are not available in sufficient spatial or temporal resolution or are simply unknown. For efficient large-scale studies it is thus beneficial to have methods at one's disposal which are parsimonious concerning the number of model parameters and the necessary input data. One such method is fuzzy-rule based modeling, which compared to other machine-learning techniques has the advantages to produce models (the fuzzy-rules) which are physically interpretable to a certain extent, and to allow the explicit introduction of expert knowledge through pre-defined rules. The study focuses on the application of fuzzy-rule based modeling for nitrate simulation in large catchments, in particular concerning decision support. Fuzzy-rule based modeling enables the generation of simple, efficient, easily understandable models with nevertheless satisfactory accuracy for problems of decision support. The chosen approach encompasses a hybrid metamodeling, which includes the generation of fuzzy-rules with data originating from physically based models as well as a coupling with a physically based water balance model. For the generation of the needed training data and also as coupled water balance model the ecohydrological model SWAT is employed. The conceptual model divides the nitrate pathway into three parts. The first fuzzy-module calculates nitrate leaching with the percolating water from soil surface to groundwater, the second module simulates groundwater passage, and the final module replaces the in-stream processes. The aim of this modularization is to create flexibility for using each of the modules on its own, for changing or completely replacing it. For fuzzy-rule based modeling this can explicitly mean that the re-training of one of the modules with newly available data will be possible without problem, while the module assembly does not have to be modified. Apart from the concept of hybrid metamodeling first results are presented for the fuzzy-module for nitrate passage through the unsaturated zone.

Cross-vendor evaluation of key user-defined clinical decision support capabilities: a scenario-based assessment of certified electronic health records with guidelines for future development

PubMed Central

Wright, Adam; Sittig, Dean F

2015-01-01

Objective Clinical decision support (CDS) is essential for delivery of high-quality, cost-effective, and safe healthcare. The authors sought to evaluate the CDS capabilities across electronic health record (EHR) systems. Methods We evaluated the CDS implementation capabilities of 8 Office of the National Coordinator for Health Information Technology Authorized Certification Body (ONC-ACB)-certified EHRs. Within each EHR, the authors attempted to implement 3 user-defined rules that utilized the various data and logic elements expected of typical EHRs and that represented clinically important evidenced-based care. The rules were: 1) if a patient has amiodarone on his or her active medication list and does not have a thyroid-stimulating hormone (TSH) result recorded in the last 12 months, suggest ordering a TSH; 2) if a patient has a hemoglobin A1c result >7% and does not have diabetes on his or her problem list, suggest adding diabetes to the problem list; and 3) if a patient has coronary artery disease on his or her problem list and does not have aspirin on the active medication list, suggest ordering aspirin. Results Most evaluated EHRs lacked some CDS capabilities; 5 EHRs were able to implement all 3 rules, and the remaining 3 EHRs were unable to implement any of the rules. One of these did not allow users to customize CDS rules at all. The most frequently found shortcomings included the inability to use laboratory test results in rules, limit rules by time, use advanced Boolean logic, perform actions from the alert interface, and adequately test rules. Conclusion Significant improvements in the EHR certification and implementation procedures are necessary. PMID:26104739

A Semantic Approach with Decision Support for Safety Service in Smart Home Management

PubMed Central

Huang, Xiaoci; Yi, Jianjun; Zhu, Xiaomin; Chen, Shaoli

2016-01-01

Research on smart homes (SHs) has increased significantly in recent years because of the convenience provided by having an assisted living environment. The functions of SHs as mentioned in previous studies, particularly safety services, are seldom discussed or mentioned. Thus, this study proposes a semantic approach with decision support for safety service in SH management. The focus of this contribution is to explore a context awareness and reasoning approach for risk recognition in SH that enables the proper decision support for flexible safety service provision. The framework of SH based on a wireless sensor network is described from the perspective of neighbourhood management. This approach is based on the integration of semantic knowledge in which a reasoner can make decisions about risk recognition and safety service. We present a management ontology for a SH and relevant monitoring contextual information, which considers its suitability in a pervasive computing environment and is service-oriented. We also propose a rule-based reasoning method to provide decision support through reasoning techniques and context-awareness. A system prototype is developed to evaluate the feasibility, time response and extendibility of the approach. The evaluation of our approach shows that it is more effective in daily risk event recognition. The decisions for service provision are shown to be accurate. PMID:27527170

A Semantic Approach with Decision Support for Safety Service in Smart Home Management.

PubMed

Huang, Xiaoci; Yi, Jianjun; Zhu, Xiaomin; Chen, Shaoli

2016-08-03

Research on smart homes (SHs) has increased significantly in recent years because of the convenience provided by having an assisted living environment. The functions of SHs as mentioned in previous studies, particularly safety services, are seldom discussed or mentioned. Thus, this study proposes a semantic approach with decision support for safety service in SH management. The focus of this contribution is to explore a context awareness and reasoning approach for risk recognition in SH that enables the proper decision support for flexible safety service provision. The framework of SH based on a wireless sensor network is described from the perspective of neighbourhood management. This approach is based on the integration of semantic knowledge in which a reasoner can make decisions about risk recognition and safety service. We present a management ontology for a SH and relevant monitoring contextual information, which considers its suitability in a pervasive computing environment and is service-oriented. We also propose a rule-based reasoning method to provide decision support through reasoning techniques and context-awareness. A system prototype is developed to evaluate the feasibility, time response and extendibility of the approach. The evaluation of our approach shows that it is more effective in daily risk event recognition. The decisions for service provision are shown to be accurate.

48 CFR 6103.306 - Decisions [Rule 306].

Code of Federal Regulations, 2010 CFR

2010-10-01

... 48 Federal Acquisition Regulations System 7 2010-10-01 2010-10-01 false Decisions [Rule 306]. 6103.306 Section 6103.306 Federal Acquisition Regulations System CIVILIAN BOARD OF CONTRACT APPEALS, GENERAL SERVICES ADMINISTRATION TRANSPORTATION RATE CASES 6103.306 Decisions [Rule 306]. The judge will issue a written decision based upon the record,...

Modelling elderly cardiac patients decision making using Cognitive Work Analysis: identifying requirements for patient decision aids.

PubMed

Dhukaram, Anandhi Vivekanandan; Baber, Chris

2015-06-01

Patients make various healthcare decisions on a daily basis. Such day-to-day decision making can have significant consequences on their own health, treatment, care, and costs. While decision aids (DAs) provide effective support in enhancing patient's decision making, to date there have been few studies examining patient's decision making process or exploring how the understanding of such decision processes can aid in extracting requirements for the design of DAs. This paper applies Cognitive Work Analysis (CWA) to analyse patient's decision making in order to inform requirements for supporting self-care decision making. This study uses focus groups to elicit information from elderly cardiovascular disease (CVD) patients concerning a range of decision situations they face on a daily basis. Specifically, the focus groups addressed issues related to the decision making of CVD in terms of medication compliance, pain, diet and exercise. The results of these focus groups are used to develop high level views using CWA. CWA framework decomposes the complex decision making problem to inform three approaches to DA design: one design based on high level requirements; one based on a normative model of decision-making for patients; and the third based on a range of heuristics that patients seem to use. CWA helps in extracting and synthesising decision making from different perspectives: decision processes, work organisation, patient competencies and strategies used in decision making. As decision making can be influenced by human behaviour like skills, rules and knowledge, it is argued that patients require support to different types of decision making. This paper also provides insights for designers in using CWA framework for the design of effective DAs to support patients in self-management. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Clinical Decision Support for a Multicenter Trial of Pediatric Head Trauma

PubMed Central

Swietlik, Marguerite; Deakyne, Sara; Hoffman, Jeffrey M.; Grundmeier, Robert W.; Paterno, Marilyn D.; Rocha, Beatriz H.; Schaeffer, Molly H; Pabbathi, Deepika; Alessandrini, Evaline; Ballard, Dustin; Goldberg, Howard S.; Kuppermann, Nathan; Dayan, Peter S.

2016-01-01

Summary Introduction For children who present to emergency departments (EDs) due to blunt head trauma, ED clinicians must decide who requires computed tomography (CT) scanning to evaluate for traumatic brain injury (TBI). The Pediatric Emergency Care Applied Research Network (PECARN) derived and validated two age-based prediction rules to identify children at very low risk of clinically-important traumatic brain injuries (ciTBIs) who do not typically require CT scans. In this case report, we describe the strategy used to implement the PECARN TBI prediction rules via electronic health record (EHR) clinical decision support (CDS) as the intervention in a multicenter clinical trial. Methods Thirteen EDs participated in this trial. The 10 sites receiving the CDS intervention used the Epic® EHR. All sites implementing EHR-based CDS built the rules by using the vendor’s CDS engine. Based on a sociotechnical analysis, we designed the CDS so that recommendations could be displayed immediately after any provider entered prediction rule data. One central site developed and tested the intervention package to be exported to other sites. The intervention package included a clinical trial alert, an electronic data collection form, the CDS rules and the format for recommendations. Results The original PECARN head trauma prediction rules were derived from physician documentation while this pragmatic trial led each site to customize their workflows and allow multiple different providers to complete the head trauma assessments. These differences in workflows led to varying completion rates across sites as well as differences in the types of providers completing the electronic data form. Site variation in internal change management processes made it challenging to maintain the same rigor across all sites. This led to downstream effects when data reports were developed. Conclusions The process of a centralized build and export of a CDS system in one commercial EHR system successfully supported a multicenter clinical trial. PMID:27437059

Fuzzy support vector machine: an efficient rule-based classification technique for microarrays.

PubMed

Hajiloo, Mohsen; Rabiee, Hamid R; Anooshahpour, Mahdi

2013-01-01

The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification. Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection methods develops a robust model with higher accuracy than the conventional microarray classification models such as support vector machine, artificial neural network, decision trees, k nearest neighbors, and diagonal linear discriminant analysis. Furthermore, the interpretable rule-base inferred from fuzzy support vector machine helps extracting biological knowledge from microarray data. Fuzzy support vector machine as a new classification model with high generalization power, robustness, and good interpretability seems to be a promising tool for gene expression microarray classification.

Heuristics in Managing Complex Clinical Decision Tasks in Experts’ Decision Making

PubMed Central

Islam, Roosan; Weir, Charlene; Del Fiol, Guilherme

2016-01-01

Background Clinical decision support is a tool to help experts make optimal and efficient decisions. However, little is known about the high level of abstractions in the thinking process for the experts. Objective The objective of the study is to understand how clinicians manage complexity while dealing with complex clinical decision tasks. Method After approval from the Institutional Review Board (IRB), three clinical experts were interviewed the transcripts from these interviews were analyzed. Results We found five broad categories of strategies by experts for managing complex clinical decision tasks: decision conflict, mental projection, decision trade-offs, managing uncertainty and generating rule of thumb. Conclusion Complexity is created by decision conflicts, mental projection, limited options and treatment uncertainty. Experts cope with complexity in a variety of ways, including using efficient and fast decision strategies to simplify complex decision tasks, mentally simulating outcomes and focusing on only the most relevant information. Application Understanding complex decision making processes can help design allocation based on the complexity of task for clinical decision support design. PMID:27275019

Heuristics in Managing Complex Clinical Decision Tasks in Experts' Decision Making.

PubMed

Islam, Roosan; Weir, Charlene; Del Fiol, Guilherme

2014-09-01

Clinical decision support is a tool to help experts make optimal and efficient decisions. However, little is known about the high level of abstractions in the thinking process for the experts. The objective of the study is to understand how clinicians manage complexity while dealing with complex clinical decision tasks. After approval from the Institutional Review Board (IRB), three clinical experts were interviewed the transcripts from these interviews were analyzed. We found five broad categories of strategies by experts for managing complex clinical decision tasks: decision conflict, mental projection, decision trade-offs, managing uncertainty and generating rule of thumb. Complexity is created by decision conflicts, mental projection, limited options and treatment uncertainty. Experts cope with complexity in a variety of ways, including using efficient and fast decision strategies to simplify complex decision tasks, mentally simulating outcomes and focusing on only the most relevant information. Understanding complex decision making processes can help design allocation based on the complexity of task for clinical decision support design.

Integrating HL7 RIM and ontology for unified knowledge and data representation in clinical decision support systems.

PubMed

Zhang, Yi-Fan; Tian, Yu; Zhou, Tian-Shu; Araki, Kenji; Li, Jing-Song

2016-01-01

The broad adoption of clinical decision support systems within clinical practice has been hampered mainly by the difficulty in expressing domain knowledge and patient data in a unified formalism. This paper presents a semantic-based approach to the unified representation of healthcare domain knowledge and patient data for practical clinical decision making applications. A four-phase knowledge engineering cycle is implemented to develop a semantic healthcare knowledge base based on an HL7 reference information model, including an ontology to model domain knowledge and patient data and an expression repository to encode clinical decision making rules and queries. A semantic clinical decision support system is designed to provide patient-specific healthcare recommendations based on the knowledge base and patient data. The proposed solution is evaluated in the case study of type 2 diabetes mellitus inpatient management. The knowledge base is successfully instantiated with relevant domain knowledge and testing patient data. Ontology-level evaluation confirms model validity. Application-level evaluation of diagnostic accuracy reaches a sensitivity of 97.5%, a specificity of 100%, and a precision of 98%; an acceptance rate of 97.3% is given by domain experts for the recommended care plan orders. The proposed solution has been successfully validated in the case study as providing clinical decision support at a high accuracy and acceptance rate. The evaluation results demonstrate the technical feasibility and application prospect of our approach. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Narrative dynamics in social groups: A discrete choice model

NASA Astrophysics Data System (ADS)

Antoci, A.; Bellanca, N.; Galdi, G.; Sodini, M.

2018-05-01

Individuals follow different rules for action: they react swiftly, grasping the short-term advantages in sight, or they waste cognitive resources to complete otherwise easy tasks, but they are able to plan ahead future complex decisions. Scholars from different disciplines studied the conditions under which either decision rule may enhance the fitness of its adopters, with a focus on the environmental features. However, we here propose that a crucial feature of the evolution of populations and their decision rules is rather inter-group interactions. Indeed, we study what happens when two groups support different decision rules, encapsulated in narratives, and their populations interact with each other. In particular, we assume that the payoff of each rule depends on the share of both social groups which adopt such rules. We then describe the most salient dynamics scenarios and identify the conditions which lead to chaotic dynamics and multistability regimes.

A model-driven privacy compliance decision support for medical data sharing in Europe.

PubMed

Boussi Rahmouni, H; Solomonides, T; Casassa Mont, M; Shiu, S; Rahmouni, M

2011-01-01

Clinical practitioners and medical researchers often have to share health data with other colleagues across Europe. Privacy compliance in this context is very important but challenging. Automated privacy guidelines are a practical way of increasing users' awareness of privacy obligations and help eliminating unintentional breaches of privacy. In this paper we present an ontology-plus-rules based approach to privacy decision support for the sharing of patient data across European platforms. We use ontologies to model the required domain and context information about data sharing and privacy requirements. In addition, we use a set of Semantic Web Rule Language rules to reason about legal privacy requirements that are applicable to a specific context of data disclosure. We make the complete set invocable through the use of a semantic web application acting as an interactive privacy guideline system can then invoke the full model in order to provide decision support. When asked, the system will generate privacy reports applicable to a specific case of data disclosure described by the user. Also reports showing guidelines per Member State may be obtained. The advantage of this approach lies in the expressiveness and extensibility of the modelling and inference languages adopted and the ability they confer to reason with complex requirements interpreted from high level regulations. However, the system cannot at this stage fully simulate the role of an ethics committee or review board.

Ventral striatum and the evaluation of memory retrieval strategies.

PubMed

Badre, David; Lebrecht, Sophie; Pagliaccio, David; Long, Nicole M; Scimeca, Jason M

2014-09-01

Adaptive memory retrieval requires mechanisms of cognitive control that facilitate the recovery of goal-relevant information. Frontoparietal systems are known to support control of memory retrieval. However, the mechanisms by which the brain acquires, evaluates, and adapts retrieval strategies remain unknown. Here, we provide evidence that ventral striatal activation tracks the success of a retrieval strategy and correlates with subsequent reliance on that strategy. Human participants were scanned with fMRI while performing a lexical decision task. A rule was provided that indicated the likely semantic category of a target word given the category of a preceding prime. Reliance on the rule improved decision-making, as estimated within a drift diffusion framework. Ventral striatal activation tracked the benefit that relying on the rule had on decision-making. Moreover, activation in ventral striatum correlated with a participant's subsequent reliance on the rule. Taken together, these results support a role for ventral striatum in learning and evaluating declarative retrieval strategies.

RANWAR: rank-based weighted association rule mining from gene expression and methylation data.

PubMed

Mallik, Saurav; Mukhopadhyay, Anirban; Maulik, Ujjwal

2015-01-01

Ranking of association rules is currently an interesting topic in data mining and bioinformatics. The huge number of evolved rules of items (or, genes) by association rule mining (ARM) algorithms makes confusion to the decision maker. In this article, we propose a weighted rule-mining technique (say, RANWAR or rank-based weighted association rule-mining) to rank the rules using two novel rule-interestingness measures, viz., rank-based weighted condensed support (wcs) and weighted condensed confidence (wcc) measures to bypass the problem. These measures are basically depended on the rank of items (genes). Using the rank, we assign weight to each item. RANWAR generates much less number of frequent itemsets than the state-of-the-art association rule mining algorithms. Thus, it saves time of execution of the algorithm. We run RANWAR on gene expression and methylation datasets. The genes of the top rules are biologically validated by Gene Ontologies (GOs) and KEGG pathway analyses. Many top ranked rules extracted from RANWAR that hold poor ranks in traditional Apriori, are highly biologically significant to the related diseases. Finally, the top rules evolved from RANWAR, that are not in Apriori, are reported.

Connecting clinical and actuarial prediction with rule-based methods.

PubMed

Fokkema, Marjolein; Smits, Niels; Kelderman, Henk; Penninx, Brenda W J H

2015-06-01

Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction methods for clinical practice. We argue that rule-based methods may be more useful than the linear main effect models usually employed in prediction studies, from a data and decision analytic as well as a practical perspective. In addition, decision rules derived with rule-based methods can be represented as fast and frugal trees, which, unlike main effects models, can be used in a sequential fashion, reducing the number of cues that have to be evaluated before making a prediction. We illustrate the usability of rule-based methods by applying RuleFit, an algorithm for deriving decision rules for classification and regression problems, to a dataset on prediction of the course of depressive and anxiety disorders from Penninx et al. (2011). The RuleFit algorithm provided a model consisting of 2 simple decision rules, requiring evaluation of only 2 to 4 cues. Predictive accuracy of the 2-rule model was very similar to that of a logistic regression model incorporating 20 predictor variables, originally applied to the dataset. In addition, the 2-rule model required, on average, evaluation of only 3 cues. Therefore, the RuleFit algorithm appears to be a promising method for creating decision tools that are less time consuming and easier to apply in psychological practice, and with accuracy comparable to traditional actuarial methods. (c) 2015 APA, all rights reserved).

Information systems: the key to evidence-based health practice.

PubMed Central

Rodrigues, R. J.

2000-01-01

Increasing prominence is being given to the use of best current evidence in clinical practice and health services and programme management decision-making. The role of information in evidence-based practice (EBP) is discussed, together with questions of how advanced information systems and technology (IS&T) can contribute to the establishment of a broader perspective for EBP. The author examines the development, validation and use of a variety of sources of evidence and knowledge that go beyond the well-established paradigm of research, clinical trials, and systematic literature review. Opportunities and challenges in the implementation and use of IS&T and knowledge management tools are examined for six application areas: reference databases, contextual data, clinical data repositories, administrative data repositories, decision support software, and Internet-based interactive health information and communication. Computerized and telecommunications applications that support EBP follow a hierarchy in which systems, tasks and complexity range from reference retrieval and the processing of relatively routine transactions, to complex "data mining" and rule-driven decision support systems. PMID:11143195

Health-Mining: a Disease Management Support Service based on Data Mining and Rule Extraction.

PubMed

Bei, Andrea; De Luca, Stefano; Ruscitti, Giancarlo; Salamon, Diego

2005-01-01

The disease management is the collection of the processes aimed to control the health care and improving the quality at same time reducing the overall cost of the procedures. Our system, Health-Mining, is a Decision Support System with the objective of controlling the adequacy of hospitalization and therapies, determining the effective use of standard guidelines and eventually identifying better ones emerged from the medical practice (Evidence Based Medicine). In realizing the system, we have the aim of creation of a path to admissions- appropriateness criteria construction, valid at an international level. A main goal of the project is rule extraction and the identification of the rules adequate in term of efficacy, quality and cost reduction, especially in the view of fast changing technologies and medicines. We tested Health-Mining in a real test case for an Italian Region, Regione Veneto, on the installation of pacemaker and ICD.

A decision support system and rule-based algorithm to augment the human interpretation of the 12-lead electrocardiogram.

PubMed

Cairns, Andrew W; Bond, Raymond R; Finlay, Dewar D; Guldenring, Daniel; Badilini, Fabio; Libretti, Guido; Peace, Aaron J; Leslie, Stephen J

The 12-lead Electrocardiogram (ECG) has been used to detect cardiac abnormalities in the same format for more than 70years. However, due to the complex nature of 12-lead ECG interpretation, there is a significant cognitive workload required from the interpreter. This complexity in ECG interpretation often leads to errors in diagnosis and subsequent treatment. We have previously reported on the development of an ECG interpretation support system designed to augment the human interpretation process. This computerised decision support system has been named 'Interactive Progressive based Interpretation' (IPI). In this study, a decision support algorithm was built into the IPI system to suggest potential diagnoses based on the interpreter's annotations of the 12-lead ECG. We hypothesise semi-automatic interpretation using a digital assistant can be an optimal man-machine model for ECG interpretation. To improve interpretation accuracy and reduce missed co-abnormalities. The Differential Diagnoses Algorithm (DDA) was developed using web technologies where diagnostic ECG criteria are defined in an open storage format, Javascript Object Notation (JSON), which is queried using a rule-based reasoning algorithm to suggest diagnoses. To test our hypothesis, a counterbalanced trial was designed where subjects interpreted ECGs using the conventional approach and using the IPI+DDA approach. A total of 375 interpretations were collected. The IPI+DDA approach was shown to improve diagnostic accuracy by 8.7% (although not statistically significant, p-value=0.1852), the IPI+DDA suggested the correct interpretation more often than the human interpreter in 7/10 cases (varying statistical significance). Human interpretation accuracy increased to 70% when seven suggestions were generated. Although results were not found to be statistically significant, we found; 1) our decision support tool increased the number of correct interpretations, 2) the DDA algorithm suggested the correct interpretation more often than humans, and 3) as many as 7 computerised diagnostic suggestions augmented human decision making in ECG interpretation. Statistical significance may be achieved by expanding sample size. Copyright © 2017 Elsevier Inc. All rights reserved.

A Wireless Sensor Network-Based Approach with Decision Support for Monitoring Lake Water Quality.

PubMed

Huang, Xiaoci; Yi, Jianjun; Chen, Shaoli; Zhu, Xiaomin

2015-11-19

Online monitoring and water quality analysis of lakes are urgently needed. A feasible and effective approach is to use a Wireless Sensor Network (WSN). Lake water environments, like other real world environments, present many changing and unpredictable situations. To ensure flexibility in such an environment, the WSN node has to be prepared to deal with varying situations. This paper presents a WSN self-configuration approach for lake water quality monitoring. The approach is based on the integration of a semantic framework, where a reasoner can make decisions on the configuration of WSN services. We present a WSN ontology and the relevant water quality monitoring context information, which considers its suitability in a pervasive computing environment. We also propose a rule-based reasoning engine that is used to conduct decision support through reasoning techniques and context-awareness. To evaluate the approach, we conduct usability experiments and performance benchmarks.

Lessons learned from implementing service-oriented clinical decision support at four sites: A qualitative study.

PubMed

Wright, Adam; Sittig, Dean F; Ash, Joan S; Erickson, Jessica L; Hickman, Trang T; Paterno, Marilyn; Gebhardt, Eric; McMullen, Carmit; Tsurikova, Ruslana; Dixon, Brian E; Fraser, Greg; Simonaitis, Linas; Sonnenberg, Frank A; Middleton, Blackford

2015-11-01

To identify challenges, lessons learned and best practices for service-oriented clinical decision support, based on the results of the Clinical Decision Support Consortium, a multi-site study which developed, implemented and evaluated clinical decision support services in a diverse range of electronic health records. Ethnographic investigation using the rapid assessment process, a procedure for agile qualitative data collection and analysis, including clinical observation, system demonstrations and analysis and 91 interviews. We identified challenges and lessons learned in eight dimensions: (1) hardware and software computing infrastructure, (2) clinical content, (3) human-computer interface, (4) people, (5) workflow and communication, (6) internal organizational policies, procedures, environment and culture, (7) external rules, regulations, and pressures and (8) system measurement and monitoring. Key challenges included performance issues (particularly related to data retrieval), differences in terminologies used across sites, workflow variability and the need for a legal framework. Based on the challenges and lessons learned, we identified eight best practices for developers and implementers of service-oriented clinical decision support: (1) optimize performance, or make asynchronous calls, (2) be liberal in what you accept (particularly for terminology), (3) foster clinical transparency, (4) develop a legal framework, (5) support a flexible front-end, (6) dedicate human resources, (7) support peer-to-peer communication, (8) improve standards. The Clinical Decision Support Consortium successfully developed a clinical decision support service and implemented it in four different electronic health records and four diverse clinical sites; however, the process was arduous. The lessons identified by the Consortium may be useful for other developers and implementers of clinical decision support services. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Expert System Shells for Rapid Clinical Decision Support Module Development: An ESTA Demonstration of a Simple Rule-Based System for the Diagnosis of Vaginal Discharge

PubMed Central

2012-01-01

Objectives This study demonstrates the feasibility of using expert system shells for rapid clinical decision support module development. Methods A readily available expert system shell was used to build a simple rule-based system for the crude diagnosis of vaginal discharge. Pictures and 'canned text explanations' are extensively used throughout the program to enhance its intuitiveness and educational dimension. All the steps involved in developing the system are documented. Results The system runs under Microsoft Windows and is available as a free download at http://healthcybermap.org/vagdisch.zip (the distribution archive includes both the program's executable and the commented knowledge base source as a text document). The limitations of the demonstration system, such as the lack of provisions for assessing uncertainty or various degrees of severity of a sign or symptom, are discussed in detail. Ways of improving the system, such as porting it to the Web and packaging it as an app for smartphones and tablets, are also presented. Conclusions An easy-to-use expert system shell enables clinicians to rapidly become their own 'knowledge engineers' and develop concise evidence-based decision support modules of simple to moderate complexity, targeting clinical practitioners, medical and nursing students, as well as patients, their lay carers and the general public (where appropriate). In the spirit of the social Web, it is hoped that an online repository can be created to peer review, share and re-use knowledge base modules covering various clinical problems and algorithms, as a service to the clinical community. PMID:23346475

Bayesian design of decision rules for failure detection

NASA Technical Reports Server (NTRS)

Chow, E. Y.; Willsky, A. S.

1984-01-01

The formulation of the decision making process of a failure detection algorithm as a Bayes sequential decision problem provides a simple conceptualization of the decision rule design problem. As the optimal Bayes rule is not computable, a methodology that is based on the Bayesian approach and aimed at a reduced computational requirement is developed for designing suboptimal rules. A numerical algorithm is constructed to facilitate the design and performance evaluation of these suboptimal rules. The result of applying this design methodology to an example shows that this approach is potentially a useful one.

29 CFR 18.103 - Rulings on evidence.

Code of Federal Regulations, 2010 CFR

2010-07-01

... is more probably true than not true that the error did not materially contribute to the decision or... if explicitly not relied upon by the judge in support of the decision or order. (b) Record of offer... making of an offer in question and answer form. (c) Plain error. Nothing in this rule precludes taking...

Decision problems in management of construction projects

NASA Astrophysics Data System (ADS)

Szafranko, E.

2017-10-01

In a construction business, one must oftentimes make decisions during all stages of a building process, from planning a new construction project through its execution to the stage of using a ready structure. As a rule, the decision making process is made more complicated due to certain conditions specific for civil engineering. With such diverse decision situations, it is recommended to apply various decision making support methods. Both, literature and hands-on experience suggest several methods based on analytical and computational procedures, some less and some more complex. This article presents the methods which can be helpful in supporting decision making processes in the management of civil engineering projects. These are multi-criteria methods, such as MCE, AHP or indicator methods. Because the methods have different advantages and disadvantages, whereas decision situations have their own specific nature, a brief summary of the methods alongside some recommendations regarding their practical applications has been given at the end of the paper. The main aim of this article is to review the methods of decision support and their analysis for possible use in the construction industry.

Belief Function Based Decision Fusion for Decentralized Target Classification in Wireless Sensor Networks

PubMed Central

Zhang, Wenyu; Zhang, Zhenjiang

2015-01-01

Decision fusion in sensor networks enables sensors to improve classification accuracy while reducing the energy consumption and bandwidth demand for data transmission. In this paper, we focus on the decentralized multi-class classification fusion problem in wireless sensor networks (WSNs) and a new simple but effective decision fusion rule based on belief function theory is proposed. Unlike existing belief function based decision fusion schemes, the proposed approach is compatible with any type of classifier because the basic belief assignments (BBAs) of each sensor are constructed on the basis of the classifier’s training output confusion matrix and real-time observations. We also derive explicit global BBA in the fusion center under Dempster’s combinational rule, making the decision making operation in the fusion center greatly simplified. Also, sending the whole BBA structure to the fusion center is avoided. Experimental results demonstrate that the proposed fusion rule has better performance in fusion accuracy compared with the naïve Bayes rule and weighted majority voting rule. PMID:26295399

Interpretable Decision Sets: A Joint Framework for Description and Prediction

PubMed Central

Lakkaraju, Himabindu; Bach, Stephen H.; Jure, Leskovec

2016-01-01

One of the most important obstacles to deploying predictive models is the fact that humans do not understand and trust them. Knowing which variables are important in a model’s prediction and how they are combined can be very powerful in helping people understand and trust automatic decision making systems. Here we propose interpretable decision sets, a framework for building predictive models that are highly accurate, yet also highly interpretable. Decision sets are sets of independent if-then rules. Because each rule can be applied independently, decision sets are simple, concise, and easily interpretable. We formalize decision set learning through an objective function that simultaneously optimizes accuracy and interpretability of the rules. In particular, our approach learns short, accurate, and non-overlapping rules that cover the whole feature space and pay attention to small but important classes. Moreover, we prove that our objective is a non-monotone submodular function, which we efficiently optimize to find a near-optimal set of rules. Experiments show that interpretable decision sets are as accurate at classification as state-of-the-art machine learning techniques. They are also three times smaller on average than rule-based models learned by other methods. Finally, results of a user study show that people are able to answer multiple-choice questions about the decision boundaries of interpretable decision sets and write descriptions of classes based on them faster and more accurately than with other rule-based models that were designed for interpretability. Overall, our framework provides a new approach to interpretable machine learning that balances accuracy, interpretability, and computational efficiency. PMID:27853627

Health data and data governance.

PubMed

Hovenga, Evelyn J S; Grain, Heather

2013-01-01

Health is a knowledge industry, based on data collected to support care, service planning, financing and knowledge advancement. Increasingly there is a need to collect, retrieve and use health record information in an electronic format to provide greater flexibility, as this enables retrieval and display of data in multiple locations and formats irrespective of where the data were collected. Electronically maintained records require greater structure and consistency to achieve this. The use of data held in records generated in real time in clinical systems also has the potential to reduce the time it takes to gain knowledge, as there is less need to collect research specific information, this is only possible if data governance principles are applied. Connected devices and information systems are now generating huge amounts of data, as never before seen. An ability to analyse and mine very large amounts of data, "Big Data", provides policy and decision makers with new insights into varied aspects of work and information flow and operational business patterns and trends, and drives greater efficiencies, and safer and more effective health care. This enables decision makers to apply rules and guidance that have been developed based upon knowledge from many individual patient records through recognition of triggers based upon that knowledge. In clinical decision support systems information about the individual is compared to rules based upon knowledge gained from accumulated information of many to provide guidance at appropriate times in the clinical process. To achieve this the data in the individual system, and the knowledge rules must be represented in a compatible and consistent manner. This chapter describes data attributes; explains the difference between data and information; outlines the requirements for quality data; shows the relevance of health data standards; and describes how data governance impacts representation of content in systems and the use of that information.

Rule Extracting based on MCG with its Application in Helicopter Power Train Fault Diagnosis

NASA Astrophysics Data System (ADS)

Wang, M.; Hu, N. Q.; Qin, G. J.

2011-07-01

In order to extract decision rules for fault diagnosis from incomplete historical test records for knowledge-based damage assessment of helicopter power train structure. A method that can directly extract the optimal generalized decision rules from incomplete information based on GrC was proposed. Based on semantic analysis of unknown attribute value, the granule was extended to handle incomplete information. Maximum characteristic granule (MCG) was defined based on characteristic relation, and MCG was used to construct the resolution function matrix. The optimal general decision rule was introduced, with the basic equivalent forms of propositional logic, the rules were extracted and reduction from incomplete information table. Combined with a fault diagnosis example of power train, the application approach of the method was present, and the validity of this method in knowledge acquisition was proved.

Enhancing the role of science in the decision-making of the European Union.

PubMed

Allio, Lorenzo; Ballantine, Bruce; Meads, Richard

2006-02-01

Used well, science provides effective ways of identifying potential risks, protecting citizens, and using resources wisely. It enables government decisions to be based on evidence and provides a foundation for a rule-based framework that supports global trade. To ensure that the best available science becomes a key input in the decisions made by EU institutions, this abridged version of a working paper produced for the European Policy Centre, a leading, independent think tank, considers how science is currently used in the policy and decision-making processes of the EU, what the limitations of scientific evidence are, and how a risk assessment process based on scientific 'good practices' can be advantageous. Finally, the paper makes recommendations on how to improve the use of science by EU institutions.

Taking a gamble or playing by the rules: Dissociable prefrontal systems implicated in probabilistic versus deterministic rule-based decisions

PubMed Central

Bhanji, Jamil P.; Beer, Jennifer S.; Bunge, Silvia A.

2014-01-01

A decision may be difficult because complex information processing is required to evaluate choices according to deterministic decision rules and/or because it is not certain which choice will lead to the best outcome in a probabilistic context. Factors that tax decision making such as decision rule complexity and low decision certainty should be disambiguated for a more complete understanding of the decision making process. Previous studies have examined the brain regions that are modulated by decision rule complexity or by decision certainty but have not examined these factors together in the context of a single task or study. In the present functional magnetic resonance imaging study, both decision rule complexity and decision certainty were varied in comparable decision tasks. Further, the level of certainty about which choice to make (choice certainty) was varied separately from certainty about the final outcome resulting from a choice (outcome certainty). Lateral prefrontal cortex, dorsal anterior cingulate cortex, and bilateral anterior insula were modulated by decision rule complexity. Anterior insula was engaged more strongly by low than high choice certainty decisions, whereas ventromedial prefrontal cortex showed the opposite pattern. These regions showed no effect of the independent manipulation of outcome certainty. The results disambiguate the influence of decision rule complexity, choice certainty, and outcome certainty on activity in diverse brain regions that have been implicated in decision making. Lateral prefrontal cortex plays a key role in implementing deterministic decision rules, ventromedial prefrontal cortex in probabilistic rules, and anterior insula in both. PMID:19781652

Automatic rule generation for high-level vision

NASA Technical Reports Server (NTRS)

Rhee, Frank Chung-Hoon; Krishnapuram, Raghu

1992-01-01

A new fuzzy set based technique that was developed for decision making is discussed. It is a method to generate fuzzy decision rules automatically for image analysis. This paper proposes a method to generate rule-based approaches to solve problems such as autonomous navigation and image understanding automatically from training data. The proposed method is also capable of filtering out irrelevant features and criteria from the rules.

Use of a remote clinical decision support service for a multicenter trial to implement prediction rules for children with minor blunt head trauma.

PubMed

Goldberg, Howard S; Paterno, Marilyn D; Grundmeier, Robert W; Rocha, Beatriz H; Hoffman, Jeffrey M; Tham, Eric; Swietlik, Marguerite; Schaeffer, Molly H; Pabbathi, Deepika; Deakyne, Sara J; Kuppermann, Nathan; Dayan, Peter S

2016-03-01

To evaluate the architecture, integration requirements, and execution characteristics of a remote clinical decision support (CDS) service used in a multicenter clinical trial. The trial tested the efficacy of implementing brain injury prediction rules for children with minor blunt head trauma. We integrated the Epic(®) electronic health record (EHR) with the Enterprise Clinical Rules Service (ECRS), a web-based CDS service, at two emergency departments. Patterns of CDS review included either a delayed, near-real-time review, where the physician viewed CDS recommendations generated by the nursing assessment, or a real-time review, where the physician viewed recommendations generated by their own documentation. A backstopping, vendor-based CDS triggered with zero delay when no recommendation was available in the EHR from the web-service. We assessed the execution characteristics of the integrated system and the source of the generated recommendations viewed by physicians. The ECRS mean execution time was 0.74 ±0.72 s. Overall execution time was substantially different at the two sites, with mean total transaction times of 19.67 and 3.99 s. Of 1930 analyzed transactions from the two sites, 60% (310/521) of all physician documentation-initiated recommendations and 99% (1390/1409) of all nurse documentation-initiated recommendations originated from the remote web service. The remote CDS system was the source of recommendations in more than half of the real-time cases and virtually all the near-real-time cases. Comparisons are limited by allowable variation in user workflow and resolution of the EHR clock. With maturation and adoption of standards for CDS services, remote CDS shows promise to decrease time-to-trial for multicenter evaluations of candidate decision support interventions. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Ventral Striatum and the Evaluation of Memory Retrieval Strategies

PubMed Central

Badre, David; Lebrecht, Sophie; Pagliaccio, David; Long, Nicole M.; Scimeca, Jason M.

2015-01-01

Adaptive memory retrieval requires mechanisms of cognitive control that facilitate the recovery of goal-relevant information. Frontoparietal systems are known to support control of memory retrieval. However, the mechanisms by which the brain acquires, evaluates, and adapts retrieval strategies remain unknown. Here, we provide evidence that ventral striatal activation tracks the success of a retrieval strategy and correlates with subsequent reliance on that strategy. Human participants were scanned with fMRI while performing a lexical decision task. A rule was provided that indicated the likely semantic category of a target word given the category of a preceding prime. Reliance on the rule improved decision-making, as estimated within a drift diffusion framework. Ventral striatal activation tracked the benefit that relying on the rule had on decision-making. Moreover, activation in ventral striatum correlated with a participant’s subsequent reliance on the rule. Taken together, these results support a role for ventral striatum in learning and evaluating declarative retrieval strategies. PMID:24564466

The benefit of using additional hydrological information from earth observations and reanalysis data on water allocation decisions in irrigation districts

NASA Astrophysics Data System (ADS)

Kaune, Alexander; López, Patricia; Werner, Micha; de Fraiture, Charlotte

2017-04-01

Hydrological information on water availability and demand is vital for sound water allocation decisions in irrigation districts, particularly in times of water scarcity. However, sub-optimal water allocation decisions are often taken with incomplete hydrological information, which may lead to agricultural production loss. In this study we evaluate the benefit of additional hydrological information from earth observations and reanalysis data in supporting decisions in irrigation districts. Current water allocation decisions were emulated through heuristic operational rules for water scarce and water abundant conditions in the selected irrigation districts. The Dynamic Water Balance Model based on the Budyko framework was forced with precipitation datasets from interpolated ground measurements, remote sensing and reanalysis data, to determine the water availability for irrigation. Irrigation demands were estimated based on estimates of potential evapotranspiration and coefficient for crops grown, adjusted with the interpolated precipitation data. Decisions made using both current and additional hydrological information were evaluated through the rate at which sub-optimal decisions were made. The decisions made using an amended set of decision rules that benefit from additional information on demand in the districts were also evaluated. Results show that sub-optimal decisions can be reduced in the planning phase through improved estimates of water availability. Where there are reliable observations of water availability through gauging stations, the benefit of the improved precipitation data is found in the improved estimates of demand, equally leading to a reduction of sub-optimal decisions.

Cross-vendor evaluation of key user-defined clinical decision support capabilities: a scenario-based assessment of certified electronic health records with guidelines for future development.

PubMed

McCoy, Allison B; Wright, Adam; Sittig, Dean F

2015-09-01

Clinical decision support (CDS) is essential for delivery of high-quality, cost-effective, and safe healthcare. The authors sought to evaluate the CDS capabilities across electronic health record (EHR) systems. We evaluated the CDS implementation capabilities of 8 Office of the National Coordinator for Health Information Technology Authorized Certification Body (ONC-ACB)-certified EHRs. Within each EHR, the authors attempted to implement 3 user-defined rules that utilized the various data and logic elements expected of typical EHRs and that represented clinically important evidenced-based care. The rules were: 1) if a patient has amiodarone on his or her active medication list and does not have a thyroid-stimulating hormone (TSH) result recorded in the last 12 months, suggest ordering a TSH; 2) if a patient has a hemoglobin A1c result >7% and does not have diabetes on his or her problem list, suggest adding diabetes to the problem list; and 3) if a patient has coronary artery disease on his or her problem list and does not have aspirin on the active medication list, suggest ordering aspirin. Most evaluated EHRs lacked some CDS capabilities; 5 EHRs were able to implement all 3 rules, and the remaining 3 EHRs were unable to implement any of the rules. One of these did not allow users to customize CDS rules at all. The most frequently found shortcomings included the inability to use laboratory test results in rules, limit rules by time, use advanced Boolean logic, perform actions from the alert interface, and adequately test rules. Significant improvements in the EHR certification and implementation procedures are necessary. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Using Participatory Action Research to Develop a Working Model That Enhances Psychiatric Nurses' Professionalism: The Architecture of Stability.

PubMed

Salzmann-Erikson, Martin

2017-11-01

Ward rules in psychiatric care aim to promote safety for both patients and staff. Simultaneously, ward rules are associated with increased patient violence, leading to neither a safe work environment nor a safe caring environment. Although ward rules are routinely used, few studies have explicitly accounted for their impact. To describe the process of a team development project considering ward rule issues, and to develop a working model to empower staff in their daily in-patient psychiatric nursing practices. The design of this study is explorative and descriptive. Participatory action research methodology was applied to understand ward rules. Data consists of audio-recorded group discussions, observations and field notes, together creating a data set of 556 text pages. More than 100 specific ward rules were identified. In this process, the word rules was relinquished in favor of adopting the term principles, since rules are inconsistent with a caring ideology. A linguistic transition led to the development of a framework embracing the (1) Principle of Safety, (2) Principle of Structure and (3) Principle of Interplay. The principles were linked to normative guidelines and applied ethical theories: deontology, consequentialism and ethics of care. The work model reminded staff about the principles, empowered their professional decision-making, decreased collegial conflicts because of increased acceptance for individual decisions, and, in general, improved well-being at work. Furthermore, the work model also empowered staff to find support for their decisions based on principles that are grounded in the ethics of totality.

Cover estimations using object-based image analysis rule sets developed across multiple scales in pinyon-juniper woodlands

USDA-ARS?s Scientific Manuscript database

Numerous studies have been conducted that evaluate the utility of remote sensing for monitoring and assessing vegetation and ground cover to support land management decisions and complement ground-measurements. However, few land cover comparisons have been made using high-resolution imagery and obj...

A pilot study of distributed knowledge management and clinical decision support in the cloud.

PubMed

Dixon, Brian E; Simonaitis, Linas; Goldberg, Howard S; Paterno, Marilyn D; Schaeffer, Molly; Hongsermeier, Tonya; Wright, Adam; Middleton, Blackford

2013-09-01

Implement and perform pilot testing of web-based clinical decision support services using a novel framework for creating and managing clinical knowledge in a distributed fashion using the cloud. The pilot sought to (1) develop and test connectivity to an external clinical decision support (CDS) service, (2) assess the exchange of data to and knowledge from the external CDS service, and (3) capture lessons to guide expansion to more practice sites and users. The Clinical Decision Support Consortium created a repository of shared CDS knowledge for managing hypertension, diabetes, and coronary artery disease in a community cloud hosted by Partners HealthCare. A limited data set for primary care patients at a separate health system was securely transmitted to a CDS rules engine hosted in the cloud. Preventive care reminders triggered by the limited data set were returned for display to clinician end users for review and display. During a pilot study, we (1) monitored connectivity and system performance, (2) studied the exchange of data and decision support reminders between the two health systems, and (3) captured lessons. During the six month pilot study, there were 1339 patient encounters in which information was successfully exchanged. Preventive care reminders were displayed during 57% of patient visits, most often reminding physicians to monitor blood pressure for hypertensive patients (29%) and order eye exams for patients with diabetes (28%). Lessons learned were grouped into five themes: performance, governance, semantic interoperability, ongoing adjustments, and usability. Remote, asynchronous cloud-based decision support performed reasonably well, although issues concerning governance, semantic interoperability, and usability remain key challenges for successful adoption and use of cloud-based CDS that will require collaboration between biomedical informatics and computer science disciplines. Decision support in the cloud is feasible and may be a reasonable path toward achieving better support of clinical decision-making across the widest range of health care providers. Published by Elsevier B.V.

Assessing experience in the deliberate practice of running using a fuzzy decision-support system

PubMed Central

Roveri, Maria Isabel; Manoel, Edison de Jesus; Onodera, Andrea Naomi; Ortega, Neli R. S.; Tessutti, Vitor Daniel; Vilela, Emerson; Evêncio, Nelson

2017-01-01

The judgement of skill experience and its levels is ambiguous though it is crucial for decision-making in sport sciences studies. We developed a fuzzy decision support system to classify experience of non-elite distance runners. Two Mamdani subsystems were developed based on expert running coaches’ knowledge. In the first subsystem, the linguistic variables of training frequency and volume were combined and the output defined the quality of running practice. The second subsystem yielded the level of running experience from the combination of the first subsystem output with the number of competitions and practice time. The model results were highly consistent with the judgment of three expert running coaches (r>0.88, p<0.001) and also with five other expert running coaches (r>0.86, p<0.001). From the expert’s knowledge and the fuzzy model, running experience is beyond the so-called "10-year rule" and depends not only on practice time, but on the quality of practice (training volume and frequency) and participation in competitions. The fuzzy rule-based model was very reliable, valid, deals with the marked ambiguities inherent in the judgment of experience and has potential applications in research, sports training, and clinical settings. PMID:28817655

Evaluation of the performance of statistical tests used in making cleanup decisions at Superfund sites. Part 1: Choosing an appropriate statistical test

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

Berman, D.W.; Allen, B.C.; Van Landingham, C.B.

1998-12-31

The decision rules commonly employed to determine the need for cleanup are evaluated both to identify conditions under which they lead to erroneous conclusions and to quantify the rate that such errors occur. Their performance is also compared with that of other applicable decision rules. The authors based the evaluation of decision rules on simulations. Results are presented as power curves. These curves demonstrate that the degree of statistical control achieved is independent of the form of the null hypothesis. The loss of statistical control that occurs when a decision rule is applied to a data set that does notmore » satisfy the rule`s validity criteria is also clearly demonstrated. Some of the rules evaluated do not offer the formal statistical control that is an inherent design feature of other rules. Nevertheless, results indicate that such informal decision rules may provide superior overall control of error rates, when their application is restricted to data exhibiting particular characteristics. The results reported here are limited to decision rules applied to uncensored and lognormally distributed data. To optimize decision rules, it is necessary to evaluate their behavior when applied to data exhibiting a range of characteristics that bracket those common to field data. The performance of decision rules applied to data sets exhibiting a broader range of characteristics is reported in the second paper of this study.« less

Evaluation of Decision Rules in a Tiered Assessment of Inhalation Exposure to Nanomaterials.

PubMed

Brouwer, Derk; Boessen, Ruud; van Duuren-Stuurman, Birgit; Bard, Delphine; Moehlmann, Carsten; Bekker, Cindy; Fransman, Wouter; Klein Entink, Rinke

2016-10-01

Tiered or stepwise approaches to assess occupational exposure to nano-objects, and their agglomerates and aggregates have been proposed, which require decision rules (DRs) to move to a next tier, or terminate the assessment. In a desk study the performance of a number of DRs based on the evaluation of results from direct reading instruments was investigated by both statistical simulations and the application of the DRs to real workplace data sets. A statistical model that accounts for autocorrelation patterns in time-series, i.e. autoregressive integrated moving average (ARIMA), was used as 'gold' standard. The simulations showed that none of the proposed DRs covered the entire range of simulated scenarios with respect to the ARIMA model parameters, however, a combined DR showed a slightly better agreement. Application of the DRs to real workplace datasets (n = 117) revealed sensitivity up to 0.72, whereas the lowest observed specificity was 0.95. The selection of the most appropriate DR is very much dependent on the consequences of the decision, i.e. ruling in or ruling out of scenarios for further evaluation. Since a basic assessment may also comprise of other type of measurements and information, an evaluation logic was proposed which embeds the DRs, but furthermore supports decision making in view of a tiered-approach exposure assessment. © The Author 2016. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.

The Supreme Court Spanking Ruling: An Issue in Debate.

ERIC Educational Resources Information Center

Welsh, Ralph S.; And Others

Few issues have polarized the educational community so completely as the 1975 and 1977 decisions by the U.S. Supreme Court to allow corporal punishment in the schools. The symposium reported here was organized and conducted following the 1975 decision but prior to the 1977 one. Three papers in support and three papers against the ruling were read,…

Clinical, information and business process modeling to promote development of safe and flexible software.

PubMed

Liaw, Siaw-Teng; Deveny, Elizabeth; Morrison, Iain; Lewis, Bryn

2006-09-01

Using a factorial vignette survey and modeling methodology, we developed clinical and information models - incorporating evidence base, key concepts, relevant terms, decision-making and workflow needed to practice safely and effectively - to guide the development of an integrated rule-based knowledge module to support prescribing decisions in asthma. We identified workflows, decision-making factors, factor use, and clinician information requirements. The Unified Modeling Language (UML) and public domain software and knowledge engineering tools (e.g. Protégé) were used, with the Australian GP Data Model as the starting point for expressing information needs. A Web Services service-oriented architecture approach was adopted within which to express functional needs, and clinical processes and workflows were expressed in the Business Process Execution Language (BPEL). This formal analysis and modeling methodology to define and capture the process and logic of prescribing best practice in a reference implementation is fundamental to tackling deficiencies in prescribing decision support software.

Measuring agreement between decision support reminders: the cloud vs. the local expert.

PubMed

Dixon, Brian Edward; Simonaitis, Linas; Perkins, Susan M; Wright, Adam; Middleton, Blackford

2014-04-10

A cloud-based clinical decision support system (CDSS) was implemented to remotely provide evidence-based guideline reminders in support of preventative health. Following implementation, we measured the agreement between preventive care reminders generated by an existing, local CDSS and the new, cloud-based CDSS operating on the same patient visit data. Electronic health record data for the same set of patients seen in primary care were sent to both the cloud-based web service and local CDSS. The clinical reminders returned by both services were captured for analysis. Cohen's Kappa coefficient was calculated to compare the two sets of reminders. Kappa statistics were further adjusted for prevalence and bias due to the potential effects of bias in the CDS logic and prevalence in the relative small sample of patients. The cloud-based CDSS generated 965 clinical reminders for 405 patient visits over 3 months. The local CDSS returned 889 reminders for the same patient visit data. When adjusted for prevalence and bias, observed agreement varied by reminder from 0.33 (95% CI 0.24 - 0.42) to 0.99 (95% CI 0.97 - 1.00) and demonstrated almost perfect agreement for 7 of the 11 reminders. Preventive care reminders delivered by two disparate CDS systems show substantial agreement. Subtle differences in rule logic and terminology mapping appear to account for much of the discordance. Cloud-based CDSS therefore show promise, opening the door for future development and implementation in support of health care providers with limited resources for knowledge management of complex logic and rules.

Measuring agreement between decision support reminders: the cloud vs. the local expert

PubMed Central

2014-01-01

Background A cloud-based clinical decision support system (CDSS) was implemented to remotely provide evidence-based guideline reminders in support of preventative health. Following implementation, we measured the agreement between preventive care reminders generated by an existing, local CDSS and the new, cloud-based CDSS operating on the same patient visit data. Methods Electronic health record data for the same set of patients seen in primary care were sent to both the cloud-based web service and local CDSS. The clinical reminders returned by both services were captured for analysis. Cohen’s Kappa coefficient was calculated to compare the two sets of reminders. Kappa statistics were further adjusted for prevalence and bias due to the potential effects of bias in the CDS logic and prevalence in the relative small sample of patients. Results The cloud-based CDSS generated 965 clinical reminders for 405 patient visits over 3 months. The local CDSS returned 889 reminders for the same patient visit data. When adjusted for prevalence and bias, observed agreement varied by reminder from 0.33 (95% CI 0.24 – 0.42) to 0.99 (95% CI 0.97 – 1.00) and demonstrated almost perfect agreement for 7 of the 11 reminders. Conclusions Preventive care reminders delivered by two disparate CDS systems show substantial agreement. Subtle differences in rule logic and terminology mapping appear to account for much of the discordance. Cloud-based CDSS therefore show promise, opening the door for future development and implementation in support of health care providers with limited resources for knowledge management of complex logic and rules. PMID:24720863

Decision Support Systems for Launch and Range Operations Using Jess

NASA Technical Reports Server (NTRS)

Thirumalainambi, Rajkumar

2007-01-01

The virtual test bed for launch and range operations developed at NASA Ames Research Center consists of various independent expert systems advising on weather effects, toxic gas dispersions and human health risk assessment during space-flight operations. An individual dedicated server supports each expert system and the master system gather information from the dedicated servers to support the launch decision-making process. Since the test bed is based on the web system, reducing network traffic and optimizing the knowledge base is critical to its success of real-time or near real-time operations. Jess, a fast rule engine and powerful scripting environment developed at Sandia National Laboratory has been adopted to build the expert systems providing robustness and scalability. Jess also supports XML representation of knowledge base with forward and backward chaining inference mechanism. Facts added - to working memory during run-time operations facilitates analyses of multiple scenarios. Knowledge base can be distributed with one inference engine performing the inference process. This paper discusses details of the knowledge base and inference engine using Jess for a launch and range virtual test bed.

The neural system of metacognition accompanying decision-making in the prefrontal cortex

PubMed Central

Qiu, Lirong; Su, Jie; Ni, Yinmei; Bai, Yang; Zhang, Xuesong; Li, Xiaoli

2018-01-01

Decision-making is usually accompanied by metacognition, through which a decision maker monitors uncertainty regarding a decision and may then consequently revise the decision. These metacognitive processes can occur prior to or in the absence of feedback. However, the neural mechanisms of metacognition remain controversial. One theory proposes an independent neural system for metacognition in the prefrontal cortex (PFC); the other, that metacognitive processes coincide and overlap with the systems used for the decision-making process per se. In this study, we devised a novel “decision–redecision” paradigm to investigate the neural metacognitive processes involved in redecision as compared to the initial decision-making process. The participants underwent a perceptual decision-making task and a rule-based decision-making task during functional magnetic resonance imaging (fMRI). We found that the anterior PFC, including the dorsal anterior cingulate cortex (dACC) and lateral frontopolar cortex (lFPC), were more extensively activated after the initial decision. The dACC activity in redecision positively scaled with decision uncertainty and correlated with individual metacognitive uncertainty monitoring abilities—commonly occurring in both tasks—indicating that the dACC was specifically involved in decision uncertainty monitoring. In contrast, the lFPC activity seen in redecision processing was scaled with decision uncertainty reduction and correlated with individual accuracy changes—positively in the rule-based decision-making task and negatively in the perceptual decision-making task. Our results show that the lFPC was specifically involved in metacognitive control of decision adjustment and was subject to different control demands of the tasks. Therefore, our findings support that a separate neural system in the PFC is essentially involved in metacognition and further, that functions of the PFC in metacognition are dissociable. PMID:29684004

An Autonomous Flight Safety System

DTIC Science & Technology

2008-11-01

are taken. AFSS can take vehicle navigation data from redundant onboard sensors and make flight termination decisions using software-based rules...implemented on redundant flight processors. By basing these decisions on actual Instantaneous Impact Predictions and by providing for an arbitrary...number of mission rules, it is the contention of the AFSS development team that the decision making process used by Missile Flight Control Officers

Design and usability of heuristic-based deliberation tools for women facing amniocentesis.

PubMed

Durand, Marie-Anne; Wegwarth, Odette; Boivin, Jacky; Elwyn, Glyn

2012-03-01

Evidence suggests that in decision contexts characterized by uncertainty and time constraints (e.g. health-care decisions), fast and frugal decision-making strategies (heuristics) may perform better than complex rules of reasoning. To examine whether it is possible to design deliberation components in decision support interventions using simple models (fast and frugal heuristics). The 'Take The Best' heuristic (i.e. selection of a 'most important reason') and 'The Tallying' integration algorithm (i.e. unitary weighing of pros and cons) were used to develop two deliberation components embedded in a Web-based decision support intervention for women facing amniocentesis testing. Ten researchers (recruited from 15), nine health-care providers (recruited from 28) and ten pregnant women (recruited from 14) who had recently been offered amniocentesis testing appraised evolving versions of 'your most important reason' (Take The Best) and 'weighing it up' (Tallying). Most researchers found the tools useful in facilitating decision making although emphasized the need for simple instructions and clear layouts. Health-care providers however expressed concerns regarding the usability and clarity of the tools. By contrast, 7 out of 10 pregnant women found the tools useful in weighing up the pros and cons of each option, helpful in structuring and clarifying their thoughts and visualizing their decision efforts. Several pregnant women felt that 'weighing it up' and 'your most important reason' were not appropriate when facing such a difficult and emotional decision. Theoretical approaches based on fast and frugal heuristics can be used to develop deliberation tools that provide helpful support to patients facing real-world decisions about amniocentesis. © 2011 Blackwell Publishing Ltd.

Route choice in mountain navigation, Naismith's rule, and the equivalence of distance and climb.

PubMed

Scarf, Philip

2007-04-01

In this paper, I consider decision making about routes in mountain navigation. In particular, I discuss Naismith's rule, a method of calculating journey times in mountainous terrain, and its use for route choice. The rule is essentially concerned with the equivalence, in terms of time duration, between climb or ascent and distance travelled. Naismith himself described a rule that is purported to be based on trigonometry and simple assumptions about rate of ascent; his rule with regard to hill-walking implies that 1 m of ascent is equivalent to 7.92 m of horizontal travel (1:7.92). The analysis of data on fell running records presented here supports Naismith's rule and it is recommended that male runners and walkers use a 1:8 equivalence ratio and females a 1:10 ratio. The present findings are contrasted with those based on the analysis of data relating to treadmill running experiments (1:3.3), and with those based on the analysis of times for a mountain road-relay (1:4.4). Analysis of cycling data suggests a similar rule (1:8.2) for cycling on mountainous roads and tracks.

Cognitive Tutoring based on Intelligent Decision Support in the PENTHA Instructional Design Model

NASA Astrophysics Data System (ADS)

dall'Acqua, Luisa

2010-06-01

The research finality of this paper is how to support Authors to develop rule driven—subject oriented, adaptable course content, meta-rules—representing the disciplinary epistemology, model of teaching, Learning Path structure, and assessment parameters—for intelligent Tutoring actions in a personalized, adaptive e-Learning environment. The focus is to instruct the student to be a decision manager for himself, able to recognize the elements of a problem, select the necessary information with the perspective of factual choices. In particular, our research intends to provide some fundamental guidelines for the definition of didactical rules and logical relations, that Authors should provide to a cognitive Tutoring system through the use of an Instructional Design method (PENTHA Model) which proposes an educational environment, able to: increase productivity and operability, create conditions for a cooperative dialogue, developing participatory research activities of knowledge, observations and discoveries, customizing the learning design in a complex and holistic vision of the learning / teaching processes.

A Recommendation Algorithm for Automating Corollary Order Generation

PubMed Central

Klann, Jeffrey; Schadow, Gunther; McCoy, JM

2009-01-01

Manual development and maintenance of decision support content is time-consuming and expensive. We explore recommendation algorithms, e-commerce data-mining tools that use collective order history to suggest purchases, to assist with this. In particular, previous work shows corollary order suggestions are amenable to automated data-mining techniques. Here, an item-based collaborative filtering algorithm augmented with association rule interestingness measures mined suggestions from 866,445 orders made in an inpatient hospital in 2007, generating 584 potential corollary orders. Our expert physician panel evaluated the top 92 and agreed 75.3% were clinically meaningful. Also, at least one felt 47.9% would be directly relevant in guideline development. This automated generation of a rough-cut of corollary orders confirms prior indications about automated tools in building decision support content. It is an important step toward computerized augmentation to decision support development, which could increase development efficiency and content quality while automatically capturing local standards. PMID:20351875

A recommendation algorithm for automating corollary order generation.

PubMed

Klann, Jeffrey; Schadow, Gunther; McCoy, J M

2009-11-14

Manual development and maintenance of decision support content is time-consuming and expensive. We explore recommendation algorithms, e-commerce data-mining tools that use collective order history to suggest purchases, to assist with this. In particular, previous work shows corollary order suggestions are amenable to automated data-mining techniques. Here, an item-based collaborative filtering algorithm augmented with association rule interestingness measures mined suggestions from 866,445 orders made in an inpatient hospital in 2007, generating 584 potential corollary orders. Our expert physician panel evaluated the top 92 and agreed 75.3% were clinically meaningful. Also, at least one felt 47.9% would be directly relevant in guideline development. This automated generation of a rough-cut of corollary orders confirms prior indications about automated tools in building decision support content. It is an important step toward computerized augmentation to decision support development, which could increase development efficiency and content quality while automatically capturing local standards.

The Space Environmental Impact System

NASA Astrophysics Data System (ADS)

Kihn, E. A.

2009-12-01

The Space Environmental Impact System (SEIS) is an operational tool for incorporating environmental data sets into DoD Modeling and Simulation (M&S) which allows for enhanced decision making regarding acquisitions, testing, operations and planning. The SEIS system creates, from the environmental archives and developed rule-base, a tool for describing the effects of the space environment on particular military systems, both historically and in real-time. The system uses data available over the web, and in particular data provided by NASA’s virtual observatory network, as well as modeled data generated specifically for this purpose. The rule base system developed to support SEIS is an open XML based model which can be extended to events from any environmental domain. This presentation will show how the SEIS tool allows users to easily and accurately evaluate the effect of space weather in terms that are meaningful to them as well as discuss the relevant standards used in its construction and go over lessons learned from fielding an operational environmental decision tool.

Situation-Assessment And Decision-Aid Production-Rule Analysis System For Nuclear Plant Monitoring And Emergency Preparedness

NASA Astrophysics Data System (ADS)

Gvillo, D.; Ragheb, M.; Parker, M.; Swartz, S.

1987-05-01

A Production-Rule Analysis System is developed for Nuclear Plant Monitoring. The signals generated by the Zion-1 Plant are considered. A Situation-Assessment and Decision-Aid capability is provided for monitoring the integrity of the Plant Radiation, the Reactor Coolant, the Fuel Clad, and the Containment Systems. A total of 41 signals are currently fed as facts to an Inference Engine functioning in the backward-chaining mode and built along the same structure as the E-Mycin system. The Goal-Tree constituting the Knowledge Base was generated using a representation in the form of Fault Trees deduced from plant procedures information. The system is constructed in support of the Data Analysis and Emergency Preparedness tasks at the Illinois Radiological Emergency Assessment Center (REAC).

Building a computer program to support children, parents, and distraction during healthcare procedures.

PubMed

Hanrahan, Kirsten; McCarthy, Ann Marie; Kleiber, Charmaine; Ataman, Kaan; Street, W Nick; Zimmerman, M Bridget; Ersig, Anne L

2012-10-01

This secondary data analysis used data mining methods to develop predictive models of child risk for distress during a healthcare procedure. Data used came from a study that predicted factors associated with children's responses to an intravenous catheter insertion while parents provided distraction coaching. From the 255 items used in the primary study, 44 predictive items were identified through automatic feature selection and used to build support vector machine regression models. Models were validated using multiple cross-validation tests and by comparing variables identified as explanatory in the traditional versus support vector machine regression. Rule-based approaches were applied to the model outputs to identify overall risk for distress. A decision tree was then applied to evidence-based instructions for tailoring distraction to characteristics and preferences of the parent and child. The resulting decision support computer application, titled Children, Parents and Distraction, is being used in research. Future use will support practitioners in deciding the level and type of distraction intervention needed by a child undergoing a healthcare procedure.

[Quantitative measures for assessing the functional state of the human body during diagnostic procedure].

PubMed

Artemenko, M V

2008-01-01

Two approaches to calculation of the qualitative measures for assessing the functional state level of human body are considered. These approaches are based on image and fuzzy set recognition theories and are used to construct diagnostic decision rules. The first approach uses the data on deviation of detected parameters from those for healthy persons; the second approach analyzes the degree of deviation of detected parameters from the approximants characterizing the correlation differences between the parameters. A method for synthesis of decision rules and the results of blood count-based research for a number of diseases (hemophilia, thrombocytopathy, hypertension, arrhythmia, hepatic cirrhosis, trichophytia) are considered. An effect of a change in the functional link between the cholesterol content in blood and the relative rate of variation of AST and ALT enzymes in blood from direct proportional (healthy state) to inverse proportional (hepatic cirrhosis) is discussed. It is shown that analysis of correlation changes in detected parameters of the human body state during diagnostic process is more effective for application in decision support systems than the state space analysis.

Rule groupings in expert systems using nearest neighbour decision rules, and convex hulls

NASA Technical Reports Server (NTRS)

Anastasiadis, Stergios

1991-01-01

Expert System shells are lacking in many areas of software engineering. Large rule based systems are not semantically comprehensible, difficult to debug, and impossible to modify or validate. Partitioning a set of rules found in CLIPS (C Language Integrated Production System) into groups of rules which reflect the underlying semantic subdomains of the problem, will address adequately the concerns stated above. Techniques are introduced to structure a CLIPS rule base into groups of rules that inherently have common semantic information. The concepts involved are imported from the field of A.I., Pattern Recognition, and Statistical Inference. Techniques focus on the areas of feature selection, classification, and a criteria of how 'good' the classification technique is, based on Bayesian Decision Theory. A variety of distance metrics are discussed for measuring the 'closeness' of CLIPS rules and various Nearest Neighbor classification algorithms are described based on the above metric.

Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule.

PubMed

Beyeler, Michael; Dutt, Nikil D; Krichmar, Jeffrey L

2013-12-01

Understanding how the human brain is able to efficiently perceive and understand a visual scene is still a field of ongoing research. Although many studies have focused on the design and optimization of neural networks to solve visual recognition tasks, most of them either lack neurobiologically plausible learning rules or decision-making processes. Here we present a large-scale model of a hierarchical spiking neural network (SNN) that integrates a low-level memory encoding mechanism with a higher-level decision process to perform a visual classification task in real-time. The model consists of Izhikevich neurons and conductance-based synapses for realistic approximation of neuronal dynamics, a spike-timing-dependent plasticity (STDP) synaptic learning rule with additional synaptic dynamics for memory encoding, and an accumulator model for memory retrieval and categorization. The full network, which comprised 71,026 neurons and approximately 133 million synapses, ran in real-time on a single off-the-shelf graphics processing unit (GPU). The network was constructed on a publicly available SNN simulator that supports general-purpose neuromorphic computer chips. The network achieved 92% correct classifications on MNIST in 100 rounds of random sub-sampling, which is comparable to other SNN approaches and provides a conservative and reliable performance metric. Additionally, the model correctly predicted reaction times from psychophysical experiments. Because of the scalability of the approach and its neurobiological fidelity, the current model can be extended to an efficient neuromorphic implementation that supports more generalized object recognition and decision-making architectures found in the brain. Copyright © 2013 Elsevier Ltd. All rights reserved.

Termination of resuscitation in the prehospital setting: A comparison of decisions in clinical practice vs. recommendations of a termination rule.

PubMed

Verhaert, Dominique V M; Bonnes, Judith L; Nas, Joris; Keuper, Wessel; van Grunsven, Pierre M; Smeets, Joep L R M; de Boer, Menko Jan; Brouwer, Marc A

2016-03-01

Of the proposed algorithms that provide guidance for in-field termination of resuscitation (TOR) decisions, the guidelines for cardiopulmonary resuscitation (CPR) refer to the basic and advanced life support (ALS)-TOR rules. To assess the potential consequences of implementation of the ALS-TOR rule, we performed a case-by-case evaluation of our in-field termination decisions and assessed the corresponding recommendations of the ALS-TOR rule. Cohort of non-traumatic out-of-hospital cardiac arrest (OHCA)-patients who were resuscitated by the ALS-practising emergency medical service (EMS) in the Nijmegen area (2008-2011). The ALS-TOR rule recommends termination in case all following criteria are met: unwitnessed arrest, no bystander CPR, no shock delivery, no return of spontaneous circulation (ROSC). Of the 598 cases reviewed, resuscitative efforts were terminated in the field in 46% and 15% survived to discharge. The ALS-TOR rule would have recommended in-field termination in only 6% of patients, due to high percentages of witnessed arrests (73%) and bystander CPR (54%). In current practice, absence of ROSC was the most important determinant of termination [aOR 35.6 (95% CI 18.3-69.3)]. Weaker associations were found for: unwitnessed and non-public arrests, non-shockable initial rhythms and longer EMS-response times. While designed to optimise hospital transportations, application of the ALS-TOR rule would almost double our hospital transportation rate to over 90% of OHCA-cases due to the favourable arrest circumstances in our region. Prior to implementation of the ALS-TOR rule, local evaluation of the potential consequences for the efficiency of triage is to be recommended and initiatives to improve field-triage for ALS-based EMS-systems are eagerly awaited. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Empirical study on voting power in participatory forest planning.

PubMed

Vainikainen, N; Kangas, A; Kangas, J

2008-07-01

Multicriteria decision support systems are applied in natural resource management in order to clarify the planning process for the stakeholders, to make all available information usable and all objectives manageable. Especially when the public is involved in planning, the decision support system should be easy to comprehend, transparent and fair. Social choice theory has recently been applied to group decision-making in natural resources management to accomplish these objectives. Although voting forms the basis of democracy, and is usually taken as a fair method, the influence of voters over the outcome may vary. It is also possible to vote strategically to improve the results from each stakeholder's point of view. This study examines the use of social choice theory in revealing stakeholders' preferences in participatory forest planning, and the influence of different voters on the outcome. The positional voting rules examined were approval voting and Borda count, but both rules were slightly modified for the purposes of this study. The third rule examined, cumulative rule, resembles utilitarian voting rules. The voting rules were tested in a real participatory forest planning situation in eastern Lapland, Finland. All voting rules resulted in a different joint order of importance of the criteria. Yet, the preference orders produced had also a lot in common and the criteria could be divided into three quite distinct groups according to their importance. The influence of individual voters varied between the voting rules, and in each case different voter was the most influential.

A Decision Support System for Evaluating and Selecting Information Systems Projects

NASA Astrophysics Data System (ADS)

Deng, Hepu; Wibowo, Santoso

2009-01-01

This chapter presents a decision support system (DSS) for effectively solving the information systems (IS) project selection problem. The proposed DSS recognizes the multidimensional nature of the IS project selection problem, the availability of multicriteria analysis (MA) methods, and the preferences of the decision-maker (DM) on the use of specific MA methods in a given situation. A knowledge base consisting of IF-THEN production rules is developed for assisting the DM with a systematic adoption of the most appropriate method with the efficient use of the powerful reasoning and explanation capabilities of intelligent DSS. The idea of letting the problem to be solved determines the method to be used is incorporated into the proposed DSS. As a result, effective decisions can be made for solving the IS project selection problem. An example is presented to demonstrate the applicability of the proposed DSS for solving the problem of selecting IS projects in real world situations.

Improvement of sand filter and constructed wetland design using an environmental decision support system.

PubMed

Turon, Clàudia; Comas, Joaquim; Torrens, Antonina; Molle, Pascal; Poch, Manel

2008-01-01

With the aim of improving effluent quality of waste stabilization ponds, different designs of vertical flow constructed wetlands and intermittent sand filters were tested on an experimental full-scale plant within the framework of a European project. The information extracted from this study was completed and updated with heuristic and bibliographic knowledge. The data and knowledge acquired were difficult to integrate into mathematical models because they involve qualitative information and expert reasoning. Therefore, it was decided to develop an environmental decision support system (EDSS-Filter-Design) as a tool to integrate mathematical models and knowledge-based techniques. This paper describes the development of this support tool, emphasizing the collection of data and knowledge and representation of this information by means of mathematical equations and a rule-based system. The developed support tool provides the main design characteristics of filters: (i) required surface, (ii) media type, and (iii) media depth. These design recommendations are based on wastewater characteristics, applied load, and required treatment level data provided by the user. The results of the EDSS-Filter-Design provide appropriate and useful information and guidelines on how to design filters, according to the expert criteria. The encapsulation of the information into a decision support system reduces the design period and provides a feasible, reasoned, and positively evaluated proposal.

Development of an expert system for assessing trumpeter swan breeding habitat in the Northern Rocky Mountains.

USGS Publications Warehouse

Sojda, Richard S.; Cornely, John E.; Howe, Adele E.

2002-01-01

A decision support system for the management of the Rocky Mountain Population of Trumpeter Swans (Cygnus buccinators) is being developed. As part of this, three expert systems are also in development: one for assessing the quality of Trumpeter Swan breeding habitat; one for making water level recommendations in montane, palustrine wetlands; and one for assessing the contribution a particular site can make towards meeting objectives from as flyway perspective. The focus of this paper is the development of the breeding habitat expert system, which currently consists of 157 rules. Out purpose is to provide decision support for issues that appear to be beyond the capability of a single persons to conceptualize and solve. We propose that by involving multiple experts in the development and use of the systems, management will be significantly improved. The knowledge base for the expert system has been developed using standard knowledge engineering techniques with a small team of ecological experts. Knowledge was then coded using production rules organized in decision trees using a commercial expert system development shell. The final system has been deployed on the world wide web.

Insurance Contract Analysis for Company Decision Support in Acquisition Management

NASA Astrophysics Data System (ADS)

Chernovita, H. P.; Manongga, D.; Iriani, A.

2017-01-01

One of company activities to retain their business is marketing the products which include in acquisition management to get new customers. Insurance contract analysis using ID3 to produce decision tree and rules to be decision support for the insurance company. The decision tree shows 13 rules that lead to contract termination claim. This could be a guide for the insurance company in acquisition management to prevent contract binding with these contract condition because it has a big chance for the customer to terminate their insurance contract before its expired date. As the result, there are several strong points that could be the determinant of contract termination such as: 1) customer age whether too young or too old, 2) long insurance period (above 10 years), 3) big insurance amount, 4) big amount of premium charges, and 5) payment method.

A study of some nine-element decision rules. [for multispectral recognition of remote sensing

NASA Technical Reports Server (NTRS)

Richardson, W.

1974-01-01

A nine-element rule is one that makes a classification decision for each pixel based on data from that pixel and its eight immediate neighbors. Three such rules, all fast and simple to use, are defined and tested. All performed substantially better on field interiors than the best one-point rule. Qualitative results indicate that fine detail and contradictory testimony tend to be overlooked by the rules.

Primary motor cortex contributes to the implementation of implicit value-based rules during motor decisions.

PubMed

Derosiere, Gerard; Zénon, Alexandre; Alamia, Andrea; Duque, Julie

2017-02-01

In the present study, we investigated the functional contribution of the human primary motor cortex (M1) to motor decisions. Continuous theta burst stimulation (cTBS) was used to alter M1 activity while participants performed a decision-making task in which the reward associated with the subjects' responses (right hand finger movements) depended on explicit and implicit value-based rules. Subjects performed the task over two consecutive days and cTBS occurred in the middle of Day 2, once the subjects were just about to implement implicit rules, in addition to the explicit instructions, to choose their responses, as evident in the control group (cTBS over the right somatosensory cortex). Interestingly, cTBS over the left M1 prevented subjects from implementing the implicit value-based rule while its implementation was enhanced in the group receiving cTBS over the right M1. Hence, cTBS had opposite effects depending on whether it was applied on the contralateral or ipsilateral M1. The use of the explicit value-based rule was unaffected by cTBS in the three groups of subject. Overall, the present study provides evidence for a functional contribution of M1 to the implementation of freshly acquired implicit rules, possibly through its involvement in a cortico-subcortical network controlling value-based motor decisions. Copyright © 2016 Elsevier Inc. All rights reserved.

Presenting Germany's drug pricing rule as a cost-per-QALY rule.

PubMed

Gandjour, Afschin

2012-06-01

In Germany, the Institute for Quality and Efficiency in Health Care (IQWiG) makes recommendations for ceiling prices of drugs based on an evaluation of the relationship between costs and effectiveness. To set ceiling prices, IQWiG uses the following decision rule: the incremental cost-effectiveness ratio of a new drug compared with the next effective intervention should not be higher than that of the next effective intervention compared to its comparator. The purpose of this paper is to show that IQWiG's decision rule can be presented as a cost-per-QALY rule by using equity-weighted QALYs. This transformation shows where both rules share commonalities. Furthermore, it makes the underlying ethical implications of IQWiG's decision rule transparent and open to debate.

A Legal Negotiatiton Support System Based on A Diagram

NASA Astrophysics Data System (ADS)

Nitta, Katsumi; Shibasaki, Masato; Yasumura, Yoshiaki; Hasegawa, Ryuzo; Fujita, Hiroshi; Koshimura, Miyuki; Inoue, Katsumi; Shirai, Yasuyuki; Komatsu, Hiroshi

We present an overview of a legal negotiation support system, ANS (Argumentation based Negotiation support System). ANS consists of a user interface, three inference engines, a database of old cases, and two decision support modules. The ANS users negotiates or disputes with others via a computer network. The negotiation status is managed in the form of the negotiation diagram. The negotiation diagram is an extension of Toulmin’s argument diagram, and it contains all arguments insisted by participants. The negotiation protocols are defined as operations to the negotiation diagram. By exchanging counter arguments each other, the negotiation diagram grows up. Nonmonotonic reasoning using rule priorities are applied to the negotiation diagram.

Heuristics: foundations for a novel approach to medical decision making.

PubMed

Bodemer, Nicolai; Hanoch, Yaniv; Katsikopoulos, Konstantinos V

2015-03-01

Medical decision-making is a complex process that often takes place during uncertainty, that is, when knowledge, time, and resources are limited. How can we ensure good decisions? We present research on heuristics-simple rules of thumb-and discuss how medical decision-making can benefit from these tools. We challenge the common view that heuristics are only second-best solutions by showing that they can be more accurate, faster, and easier to apply in comparison to more complex strategies. Using the example of fast-and-frugal decision trees, we illustrate how heuristics can be studied and implemented in the medical context. Finally, we suggest how a heuristic-friendly culture supports the study and application of heuristics as complementary strategies to existing decision rules.

How power influences moral thinking.

PubMed

Lammers, Joris; Stapel, Diederik A

2009-08-01

The authors conducted 5 studies to test the idea that both thinking about and having power affects the way in which people resolve moral dilemmas. It is shown that high power increases the use of rule-based (deontological) moral thinking styles, whereas low power increases reliance on outcome-based (consequentialist) moral thinking. Stated differently, in determining whether an act is right or wrong, the powerful focus on whether rules and principles are violated, whereas the powerless focus on the consequences. For this reason, the powerful are also more inclined to stick to the rules, irrespective of whether this has positive or negative effects, whereas the powerless are more inclined to make exceptions. The first 3 experiments show that thinking about power increases rule-based thinking and decreases outcome-based thinking in participants' moral decision making. A 4th experiment shows the mediating role of moral orientation in the effect of power on moral decisions. The 5th experiment demonstrates the role of self-interest by showing that the power-moral link is reversed when rule-based decisions threaten participants' own self-interests.

Multicriteria decision model for retrofitting existing buildings

NASA Astrophysics Data System (ADS)

Bostenaru Dan, B.

2003-04-01

In this paper a model to decide which buildings from an urban area should be retrofitted is presented. The model has been cast into existing ones by choosing the decision rule, criterion weighting and decision support system types most suitable for the spatial problem of reducing earthquake risk in urban areas, considering existing spatial multiatributive and multiobjective decision methods and especially collaborative issues. Due to the participative character of the group decision problem "retrofitting existing buildings" the decision making model is based on interactivity. Buildings have been modeled following the criteria of spatial decision support systems. This includes identifying the corresponding spatial elements of buildings according to the information needs of actors from different sphaeres like architects, construction engineers and economists. The decision model aims to facilitate collaboration between this actors. The way of setting priorities interactivelly will be shown, by detailing the two phases: judgemental and computational, in this case site analysis, collection and evaluation of the unmodified data and converting survey data to information with computational methods using additional expert support. Buildings have been divided into spatial elements which are characteristic for the survey, present typical damages in case of an earthquake and are decisive for a better seismic behaviour in case of retrofitting. The paper describes the architectural and engineering characteristics as well as the structural damage for constuctions of different building ages on the example of building types in Bucharest, Romania in compressible and interdependent charts, based on field observation, reports from the 1977 earthquake and detailed studies made by the author together with a local engineer for the EERI Web Housing Encyclopedia. On this base criteria for setting priorities flow into the expert information contained in the system.

Automatic and controlled components of judgment and decision making.

PubMed

Ferreira, Mario B; Garcia-Marques, Leonel; Sherman, Steven J; Sherman, Jeffrey W

2006-11-01

The categorization of inductive reasoning into largely automatic processes (heuristic reasoning) and controlled analytical processes (rule-based reasoning) put forward by dual-process approaches of judgment under uncertainty (e.g., K. E. Stanovich & R. F. West, 2000) has been primarily a matter of assumption with a scarcity of direct empirical findings supporting it. The present authors use the process dissociation procedure (L. L. Jacoby, 1991) to provide convergent evidence validating a dual-process perspective to judgment under uncertainty based on the independent contributions of heuristic and rule-based reasoning. Process dissociations based on experimental manipulation of variables were derived from the most relevant theoretical properties typically used to contrast the two forms of reasoning. These include processing goals (Experiment 1), cognitive resources (Experiment 2), priming (Experiment 3), and formal training (Experiment 4); the results consistently support the author's perspective. They conclude that judgment under uncertainty is neither an automatic nor a controlled process but that it reflects both processes, with each making independent contributions.

Evolving optimised decision rules for intrusion detection using particle swarm paradigm

NASA Astrophysics Data System (ADS)

Sivatha Sindhu, Siva S.; Geetha, S.; Kannan, A.

2012-12-01

The aim of this article is to construct a practical intrusion detection system (IDS) that properly analyses the statistics of network traffic pattern and classify them as normal or anomalous class. The objective of this article is to prove that the choice of effective network traffic features and a proficient machine-learning paradigm enhances the detection accuracy of IDS. In this article, a rule-based approach with a family of six decision tree classifiers, namely Decision Stump, C4.5, Naive Baye's Tree, Random Forest, Random Tree and Representative Tree model to perform the detection of anomalous network pattern is introduced. In particular, the proposed swarm optimisation-based approach selects instances that compose training set and optimised decision tree operate over this trained set producing classification rules with improved coverage, classification capability and generalisation ability. Experiment with the Knowledge Discovery and Data mining (KDD) data set which have information on traffic pattern, during normal and intrusive behaviour shows that the proposed algorithm produces optimised decision rules and outperforms other machine-learning algorithm.

Using Decision Structures for Policy Analysis in Software Product-line Evolution - A Case Study

NASA Astrophysics Data System (ADS)

Sarang, Nita; Sanglikar, Mukund A.

Project management decisions are the primary basis for project success (or failure). Mostly, such decisions are based on an intuitive understanding of the underlying software engineering and management process and have a likelihood of being misjudged. Our problem domain is product-line evolution. We model the dynamics of the process by incorporating feedback loops appropriate to two decision structures: staffing policy, and the forces of growth associated with long-term software evolution. The model is executable and supports project managers to assess the long-term effects of possible actions. Our work also corroborates results from earlier studies of E-type systems, in particular the FEAST project and the rules for software evolution, planning and management.

Identified research directions for using manufacturing knowledge earlier in the product lifecycle

PubMed Central

Hedberg, Thomas D.; Hartman, Nathan W.; Rosche, Phil; Fischer, Kevin

2016-01-01

Design for Manufacturing (DFM), especially the use of manufacturing knowledge to support design decisions, has received attention in the academic domain. However, industry practice has not been studied enough to provide solutions that are mature for industry. The current state of the art for DFM is often rule-based functionality within Computer-Aided Design (CAD) systems that enforce specific design requirements. That rule-based functionality may or may not dynamically affect geometry definition. And, if rule-based functionality exists in the CAD system, it is typically a customization on a case-by-case basis. Manufacturing knowledge is a phrase with vast meanings, which may include knowledge on the effects of material properties decisions, machine and process capabilities, or understanding the unintended consequences of design decisions on manufacturing. One of the DFM questions to answer is how can manufacturing knowledge, depending on its definition, be used earlier in the product lifecycle to enable a more collaborative development environment? This paper will discuss the results of a workshop on manufacturing knowledge that highlights several research questions needing more study. This paper proposes recommendations for investigating the relationship of manufacturing knowledge with shape, behavior, and context characteristics of product to produce a better understanding of what knowledge is most important. In addition, the proposal includes recommendations for investigating the system-level barriers to reusing manufacturing knowledge and how model-based manufacturing may ease the burden of knowledge sharing. Lastly, the proposal addresses the direction of future research for holistic solutions of using manufacturing knowledge earlier in the product lifecycle. PMID:27990027

Identified research directions for using manufacturing knowledge earlier in the product lifecycle.

PubMed

Hedberg, Thomas D; Hartman, Nathan W; Rosche, Phil; Fischer, Kevin

2017-01-01

Design for Manufacturing (DFM), especially the use of manufacturing knowledge to support design decisions, has received attention in the academic domain. However, industry practice has not been studied enough to provide solutions that are mature for industry. The current state of the art for DFM is often rule-based functionality within Computer-Aided Design (CAD) systems that enforce specific design requirements. That rule-based functionality may or may not dynamically affect geometry definition. And, if rule-based functionality exists in the CAD system, it is typically a customization on a case-by-case basis. Manufacturing knowledge is a phrase with vast meanings, which may include knowledge on the effects of material properties decisions, machine and process capabilities, or understanding the unintended consequences of design decisions on manufacturing. One of the DFM questions to answer is how can manufacturing knowledge, depending on its definition, be used earlier in the product lifecycle to enable a more collaborative development environment? This paper will discuss the results of a workshop on manufacturing knowledge that highlights several research questions needing more study. This paper proposes recommendations for investigating the relationship of manufacturing knowledge with shape, behavior, and context characteristics of product to produce a better understanding of what knowledge is most important. In addition, the proposal includes recommendations for investigating the system-level barriers to reusing manufacturing knowledge and how model-based manufacturing may ease the burden of knowledge sharing. Lastly, the proposal addresses the direction of future research for holistic solutions of using manufacturing knowledge earlier in the product lifecycle.

Portfolio theory and the alternative decision rule of cost-effectiveness analysis: theoretical and practical considerations.

PubMed

Sendi, Pedram; Al, Maiwenn J; Gafni, Amiram; Birch, Stephen

2004-05-01

Bridges and Terris (Soc. Sci. Med. (2004)) critique our paper on the alternative decision rule of economic evaluation in the presence of uncertainty and constrained resources within the context of a portfolio of health care programs (Sendi et al. Soc. Sci. Med. 57 (2003) 2207). They argue that by not adopting a formal portfolio theory approach we overlook the optimal solution. We show that these arguments stem from a fundamental misunderstanding of the alternative decision rule of economic evaluation. In particular, the portfolio theory approach advocated by Bridges and Terris is based on the same theoretical assumptions that the alternative decision rule set out to relax. Moreover, Bridges and Terris acknowledge that the proposed portfolio theory approach may not identify the optimal solution to resource allocation problems. Hence, it provides neither theoretical nor practical improvements to the proposed alternative decision rule.

Collaboration pathway(s) using new tools for optimizing operational climate monitoring from space

NASA Astrophysics Data System (ADS)

Helmuth, Douglas B.; Selva, Daniel; Dwyer, Morgan M.

2014-10-01

Consistently collecting the earth's climate signatures remains a priority for world governments and international scientific organizations. Architecting a solution requires transforming scientific missions into an optimized robust `operational' constellation that addresses the needs of decision makers, scientific investigators and global users for trusted data. The application of new tools offers pathways for global architecture collaboration. Recent (2014) rulebased decision engine modeling runs that targeted optimizing the intended NPOESS architecture, becomes a surrogate for global operational climate monitoring architecture(s). This rule-based systems tools provide valuable insight for Global climate architectures, through the comparison and evaluation of alternatives considered and the exhaustive range of trade space explored. A representative optimization of Global ECV's (essential climate variables) climate monitoring architecture(s) is explored and described in some detail with thoughts on appropriate rule-based valuations. The optimization tools(s) suggest and support global collaboration pathways and hopefully elicit responses from the audience and climate science shareholders.

Evaluation of a multi-arm multi-stage Bayesian design for phase II drug selection trials - an example in hemato-oncology.

PubMed

Jacob, Louis; Uvarova, Maria; Boulet, Sandrine; Begaj, Inva; Chevret, Sylvie

2016-06-02

Multi-Arm Multi-Stage designs aim at comparing several new treatments to a common reference, in order to select or drop any treatment arm to move forward when such evidence already exists based on interim analyses. We redesigned a Bayesian adaptive design initially proposed for dose-finding, focusing our interest in the comparison of multiple experimental drugs to a control on a binary criterion measure. We redesigned a phase II clinical trial that randomly allocates patients across three (one control and two experimental) treatment arms to assess dropping decision rules. We were interested in dropping any arm due to futility, either based on historical control rate (first rule) or comparison across arms (second rule), and in stopping experimental arm due to its ability to reach a sufficient response rate (third rule), using the difference of response probabilities in Bayes binomial trials between the treated and control as a measure of treatment benefit. Simulations were then conducted to investigate the decision operating characteristics under a variety of plausible scenarios, as a function of the decision thresholds. Our findings suggest that one experimental treatment was less efficient than the control and could have been dropped from the trial based on a sample of approximately 20 instead of 40 patients. In the simulation study, stopping decisions were reached sooner for the first rule than for the second rule, with close mean estimates of response rates and small bias. According to the decision threshold, the mean sample size to detect the required 0.15 absolute benefit ranged from 63 to 70 (rule 3) with false negative rates of less than 2 % (rule 1) up to 6 % (rule 2). In contrast, detecting a 0.15 inferiority in response rates required a sample size ranging on average from 23 to 35 (rules 1 and 2, respectively) with a false positive rate ranging from 3.6 to 0.6 % (rule 3). Adaptive trial design is a good way to improve clinical trials. It allows removing ineffective drugs and reducing the trial sample size, while maintaining unbiased estimates. Decision thresholds can be set according to predefined fixed error decision rates. ClinicalTrials.gov Identifier: NCT01342692 .

Combined Economic and Hydrologic Modeling to Support Collaborative Decision Making Processes

NASA Astrophysics Data System (ADS)

Sheer, D. P.

2008-12-01

For more than a decade, the core concept of the author's efforts in support of collaborative decision making has been a combination of hydrologic simulation and multi-objective optimization. The modeling has generally been used to support collaborative decision making processes. The OASIS model developed by HydroLogics Inc. solves a multi-objective optimization at each time step using a mixed integer linear program (MILP). The MILP can be configured to include any user defined objective, including but not limited too economic objectives. For example, an estimated marginal value for water for crops and M&I use were included in the objective function to drive trades in a model of the lower Rio Grande. The formulation of the MILP, constraints and objectives, in any time step is conditional: it changes based on the value of state variables and dynamic external forcing functions, such as rainfall, hydrology, market prices, arrival of migratory fish, water temperature, etc. It therefore acts as a dynamic short term multi-objective economic optimization for each time step. MILP is capable of solving a general problem that includes a very realistic representation of the physical system characteristics in addition to the normal multi-objective optimization objectives and constraints included in economic models. In all of these models, the short term objective function is a surrogate for achieving long term multi-objective results. The long term performance for any alternative (especially including operating strategies) is evaluated by simulation. An operating rule is the combination of conditions, parameters, constraints and objectives used to determine the formulation of the short term optimization in each time step. Heuristic wrappers for the simulation program have been developed improve the parameters of an operating rule, and are initiating research on a wrapper that will allow us to employ a genetic algorithm to improve the form of the rule (conditions, constraints, and short term objectives) as well. In the models operating rules represent different models of human behavior, and the objective of the modeling is to find rules for human behavior that perform well in terms of long term human objectives. The conceptual model used to represent human behavior incorporates economic multi-objective optimization for surrogate objectives, and rules that set those objectives based on current conditions and accounting for uncertainty, at least implicitly. The author asserts that real world operating rules follow this form and have evolved because they have been perceived as successful in the past. Thus, the modeling efforts focus on human behavior in much the same way that economic models focus on human behavior. This paper illustrates the above concepts with real world examples.

Informing the design of clinical decision support services for evaluation of children with minor blunt head trauma in the emergency department: a sociotechnical analysis.

PubMed

Sheehan, Barbara; Nigrovic, Lise E; Dayan, Peter S; Kuppermann, Nathan; Ballard, Dustin W; Alessandrini, Evaline; Bajaj, Lalit; Goldberg, Howard; Hoffman, Jeffrey; Offerman, Steven R; Mark, Dustin G; Swietlik, Marguerite; Tham, Eric; Tzimenatos, Leah; Vinson, David R; Jones, Grant S; Bakken, Suzanne

2013-10-01

Integration of clinical decision support services (CDSS) into electronic health records (EHRs) may be integral to widespread dissemination and use of clinical prediction rules in the emergency department (ED). However, the best way to design such services to maximize their usefulness in such a complex setting is poorly understood. We conducted a multi-site cross-sectional qualitative study whose aim was to describe the sociotechnical environment in the ED to inform the design of a CDSS intervention to implement the Pediatric Emergency Care Applied Research Network (PECARN) clinical prediction rules for children with minor blunt head trauma. Informed by a sociotechnical model consisting of eight dimensions, we conducted focus groups, individual interviews and workflow observations in 11 EDs, of which 5 were located in academic medical centers and 6 were in community hospitals. A total of 126 ED clinicians, information technology specialists, and administrators participated. We clustered data into 19 categories of sociotechnical factors through a process of thematic analysis and subsequently organized the categories into a sociotechnical matrix consisting of three high-level sociotechnical dimensions (workflow and communication, organizational factors, human factors) and three themes (interdisciplinary assessment processes, clinical practices related to prediction rules, EHR as a decision support tool). Design challenges that emerged from the analysis included the need to use structured data fields to support data capture and re-use while maintaining efficient care processes, supporting interdisciplinary communication, and facilitating family-clinician interaction for decision-making. Copyright © 2013 Elsevier Inc. All rights reserved.

Integrative review of clinical decision support for registered nurses in acute care settings.

PubMed

Dunn Lopez, Karen; Gephart, Sheila M; Raszewski, Rebecca; Sousa, Vanessa; Shehorn, Lauren E; Abraham, Joanna

2017-03-01

To report on the state of the science of clinical decision support (CDS) for hospital bedside nurses. We performed an integrative review of qualitative and quantitative peer-reviewed original research studies using a structured search of PubMed, Embase, Cumulative Index to Nursing and Applied Health Literature (CINAHL), Scopus, Web of Science, and IEEE Xplore (Institute of Electrical and Electronics Engineers Xplore Digital Library). We included articles that reported on CDS targeting bedside nurses and excluded in stages based on rules for titles, abstracts, and full articles. We extracted research design and methods, CDS purpose, electronic health record integration, usability, and process and patient outcomes. Our search yielded 3157 articles. After removing duplicates and applying exclusion rules, 28 articles met the inclusion criteria. The majority of studies were single-site, descriptive or qualitative (43%) or quasi-experimental (36%). There was only 1 randomized controlled trial. The purpose of most CDS was to support diagnostic decision-making (36%), guideline adherence (32%), medication management (29%), and situational awareness (25%). All the studies that included process outcomes (7) and usability outcomes (4) and also had analytic procedures to detect changes in outcomes demonstrated statistically significant improvements. Three of 4 studies that included patient outcomes and also had analytic procedures to detect change showed statistically significant improvements. No negative effects of CDS were found on process, usability, or patient outcomes. Clinical support systems targeting bedside nurses have positive effects on outcomes and hold promise for improving care quality; however, this research is lagging behind studies of CDS targeting medical decision-making in both volume and level of evidence. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Design and usability of heuristic‐based deliberation tools for women facing amniocentesis

PubMed Central

Durand, Marie‐Anne; Wegwarth, Odette; Boivin, Jacky; Elwyn, Glyn

2011-01-01

Abstract Background  Evidence suggests that in decision contexts characterized by uncertainty and time constraints (e.g. health‐care decisions), fast and frugal decision‐making strategies (heuristics) may perform better than complex rules of reasoning. Objective  To examine whether it is possible to design deliberation components in decision support interventions using simple models (fast and frugal heuristics). Design  The ‘Take The Best’ heuristic (i.e. selection of a ‘most important reason’) and ‘The Tallying’ integration algorithm (i.e. unitary weighing of pros and cons) were used to develop two deliberation components embedded in a Web‐based decision support intervention for women facing amniocentesis testing. Ten researchers (recruited from 15), nine health‐care providers (recruited from 28) and ten pregnant women (recruited from 14) who had recently been offered amniocentesis testing appraised evolving versions of ‘your most important reason’ (Take The Best) and ‘weighing it up’ (Tallying). Results  Most researchers found the tools useful in facilitating decision making although emphasized the need for simple instructions and clear layouts. Health‐care providers however expressed concerns regarding the usability and clarity of the tools. By contrast, 7 out of 10 pregnant women found the tools useful in weighing up the pros and cons of each option, helpful in structuring and clarifying their thoughts and visualizing their decision efforts. Several pregnant women felt that ‘weighing it up’ and ‘your most important reason’ were not appropriate when facing such a difficult and emotional decision. Conclusion  Theoretical approaches based on fast and frugal heuristics can be used to develop deliberation tools that provide helpful support to patients facing real‐world decisions about amniocentesis. PMID:21241434

The future of decision-making in critical care after Cuthbertson v. Rasouli.

PubMed

Hawryluck, Laura; Baker, Andrew J; Faith, Andrew; Singh, Jeffrey M

2014-10-01

The Supreme Court of Canada (SCC) ruling on Cuthbertson v. Rasouli has implications for all acute healthcare providers. This well-publicized case involved a disagreement between healthcare providers and a patient's family regarding the principles surrounding withdrawal of life support, which the physicians involved considered no longer of medical benefit and outside the standard of care, and whether consent was required for such withdrawals. Our objective in writing this article is to clarify the implications of this ruling on the care of critically ill patients. SCC ruling Cuthbertson v. Rasouli. The SCC ruled that consent must be obtained for all treatments that serve a "health-related purpose", including withdrawal of such treatments. The SCC did not fully consider what the standard of care should be. Health-related purpose is not sufficient in and of itself to mandate treatment, and clinicians must still ensure that their patients or decision-makers are aware of the possible medical benefits, risks, and expected outcomes of treatments. The provision of treatments that have no potential to provide medical benefit and carry only risks would still fall outside the standard of care. Nevertheless, due to their health-related purpose, physicians must seek consent for the discontinuation of these treatments. The SCC ruled that due to the legal definition of "health-related purpose", which is distinct from medical benefit, consent is required to withdraw life-support and outlined the steps to be taken should conflict arise. The SCC decision did not directly address the role of medical standard of care in these situations. In order to ensure optimal decision-making and communication with patients and their families, it is critical for healthcare providers to have a clear understanding of the implications of this legal ruling on medical practice.

Extraneous factors in judicial decisions

PubMed Central

Danziger, Shai; Levav, Jonathan; Avnaim-Pesso, Liora

2011-01-01

Are judicial rulings based solely on laws and facts? Legal formalism holds that judges apply legal reasons to the facts of a case in a rational, mechanical, and deliberative manner. In contrast, legal realists argue that the rational application of legal reasons does not sufficiently explain the decisions of judges and that psychological, political, and social factors influence judicial rulings. We test the common caricature of realism that justice is “what the judge ate for breakfast” in sequential parole decisions made by experienced judges. We record the judges’ two daily food breaks, which result in segmenting the deliberations of the day into three distinct “decision sessions.” We find that the percentage of favorable rulings drops gradually from ≈65% to nearly zero within each decision session and returns abruptly to ≈65% after a break. Our findings suggest that judicial rulings can be swayed by extraneous variables that should have no bearing on legal decisions. PMID:21482790

Building a Computer Program to Support Children, Parents, and Distraction during Healthcare Procedures

PubMed Central

McCarthy, Ann Marie; Kleiber, Charmaine; Ataman, Kaan; Street, W. Nick; Zimmerman, M. Bridget; Ersig, Anne L.

2012-01-01

This secondary data analysis used data mining methods to develop predictive models of child risk for distress during a healthcare procedure. Data used came from a study that predicted factors associated with children’s responses to an intravenous catheter insertion while parents provided distraction coaching. From the 255 items used in the primary study, 44 predictive items were identified through automatic feature selection and used to build support vector machine regression models. Models were validated using multiple cross-validation tests and by comparing variables identified as explanatory in the traditional versus support vector machine regression. Rule-based approaches were applied to the model outputs to identify overall risk for distress. A decision tree was then applied to evidence-based instructions for tailoring distraction to characteristics and preferences of the parent and child. The resulting decision support computer application, the Children, Parents and Distraction (CPaD), is being used in research. Future use will support practitioners in deciding the level and type of distraction intervention needed by a child undergoing a healthcare procedure. PMID:22805121

Inside the black box: starting to uncover the underlying decision rules used in one-by-one expert assessment of occupational exposure in case-control studies

PubMed Central

Wheeler, David C.; Burstyn, Igor; Vermeulen, Roel; Yu, Kai; Shortreed, Susan M.; Pronk, Anjoeka; Stewart, Patricia A.; Colt, Joanne S.; Baris, Dalsu; Karagas, Margaret R.; Schwenn, Molly; Johnson, Alison; Silverman, Debra T.; Friesen, Melissa C.

2014-01-01

Objectives Evaluating occupational exposures in population-based case-control studies often requires exposure assessors to review each study participants' reported occupational information job-by-job to derive exposure estimates. Although such assessments likely have underlying decision rules, they usually lack transparency, are time-consuming and have uncertain reliability and validity. We aimed to identify the underlying rules to enable documentation, review, and future use of these expert-based exposure decisions. Methods Classification and regression trees (CART, predictions from a single tree) and random forests (predictions from many trees) were used to identify the underlying rules from the questionnaire responses and an expert's exposure assignments for occupational diesel exhaust exposure for several metrics: binary exposure probability and ordinal exposure probability, intensity, and frequency. Data were split into training (n=10,488 jobs), testing (n=2,247), and validation (n=2,248) data sets. Results The CART and random forest models' predictions agreed with 92–94% of the expert's binary probability assignments. For ordinal probability, intensity, and frequency metrics, the two models extracted decision rules more successfully for unexposed and highly exposed jobs (86–90% and 57–85%, respectively) than for low or medium exposed jobs (7–71%). Conclusions CART and random forest models extracted decision rules and accurately predicted an expert's exposure decisions for the majority of jobs and identified questionnaire response patterns that would require further expert review if the rules were applied to other jobs in the same or different study. This approach makes the exposure assessment process in case-control studies more transparent and creates a mechanism to efficiently replicate exposure decisions in future studies. PMID:23155187

A decision-support system for the analysis of clinical practice patterns.

PubMed

Balas, E A; Li, Z R; Mitchell, J A; Spencer, D C; Brent, E; Ewigman, B G

1994-01-01

Several studies documented substantial variation in medical practice patterns, but physicians often do not have adequate information on the cumulative clinical and financial effects of their decisions. The purpose of developing an expert system for the analysis of clinical practice patterns was to assist providers in analyzing and improving the process and outcome of patient care. The developed QFES (Quality Feedback Expert System) helps users in the definition and evaluation of measurable quality improvement objectives. Based on objectives and actual clinical data, several measures can be calculated (utilization of procedures, annualized cost effect of using a particular procedure, and expected utilization based on peer-comparison and case-mix adjustment). The quality management rules help to detect important discrepancies among members of the selected provider group and compare performance with objectives. The system incorporates a variety of data and knowledge bases: (i) clinical data on actual practice patterns, (ii) frames of quality parameters derived from clinical practice guidelines, and (iii) rules of quality management for data analysis. An analysis of practice patterns of 12 family physicians in the management of urinary tract infections illustrates the use of the system.

A collaborative framework for contributing DICOM RT PHI (Protected Health Information) to augment data mining in clinical decision support

NASA Astrophysics Data System (ADS)

Deshpande, Ruchi; Thuptimdang, Wanwara; DeMarco, John; Liu, Brent J.

2014-03-01

We have built a decision support system that provides recommendations for customizing radiation therapy treatment plans, based on patient models generated from a database of retrospective planning data. This database consists of relevant metadata and information derived from the following DICOM objects - CT images, RT Structure Set, RT Dose and RT Plan. The usefulness and accuracy of such patient models partly depends on the sample size of the learning data set. Our current goal is to increase this sample size by expanding our decision support system into a collaborative framework to include contributions from multiple collaborators. Potential collaborators are often reluctant to upload even anonymized patient files to repositories outside their local organizational network in order to avoid any conflicts with HIPAA Privacy and Security Rules. We have circumvented this problem by developing a tool that can parse DICOM files on the client's side and extract de-identified numeric and text data from DICOM RT headers for uploading to a centralized system. As a result, the DICOM files containing PHI remain local to the client side. This is a novel workflow that results in adding only relevant yet valuable data from DICOM files to the centralized decision support knowledge base in such a way that the DICOM files never leave the contributor's local workstation in a cloud-based environment. Such a workflow serves to encourage clinicians to contribute data for research endeavors by ensuring protection of electronic patient data.

A sharable cloud-based pancreaticoduodenectomy collaborative database for physicians: emphasis on security and clinical rule supporting.

PubMed

Yu, Hwan-Jeu; Lai, Hong-Shiee; Chen, Kuo-Hsin; Chou, Hsien-Cheng; Wu, Jin-Ming; Dorjgochoo, Sarangerel; Mendjargal, Adilsaikhan; Altangerel, Erdenebaatar; Tien, Yu-Wen; Hsueh, Chih-Wen; Lai, Feipei

2013-08-01

Pancreaticoduodenectomy (PD) is a major operation with high complication rate. Thereafter, patients may develop morbidity because of the complex reconstruction and loss of pancreatic parenchyma. A well-designed database is very important to address both the short-term and long-term outcomes after PD. The objective of this research was to build an international PD database implemented with security and clinical rule supporting functions, which made the data-sharing easier and improve the accuracy of data. The proposed system is a cloud-based application. To fulfill its requirements, the system comprises four subsystems: a data management subsystem, a clinical rule supporting subsystem, a short message notification subsystem, and an information security subsystem. After completing the surgery, the physicians input the data retrospectively, which are analyzed to study factors associated with post-PD common complications (delayed gastric emptying and pancreatic fistula) to validate the clinical value of this system. Currently, this database contains data from nearly 500 subjects. Five medical centers in Taiwan and two cancer centers in Mongolia are participating in this study. A data mining model of the decision tree analysis showed that elderly patients (>76 years) with pylorus-preserving PD (PPPD) have higher proportion of delayed gastric emptying. About the pancreatic fistula, the data mining model of the decision tree analysis revealed that cases with non-pancreaticogastrostomy (PG) reconstruction - body mass index (BMI)>29.65 or PG reconstruction - BMI>23.7 - non-classic PD have higher proportion of pancreatic fistula after PD. The proposed system allows medical staff to collect and store clinical data in a cloud, sharing the data with other physicians in a secure manner to achieve collaboration in research. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

The cost-effectiveness of diagnostic management strategies for adults with minor head injury.

PubMed

Holmes, M W; Goodacre, S; Stevenson, M D; Pandor, A; Pickering, A

2012-09-01

To estimate the cost-effectiveness of diagnostic management strategies for adults with minor head injury. A mathematical model was constructed to evaluate the incremental costs and effectiveness (Quality Adjusted Life years Gained, QALYs) of ten diagnostic management strategies for adults with minor head injuries. Secondary analyses were undertaken to determine the cost-effectiveness of hospital admission compared to discharge home and to explore the cost-effectiveness of strategies when no responsible adult was available to observe the patient after discharge. The apparent optimal strategy was based on the high and medium risk Canadian CT Head Rule (CCHRhm), although the costs and outcomes associated with each strategy were broadly similar. Hospital admission for patients with non-neurosurgical injury on CT dominated discharge home, whilst hospital admission for clinically normal patients with a normal CT was not cost-effective compared to discharge home with or without a responsible adult at £39 and £2.5 million per QALY, respectively. A selective CT strategy with discharge home if the CT scan was normal remained optimal compared to not investigating or CT scanning all patients when there was no responsible adult available to observe them after discharge. Our economic analysis confirms that the recent extension of access to CT scanning for minor head injury is appropriate. Liberal use of CT scanning based on a high sensitivity decision rule is not only effective but also cost-saving. The cost of CT scanning is very small compared to the estimated cost of caring for patients with brain injury worsened by delayed treatment. It is recommended therefore that all hospitals receiving patients with minor head injury should have unrestricted access to CT scanning for use in conjunction with evidence based guidelines. Provisionally the CCHRhm decision rule appears to be the best strategy although there is considerable uncertainty around the optimal decision rule. However, the CCHRhm rule appears to be the most widely validated and it therefore seems appropriate to conclude that the CCHRhm rule has the best evidence to support its use. Copyright © 2011 Elsevier Ltd. All rights reserved.

Knowledge-based reasoning in the Paladin tactical decision generation system

NASA Technical Reports Server (NTRS)

Chappell, Alan R.

1993-01-01

A real-time tactical decision generation system for air combat engagements, Paladin, has been developed. A pilot's job in air combat includes tasks that are largely symbolic. These symbolic tasks are generally performed through the application of experience and training (i.e. knowledge) gathered over years of flying a fighter aircraft. Two such tasks, situation assessment and throttle control, are identified and broken out in Paladin to be handled by specialized knowledge based systems. Knowledge pertaining to these tasks is encoded into rule-bases to provide the foundation for decisions. Paladin uses a custom built inference engine and a partitioned rule-base structure to give these symbolic results in real-time. This paper provides an overview of knowledge-based reasoning systems as a subset of rule-based systems. The knowledge used by Paladin in generating results as well as the system design for real-time execution is discussed.

Individual versus Household Migration Decision Rules: Gender and Marital Status Differences in Intentions to Migrate in South Africa.

PubMed

Gubhaju, Bina; De Jong, Gordon F

2009-03-01

This research tests the thesis that the neoclassical micro-economic and the new household economic theoretical assumptions on migration decision-making rules are segmented by gender, marital status, and time frame of intention to migrate. Comparative tests of both theories within the same study design are relatively rare. Utilizing data from the Causes of Migration in South Africa national migration survey, we analyze how individually held "own-future" versus alternative "household well-being" migration decision rules effect the intentions to migrate of male and female adults in South Africa. Results from the gender and marital status specific logistic regressions models show consistent support for the different gender-marital status decision rule thesis. Specifically, the "maximizing one's own future" neoclassical microeconomic theory proposition is more applicable for never married men and women, the "maximizing household income" proposition for married men with short-term migration intentions, and the "reduce household risk" proposition for longer time horizon migration intentions of married men and women. Results provide new evidence on the way household strategies and individual goals jointly affect intentions to move or stay.

Knowledge as a Service at the Point of Care.

PubMed

Shellum, Jane L; Freimuth, Robert R; Peters, Steve G; Nishimura, Rick A; Chaudhry, Rajeev; Demuth, Steve J; Knopp, Amy L; Miksch, Timothy A; Milliner, Dawn S

2016-01-01

An electronic health record (EHR) can assist the delivery of high-quality patient care, in part by providing the capability for a broad range of clinical decision support, including contextual references (e.g., Infobuttons), alerts and reminders, order sets, and dashboards. All of these decision support tools are based on clinical knowledge; unfortunately, the mechanisms for managing rules, order sets, Infobuttons, and dashboards are often unrelated, making it difficult to coordinate the application of clinical knowledge to various components of the clinical workflow. Additional complexity is encountered when updating enterprise-wide knowledge bases and delivering the content through multiple modalities to different consumers. We present the experience of Mayo Clinic as a case study to examine the requirements and implementation challenges related to knowledge management across a large, multi-site medical center. The lessons learned through the development of our knowledge management and delivery platform will help inform the future development of interoperable knowledge resources.

Knowledge as a Service at the Point of Care

PubMed Central

Shellum, Jane L.; Freimuth, Robert R.; Peters, Steve G.; Nishimura, Rick A.; Chaudhry, Rajeev; Demuth, Steve J.; Knopp, Amy L.; Miksch, Timothy A.; Milliner, Dawn S.

2016-01-01

An electronic health record (EHR) can assist the delivery of high-quality patient care, in part by providing the capability for a broad range of clinical decision support, including contextual references (e.g., Infobuttons), alerts and reminders, order sets, and dashboards. All of these decision support tools are based on clinical knowledge; unfortunately, the mechanisms for managing rules, order sets, Infobuttons, and dashboards are often unrelated, making it difficult to coordinate the application of clinical knowledge to various components of the clinical workflow. Additional complexity is encountered when updating enterprise-wide knowledge bases and delivering the content through multiple modalities to different consumers. We present the experience of Mayo Clinic as a case study to examine the requirements and implementation challenges related to knowledge management across a large, multi-site medical center. The lessons learned through the development of our knowledge management and delivery platform will help inform the future development of interoperable knowledge resources. PMID:28269911

Analysis of Human Mobility Based on Cellular Data

NASA Astrophysics Data System (ADS)

Arifiansyah, F.; Saptawati, G. A. P.

2017-01-01

Nowadays not only adult but even teenager and children have then own mobile phones. This phenomena indicates that the mobile phone becomes an important part of everyday’s life. Based on these indication, the amount of cellular data also increased rapidly. Cellular data defined as the data that records communication among mobile phone users. Cellular data is easy to obtain because the telecommunications company had made a record of the data for the billing system of the company. Billing data keeps a log of the users cellular data usage each time. We can obtained information from the data about communication between users. Through data visualization process, an interesting pattern can be seen in the raw cellular data, so that users can obtain prior knowledge to perform data analysis. Cellular data processing can be done using data mining to find out human mobility patterns and on the existing data. In this paper, we use frequent pattern mining and finding association rules to observe the relation between attributes in cellular data and then visualize them. We used weka tools for finding the rules in stage of data mining. Generally, the utilization of cellular data can provide supporting information for the decision making process and become a data support to provide solutions and information needed by the decision makers.

49 CFR 1017.7 - Written decision following hearing.

Code of Federal Regulations, 2010 CFR

2010-10-01

... BOARD, DEPARTMENT OF TRANSPORTATION GENERAL RULES AND REGULATIONS DEBT COLLECTION-COLLECTION BY OFFSET FROM INDEBTED GOVERNMENT AND FORMER GOVERNMENT EMPLOYEES § 1017.7 Written decision following hearing... facts presented to support the nature and origin of the alleged debt; (2) The Administrative Law Judge's...

16 CFR 5.64 - Initial decision.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 16 Commercial Practices 1 2010-01-01 2010-01-01 false Initial decision. 5.64 Section 5.64 Commercial Practices FEDERAL TRADE COMMISSION ORGANIZATION, PROCEDURES AND RULES OF PRACTICE STANDARDS OF... this subpart, except that the determination of the Administrative Law Judge must be supported by a...

Evaluating MEDEVAC Force Structure Requirements Using an Updated Army Scenario, Total Army Analysis Admission Data, Monte Carlo Simulation, and Theater Structure.

PubMed

Fulton, Lawrence; Kerr, Bernie; Inglis, James M; Brooks, Matthew; Bastian, Nathaniel D

2015-07-01

In this study, we re-evaluate air ambulance requirements (rules of allocation) and planning considerations based on an Army-approved, Theater Army Analysis scenario. A previous study using workload only estimated a requirement of 0.4 to 0.6 aircraft per admission, a significant bolus over existence-based rules. In this updated study, we estimate requirements for Phase III (major combat operations) using a simulation grounded in previously published work and Phase IV (stability operations) based on four rules of allocation: unit existence rules, workload factors, theater structure (geography), and manual input. This study improves upon previous work by including the new air ambulance mission requirements of Department of Defense 51001.1, Roles and Functions of the Services, by expanding the analysis over two phases, and by considering unit rotation requirements known as Army Force Generation based on Department of Defense policy. The recommendations of this study are intended to inform future planning factors and already provided decision support to the Army Aviation Branch in determining force structure requirements. Reprint & Copyright © 2015 Association of Military Surgeons of the U.S.

Characterising bias in regulatory risk and decision analysis: An analysis of heuristics applied in health technology appraisal, chemicals regulation, and climate change governance.

PubMed

MacGillivray, Brian H

2017-08-01

In many environmental and public health domains, heuristic methods of risk and decision analysis must be relied upon, either because problem structures are ambiguous, reliable data is lacking, or decisions are urgent. This introduces an additional source of uncertainty beyond model and measurement error - uncertainty stemming from relying on inexact inference rules. Here we identify and analyse heuristics used to prioritise risk objects, to discriminate between signal and noise, to weight evidence, to construct models, to extrapolate beyond datasets, and to make policy. Some of these heuristics are based on causal generalisations, yet can misfire when these relationships are presumed rather than tested (e.g. surrogates in clinical trials). Others are conventions designed to confer stability to decision analysis, yet which may introduce serious error when applied ritualistically (e.g. significance testing). Some heuristics can be traced back to formal justifications, but only subject to strong assumptions that are often violated in practical applications. Heuristic decision rules (e.g. feasibility rules) in principle act as surrogates for utility maximisation or distributional concerns, yet in practice may neglect costs and benefits, be based on arbitrary thresholds, and be prone to gaming. We highlight the problem of rule-entrenchment, where analytical choices that are in principle contestable are arbitrarily fixed in practice, masking uncertainty and potentially introducing bias. Strategies for making risk and decision analysis more rigorous include: formalising the assumptions and scope conditions under which heuristics should be applied; testing rather than presuming their underlying empirical or theoretical justifications; using sensitivity analysis, simulations, multiple bias analysis, and deductive systems of inference (e.g. directed acyclic graphs) to characterise rule uncertainty and refine heuristics; adopting "recovery schemes" to correct for known biases; and basing decision rules on clearly articulated values and evidence, rather than convention. Copyright © 2017. Published by Elsevier Ltd.

A Comparison of Common and Novel Curriculum-Based Measurement of Reading Decision Rules to Predict Spring Performance for Students Receiving Supplemental Interventions

ERIC Educational Resources Information Center

Van Norman, Ethan R.; Parker, David C.

2018-01-01

Recent simulations suggest that trend line decision rules applied to curriculum-based measurement of reading progress monitoring data may lead to inaccurate interpretations unless data are collected for upward of 3 months. The authors of those studies did not manipulate goal line slope or account for a student's level of initial performance when…

Determining rules for closing customer service centers: A public utility company's fuzzy decision

NASA Technical Reports Server (NTRS)

Dekorvin, Andre; Shipley, Margaret F.

1992-01-01

In the present work, we consider the general problem of knowledge acquisition under uncertainty. A commonly used method is to learn by examples. We observe how the expert solves specific cases and from this infer some rules by which the decision was made. Unique to this work is the fuzzy set representation of the conditions or attributes upon which the decision make may base his fuzzy set decision. From our examples, we infer certain and possible rules containing fuzzy terms. It should be stressed that the procedure determines how closely the expert follows the conditions under consideration in making his decision. We offer two examples pertaining to the possible decision to close a customer service center of a public utility company. In the first example, the decision maker does not follow too closely the conditions. In the second example, the conditions are much more relevant to the decision of the expert.

Flexible Early Warning Systems with Workflows and Decision Tables

NASA Astrophysics Data System (ADS)

Riedel, F.; Chaves, F.; Zeiner, H.

2012-04-01

An essential part of early warning systems and systems for crisis management are decision support systems that facilitate communication and collaboration. Often official policies specify how different organizations collaborate and what information is communicated to whom. For early warning systems it is crucial that information is exchanged dynamically in a timely manner and all participants get exactly the information they need to fulfil their role in the crisis management process. Information technology obviously lends itself to automate parts of the process. We have experienced however that in current operational systems the information logistics processes are hard-coded, even though they are subject to change. In addition, systems are tailored to the policies and requirements of a certain organization and changes can require major software refactoring. We seek to develop a system that can be deployed and adapted to multiple organizations with different dynamic runtime policies. A major requirement for such a system is that changes can be applied locally without affecting larger parts of the system. In addition to the flexibility regarding changes in policies and processes, the system needs to be able to evolve; when new information sources become available, it should be possible to integrate and use these in the decision process. In general, this kind of flexibility comes with a significant increase in complexity. This implies that only IT professionals can maintain a system that can be reconfigured and adapted; end-users are unable to utilise the provided flexibility. In the business world similar problems arise and previous work suggested using business process management systems (BPMS) or workflow management systems (WfMS) to guide and automate early warning processes or crisis management plans. However, the usability and flexibility of current WfMS are limited, because current notations and user interfaces are still not suitable for end-users, and workflows are usually only suited for rigid processes. We show how improvements can be achieved by using decision tables and rule-based adaptive workflows. Decision tables have been shown to be an intuitive tool that can be used by domain experts to express rule sets that can be interpreted automatically at runtime. Adaptive workflows use a rule-based approach to increase the flexibility of workflows by providing mechanisms to adapt workflows based on context changes, human intervention and availability of services. The combination of workflows, decision tables and rule-based adaption creates a framework that opens up new possibilities for flexible and adaptable workflows, especially, for use in early warning and crisis management systems.

A Clinical Decision Support System for Breast Cancer Patients

NASA Astrophysics Data System (ADS)

Fernandes, Ana S.; Alves, Pedro; Jarman, Ian H.; Etchells, Terence A.; Fonseca, José M.; Lisboa, Paulo J. G.

This paper proposes a Web clinical decision support system for clinical oncologists and for breast cancer patients making prognostic assessments, using the particular characteristics of the individual patient. This system comprises three different prognostic modelling methodologies: the clinically widely used Nottingham prognostic index (NPI); the Cox regression modelling and a partial logistic artificial neural network with automatic relevance determination (PLANN-ARD). All three models yield a different prognostic index that can be analysed together in order to obtain a more accurate prognostic assessment of the patient. Missing data is incorporated in the mentioned models, a common issue in medical data that was overcome using multiple imputation techniques. Risk group assignments are also provided through a methodology based on regression trees, where Boolean rules can be obtained expressed with patient characteristics.

Evaluation of RxNorm for Medication Clinical Decision Support.

PubMed

Freimuth, Robert R; Wix, Kelly; Zhu, Qian; Siska, Mark; Chute, Christopher G

2014-01-01

We evaluated the potential use of RxNorm to provide standardized representations of generic drug name and route of administration to facilitate management of drug lists for clinical decision support (CDS) rules. We found a clear representation of generic drug name but not route of administration. We identified several issues related to data quality, including erroneous or missing defined relationships, and the use of different concept hierarchies to represent the same drug. More importantly, we found extensive semantic precoordination of orthogonal concepts related to route and dose form, which would complicate the use of RxNorm for drug-based CDS. This study demonstrated that while RxNorm is a valuable resource for the standardization of medications used in clinical practice, additional work is required to enhance the terminology so that it can support expanded use cases, such as managing drug lists for CDS.

Evaluation of RxNorm for Medication Clinical Decision Support

PubMed Central

Freimuth, Robert R.; Wix, Kelly; Zhu, Qian; Siska, Mark; Chute, Christopher G.

2014-01-01

We evaluated the potential use of RxNorm to provide standardized representations of generic drug name and route of administration to facilitate management of drug lists for clinical decision support (CDS) rules. We found a clear representation of generic drug name but not route of administration. We identified several issues related to data quality, including erroneous or missing defined relationships, and the use of different concept hierarchies to represent the same drug. More importantly, we found extensive semantic precoordination of orthogonal concepts related to route and dose form, which would complicate the use of RxNorm for drug-based CDS. This study demonstrated that while RxNorm is a valuable resource for the standardization of medications used in clinical practice, additional work is required to enhance the terminology so that it can support expanded use cases, such as managing drug lists for CDS. PMID:25954360

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

Wu, Qishi; Zhu, Mengxia; Rao, Nageswara S

We propose an intelligent decision support system based on sensor and computer networks that incorporates various component techniques for sensor deployment, data routing, distributed computing, and information fusion. The integrated system is deployed in a distributed environment composed of both wireless sensor networks for data collection and wired computer networks for data processing in support of homeland security defense. We present the system framework and formulate the analytical problems and develop approximate or exact solutions for the subtasks: (i) sensor deployment strategy based on a two-dimensional genetic algorithm to achieve maximum coverage with cost constraints; (ii) data routing scheme tomore » achieve maximum signal strength with minimum path loss, high energy efficiency, and effective fault tolerance; (iii) network mapping method to assign computing modules to network nodes for high-performance distributed data processing; and (iv) binary decision fusion rule that derive threshold bounds to improve system hit rate and false alarm rate. These component solutions are implemented and evaluated through either experiments or simulations in various application scenarios. The extensive results demonstrate that these component solutions imbue the integrated system with the desirable and useful quality of intelligence in decision making.« less

Application of preprocessing filtering on Decision Tree C4.5 and rough set theory

NASA Astrophysics Data System (ADS)

Chan, Joseph C. C.; Lin, Tsau Y.

2001-03-01

This paper compares two artificial intelligence methods: the Decision Tree C4.5 and Rough Set Theory on the stock market data. The Decision Tree C4.5 is reviewed with the Rough Set Theory. An enhanced window application is developed to facilitate the pre-processing filtering by introducing the feature (attribute) transformations, which allows users to input formulas and create new attributes. Also, the application produces three varieties of data set with delaying, averaging, and summation. The results prove the improvement of pre-processing by applying feature (attribute) transformations on Decision Tree C4.5. Moreover, the comparison between Decision Tree C4.5 and Rough Set Theory is based on the clarity, automation, accuracy, dimensionality, raw data, and speed, which is supported by the rules sets generated by both algorithms on three different sets of data.

What Goes Into a Decision? How Nursing Faculty Decide Which Best Practices to Use for Classroom Testing.

PubMed

Killingsworth, Erin; Kimble, Laura P; Sudia, Tanya

2015-01-01

To explore the decision-making process of BSN faculty when determining which best practices to use for classroom testing. A descriptive, correlational study was conducted with a national sample (N = 127) of full-time BSN faculty. Participants completed a web-based survey incorporating instruments that measured beliefs about evaluation, decision-making, and best practices for item analysis and constructing and revising classroom tests. Study participants represented 31 states and were primarily middle-aged white women. In multiple linear regression analyses, faculty beliefs, contextual factors for decision-making, and decision-making processes accounted for statistically significant amounts of the variance in item analysis and test construction and revision. Strong faculty beliefs that rules were important when evaluating students was a significant predictor of increased use of best practices. Results support that understanding faculty beliefs around classroom testing is important in promoting the use of best practices.

An Intelligent Tutoring System for Classifying Students into Instructional Treatments with Mastery Scores. Research Report 94-15.

ERIC Educational Resources Information Center

Vos, Hans J.

As part of a project formulating optimal rules for decision making in computer assisted instructional systems in which the computer is used as a decision support tool, an approach that simultaneously optimizes classification of students into two treatments, each followed by a mastery decision, is presented using the framework of Bayesian decision…

Multicriteria meta-heuristics for AGV dispatching control based on computational intelligence.

PubMed

Naso, David; Turchiano, Biagio

2005-04-01

In many manufacturing environments, automated guided vehicles are used to move the processed materials between various pickup and delivery points. The assignment of vehicles to unit loads is a complex problem that is often solved in real-time with simple dispatching rules. This paper proposes an automated guided vehicles dispatching approach based on computational intelligence. We adopt a fuzzy multicriteria decision strategy to simultaneously take into account multiple aspects in every dispatching decision. Since the typical short-term view of dispatching rules is one of the main limitations of such real-time assignment heuristics, we also incorporate in the multicriteria algorithm a specific heuristic rule that takes into account the empty-vehicle travel on a longer time-horizon. Moreover, we also adopt a genetic algorithm to tune the weights associated to each decision criteria in the global decision algorithm. The proposed approach is validated by means of a comparison with other dispatching rules, and with other recently proposed multicriteria dispatching strategies also based on computational Intelligence. The analysis of the results obtained by the proposed dispatching approach in both nominal and perturbed operating conditions (congestions, faults) confirms its effectiveness.

Intelligent Diagnostic Assistant for Complicated Skin Diseases through C5's Algorithm.

PubMed

Jeddi, Fatemeh Rangraz; Arabfard, Masoud; Kermany, Zahra Arab

2017-09-01

Intelligent Diagnostic Assistant can be used for complicated diagnosis of skin diseases, which are among the most common causes of disability. The aim of this study was to design and implement a computerized intelligent diagnostic assistant for complicated skin diseases through C5's Algorithm. An applied-developmental study was done in 2015. Knowledge base was developed based on interviews with dermatologists through questionnaires and checklists. Knowledge representation was obtained from the train data in the database using Excel Microsoft Office. Clementine Software and C5's Algorithms were applied to draw the decision tree. Analysis of test accuracy was performed based on rules extracted using inference chains. The rules extracted from the decision tree were entered into the CLIPS programming environment and the intelligent diagnostic assistant was designed then. The rules were defined using forward chaining inference technique and were entered into Clips programming environment as RULE. The accuracy and error rates obtained in the training phase from the decision tree were 99.56% and 0.44%, respectively. The accuracy of the decision tree was 98% and the error was 2% in the test phase. Intelligent diagnostic assistant can be used as a reliable system with high accuracy, sensitivity, specificity, and agreement.

The Decision to Engage Cognitive Control Is Driven by Expected Reward-Value: Neural and Behavioral Evidence

PubMed Central

Dixon, Matthew L.; Christoff, Kalina

2012-01-01

Cognitive control is a fundamental skill reflecting the active use of task-rules to guide behavior and suppress inappropriate automatic responses. Prior work has traditionally used paradigms in which subjects are told when to engage cognitive control. Thus, surprisingly little is known about the factors that influence individuals' initial decision of whether or not to act in a reflective, rule-based manner. To examine this, we took three classic cognitive control tasks (Stroop, Wisconsin Card Sorting Task, Go/No-Go task) and created novel ‘free-choice’ versions in which human subjects were free to select an automatic, pre-potent action, or an action requiring rule-based cognitive control, and earned varying amounts of money based on their choices. Our findings demonstrated that subjects' decision to engage cognitive control was driven by an explicit representation of monetary rewards expected to be obtained from rule-use. Subjects rarely engaged cognitive control when the expected outcome was of equal or lesser value as compared to the value of the automatic response, but frequently engaged cognitive control when it was expected to yield a larger monetary outcome. Additionally, we exploited fMRI-adaptation to show that the lateral prefrontal cortex (LPFC) represents associations between rules and expected reward outcomes. Together, these findings suggest that individuals are more likely to act in a reflective, rule-based manner when they expect that it will result in a desired outcome. Thus, choosing to exert cognitive control is not simply a matter of reason and willpower, but rather, conforms to standard mechanisms of value-based decision making. Finally, in contrast to current models of LPFC function, our results suggest that the LPFC plays a direct role in representing motivational incentives. PMID:23284730

Baxter v. Montana, libertarianism, and end-of-life: the ripe time for a paradigm shift.

PubMed

Ruble, James H

2010-09-01

Baxter v. Montana (2009 WL 5155363 [Mont. 2009]) is a recent decision from the Montana Supreme Court that provides new legal insight into the societal issue of aid in dying. This case involves interests of persons with terminal illness, medical practitioners, law enforcement, legislative and judicial bodies, as well as the citizens of Montana. A summary judgment ruling at the Montana district court level was based almost entirely on a constitutional fundamental rights analysis. In contrast, the Montana Supreme Court affirming decision was based almost entirely on a statutory rights analysis. Both rulings from the Montana courts support the position that licensed prescribers in Montana who provide aid in dying assistance to terminally ill patients have some immunity from criminal prosecution. Each side in the case argued what they believed to be the intents and purposes of the people of Montana. Baxter v. Montana illustrates different methods to determine the will of the people concerning aid in dying and public policy. This case very subtly suggests a paradigm shift may be occurring in aid in dying policy.

Decision Fusion with Channel Errors in Distributed Decode-Then-Fuse Sensor Networks

PubMed Central

Yan, Yongsheng; Wang, Haiyan; Shen, Xiaohong; Zhong, Xionghu

2015-01-01

Decision fusion for distributed detection in sensor networks under non-ideal channels is investigated in this paper. Usually, the local decisions are transmitted to the fusion center (FC) and decoded, and a fusion rule is then applied to achieve a global decision. We propose an optimal likelihood ratio test (LRT)-based fusion rule to take the uncertainty of the decoded binary data due to modulation, reception mode and communication channel into account. The average bit error rate (BER) is employed to characterize such an uncertainty. Further, the detection performance is analyzed under both non-identical and identical local detection performance indices. In addition, the performance of the proposed method is compared with the existing optimal and suboptimal LRT fusion rules. The results show that the proposed fusion rule is more robust compared to these existing ones. PMID:26251908

A Predictive Analysis of the Department of Defense Distribution System Utilizing Random Forests

DTIC Science & Technology

2016-06-01

resources capable of meeting both customer and individual resource constraints and goals while also maximizing the global benefit to the supply...and probability rules to determine the optimal red wine distribution network for an Italian-based wine producer. The decision support model for...combinations of factors that will result in delivery of the highest quality wines . The model’s first stage inputs basic logistics information to look

Intraoperative Clinical Decision Support for Anesthesia: A Narrative Review of Available Systems.

PubMed

Nair, Bala G; Gabel, Eilon; Hofer, Ira; Schwid, Howard A; Cannesson, Maxime

2017-02-01

With increasing adoption of anesthesia information management systems (AIMS), there is growing interest in utilizing AIMS data for intraoperative clinical decision support (CDS). CDS for anesthesia has the potential for improving quality of care, patient safety, billing, and compliance. Intraoperative CDS can range from passive and post hoc systems to active real-time systems that can detect ongoing clinical issues and deviations from best practice care. Real-time CDS holds the most promise because real-time alerts and guidance can drive provider behavior toward evidence-based standardized care during the ongoing case. In this review, we describe the different types of intraoperative CDS systems with specific emphasis on real-time systems. The technical considerations in developing and implementing real-time CDS are systematically covered. This includes the functional modules of a CDS system, development and execution of decision rules, and modalities to alert anesthesia providers concerning clinical issues. We also describe the regulatory aspects that affect development, implementation, and use of intraoperative CDS. Methods and measures to assess the effectiveness of intraoperative CDS are discussed. Last, we outline areas of future development of intraoperative CDS, particularly the possibility of providing predictive and prescriptive decision support.

Orthogonal search-based rule extraction for modelling the decision to transfuse.

PubMed

Etchells, T A; Harrison, M J

2006-04-01

Data from an audit relating to transfusion decisions during intermediate or major surgery were analysed to determine the strengths of certain factors in the decision making process. The analysis, using orthogonal search-based rule extraction (OSRE) from a trained neural network, demonstrated that the risk of tissue hypoxia (ROTH) assessed using a 100-mm visual analogue scale, the haemoglobin value (Hb) and the presence or absence of on-going haemorrhage (OGH) were able to reproduce the transfusion decisions with a joint specificity of 0.96 and sensitivity of 0.93 and a positive predictive value of 0.9. The rules indicating transfusion were: 1. ROTH > 32 mm and Hb < 94 g x l(-1); 2. ROTH > 13 mm and Hb < 87 g x l(-1); 3. ROTH > 38 mm, Hb < 102 g x l(-1) and OGH; 4. Hb < 78 g x l(-1).

Does the cost function matter in Bayes decision rule?

PubMed

Schlü ter, Ralf; Nussbaum-Thom, Markus; Ney, Hermann

2012-02-01

In many tasks in pattern recognition, such as automatic speech recognition (ASR), optical character recognition (OCR), part-of-speech (POS) tagging, and other string recognition tasks, we are faced with a well-known inconsistency: The Bayes decision rule is usually used to minimize string (symbol sequence) error, whereas, in practice, we want to minimize symbol (word, character, tag, etc.) error. When comparing different recognition systems, we do indeed use symbol error rate as an evaluation measure. The topic of this work is to analyze the relation between string (i.e., 0-1) and symbol error (i.e., metric, integer valued) cost functions in the Bayes decision rule, for which fundamental analytic results are derived. Simple conditions are derived for which the Bayes decision rule with integer-valued metric cost function and with 0-1 cost gives the same decisions or leads to classes with limited cost. The corresponding conditions can be tested with complexity linear in the number of classes. The results obtained do not make any assumption w.r.t. the structure of the underlying distributions or the classification problem. Nevertheless, the general analytic results are analyzed via simulations of string recognition problems with Levenshtein (edit) distance cost function. The results support earlier findings that considerable improvements are to be expected when initial error rates are high.

How accurate are interpretations of curriculum-based measurement progress monitoring data? Visual analysis versus decision rules.

PubMed

Van Norman, Ethan R; Christ, Theodore J

2016-10-01

Curriculum based measurement of oral reading (CBM-R) is used to monitor the effects of academic interventions for individual students. Decisions to continue, modify, or terminate these interventions are made by interpreting time series CBM-R data. Such interpretation is founded upon visual analysis or the application of decision rules. The purpose of this study was to compare the accuracy of visual analysis and decision rules. Visual analysts interpreted 108 CBM-R progress monitoring graphs one of three ways: (a) without graphic aids, (b) with a goal line, or (c) with a goal line and a trend line. Graphs differed along three dimensions, including trend magnitude, variability of observations, and duration of data collection. Automated trend line and data point decision rules were also applied to each graph. Inferential analyses permitted the estimation of the probability of a correct decision (i.e., the student is improving - continue the intervention, or the student is not improving - discontinue the intervention) for each evaluation method as a function of trend magnitude, variability of observations, and duration of data collection. All evaluation methods performed better when students made adequate progress. Visual analysis and decision rules performed similarly when observations were less variable. Results suggest that educators should collect data for more than six weeks, take steps to control measurement error, and visually analyze graphs when data are variable. Implications for practice and research are discussed. Copyright © 2016 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Towards the ecotourism: a decision support model for the assessment of sustainability of mountain huts in the Alps.

PubMed

Stubelj Ars, Mojca; Bohanec, Marko

2010-12-01

This paper studies mountain hut infrastructure in the Alps as an important element of ecotourism in the Alpine region. To improve the decision-making process regarding the implementation of future infrastructure and improvement of existing infrastructure in the vulnerable natural environment of mountain ecosystems, a new decision support model has been developed. The methodology is based on qualitative multi-attribute modelling supported by the DEXi software. The integrated rule-based model is hierarchical and consists of two submodels that cover the infrastructure of the mountain huts and that of the huts' surroundings. The final goal for the designed tool is to help minimize the ecological footprint of tourists in environmentally sensitive and undeveloped mountain areas and contribute to mountain ecotourism. The model has been tested in the case study of four mountain huts in Triglav National Park in Slovenia. Study findings provide a new empirical approach to evaluating existing mountain infrastructure and predicting improvements for the future. The assessment results are of particular interest for decision makers in protected areas, such as Alpine national parks managers and administrators. In a way, this model proposes an approach to the management assessment of mountain huts with the main aim of increasing the quality of life of mountain environment visitors as well as the satisfaction of tourists who may eventually become ecotourists. Copyright © 2010 Elsevier Ltd. All rights reserved.

Mixture-based gatekeeping procedures in adaptive clinical trials.

PubMed

Kordzakhia, George; Dmitrienko, Alex; Ishida, Eiji

2018-01-01

Clinical trials with data-driven decision rules often pursue multiple clinical objectives such as the evaluation of several endpoints or several doses of an experimental treatment. These complex analysis strategies give rise to "multivariate" multiplicity problems with several components or sources of multiplicity. A general framework for defining gatekeeping procedures in clinical trials with adaptive multistage designs is proposed in this paper. The mixture method is applied to build a gatekeeping procedure at each stage and inferences at each decision point (interim or final analysis) are performed using the combination function approach. An advantage of utilizing the mixture method is that it enables powerful gatekeeping procedures applicable to a broad class of settings with complex logical relationships among the hypotheses of interest. Further, the combination function approach supports flexible data-driven decisions such as a decision to increase the sample size or remove a treatment arm. The paper concludes with a clinical trial example that illustrates the methodology by applying it to develop an adaptive two-stage design with a mixture-based gatekeeping procedure.

A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue.

PubMed

Chen, Zhenyu; Li, Jianping; Wei, Liwei

2007-10-01

Recently, gene expression profiling using microarray techniques has been shown as a promising tool to improve the diagnosis and treatment of cancer. Gene expression data contain high level of noise and the overwhelming number of genes relative to the number of available samples. It brings out a great challenge for machine learning and statistic techniques. Support vector machine (SVM) has been successfully used to classify gene expression data of cancer tissue. In the medical field, it is crucial to deliver the user a transparent decision process. How to explain the computed solutions and present the extracted knowledge becomes a main obstacle for SVM. A multiple kernel support vector machine (MK-SVM) scheme, consisting of feature selection, rule extraction and prediction modeling is proposed to improve the explanation capacity of SVM. In this scheme, we show that the feature selection problem can be translated into an ordinary multiple parameters learning problem. And a shrinkage approach: 1-norm based linear programming is proposed to obtain the sparse parameters and the corresponding selected features. We propose a novel rule extraction approach using the information provided by the separating hyperplane and support vectors to improve the generalization capacity and comprehensibility of rules and reduce the computational complexity. Two public gene expression datasets: leukemia dataset and colon tumor dataset are used to demonstrate the performance of this approach. Using the small number of selected genes, MK-SVM achieves encouraging classification accuracy: more than 90% for both two datasets. Moreover, very simple rules with linguist labels are extracted. The rule sets have high diagnostic power because of their good classification performance.

Increasing Complexity in Rule-Based Clinical Decision Support: The Symptom Assessment and Management Intervention.

PubMed

Lobach, David F; Johns, Ellis B; Halpenny, Barbara; Saunders, Toni-Ann; Brzozowski, Jane; Del Fiol, Guilherme; Berry, Donna L; Braun, Ilana M; Finn, Kathleen; Wolfe, Joanne; Abrahm, Janet L; Cooley, Mary E

2016-11-08

Management of uncontrolled symptoms is an important component of quality cancer care. Clinical guidelines are available for optimal symptom management, but are not often integrated into the front lines of care. The use of clinical decision support (CDS) at the point-of-care is an innovative way to incorporate guideline-based symptom management into routine cancer care. The objective of this study was to develop and evaluate a rule-based CDS system to enable management of multiple symptoms in lung cancer patients at the point-of-care. This study was conducted in three phases involving a formative evaluation, a system evaluation, and a contextual evaluation of clinical use. In Phase 1, we conducted iterative usability testing of user interface prototypes with patients and health care providers (HCPs) in two thoracic oncology clinics. In Phase 2, we programmed complex algorithms derived from clinical practice guidelines into a rules engine that used Web services to communicate with the end-user application. Unit testing of algorithms was conducted using a stack-traversal tree-spanning methodology to identify all possible permutations of pathways through each algorithm, to validate accuracy. In Phase 3, we evaluated clinical use of the system among patients and HCPs in the two clinics via observations, structured interviews, and questionnaires. In Phase 1, 13 patients and 5 HCPs engaged in two rounds of formative testing, and suggested improvements leading to revisions until overall usability scores met a priori benchmarks. In Phase 2, symptom management algorithms contained between 29 and 1425 decision nodes, resulting in 19 to 3194 unique pathways per algorithm. Unit testing required 240 person-hours, and integration testing required 40 person-hours. In Phase 3, both patients and HCPs found the system usable and acceptable, and offered suggestions for improvements. A rule-based CDS system for complex symptom management was systematically developed and tested. The complexity of the algorithms required extensive development and innovative testing. The Web service-based approach allowed remote access to CDS knowledge, and could enable scaling and sharing of this knowledge to accelerate availability, and reduce duplication of effort. Patients and HCPs found the system to be usable and useful. ©David F Lobach, Ellis B Johns, Barbara Halpenny, Toni-Ann Saunders, Jane Brzozowski, Guilherme Del Fiol, Donna L Berry, Ilana M Braun, Kathleen Finn, Joanne Wolfe, Janet L Abrahm, Mary E Cooley. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 08.11.2016.

Automatic information extraction from unstructured mammography reports using distributed semantics.

PubMed

Gupta, Anupama; Banerjee, Imon; Rubin, Daniel L

2018-02-01

To date, the methods developed for automated extraction of information from radiology reports are mainly rule-based or dictionary-based, and, therefore, require substantial manual effort to build these systems. Recent efforts to develop automated systems for entity detection have been undertaken, but little work has been done to automatically extract relations and their associated named entities in narrative radiology reports that have comparable accuracy to rule-based methods. Our goal is to extract relations in a unsupervised way from radiology reports without specifying prior domain knowledge. We propose a hybrid approach for information extraction that combines dependency-based parse tree with distributed semantics for generating structured information frames about particular findings/abnormalities from the free-text mammography reports. The proposed IE system obtains a F 1 -score of 0.94 in terms of completeness of the content in the information frames, which outperforms a state-of-the-art rule-based system in this domain by a significant margin. The proposed system can be leveraged in a variety of applications, such as decision support and information retrieval, and may also easily scale to other radiology domains, since there is no need to tune the system with hand-crafted information extraction rules. Copyright © 2018 Elsevier Inc. All rights reserved.

Individual versus Household Migration Decision Rules: Gender and Marital Status Differences in Intentions to Migrate in South Africa

PubMed Central

Gubhaju, Bina; De Jong, Gordon F.

2009-01-01

This research tests the thesis that the neoclassical micro-economic and the new household economic theoretical assumptions on migration decision-making rules are segmented by gender, marital status, and time frame of intention to migrate. Comparative tests of both theories within the same study design are relatively rare. Utilizing data from the Causes of Migration in South Africa national migration survey, we analyze how individually held “own-future” versus alternative “household well-being” migration decision rules effect the intentions to migrate of male and female adults in South Africa. Results from the gender and marital status specific logistic regressions models show consistent support for the different gender-marital status decision rule thesis. Specifically, the “maximizing one’s own future” neoclassical microeconomic theory proposition is more applicable for never married men and women, the “maximizing household income” proposition for married men with short-term migration intentions, and the “reduce household risk” proposition for longer time horizon migration intentions of married men and women. Results provide new evidence on the way household strategies and individual goals jointly affect intentions to move or stay. PMID:20161187

Integrated approach using data mining-based decision tree and object-based image analysis for high-resolution urban mapping of WorldView-2 satellite sensor data

NASA Astrophysics Data System (ADS)

Hamedianfar, Alireza; Shafri, Helmi Zulhaidi Mohd

2016-04-01

This paper integrates decision tree-based data mining (DM) and object-based image analysis (OBIA) to provide a transferable model for the detailed characterization of urban land-cover classes using WorldView-2 (WV-2) satellite images. Many articles have been published on OBIA in recent years based on DM for different applications. However, less attention has been paid to the generation of a transferable model for characterizing detailed urban land cover features. Three subsets of WV-2 images were used in this paper to generate transferable OBIA rule-sets. Many features were explored by using a DM algorithm, which created the classification rules as a decision tree (DT) structure from the first study area. The developed DT algorithm was applied to object-based classifications in the first study area. After this process, we validated the capability and transferability of the classification rules into second and third subsets. Detailed ground truth samples were collected to assess the classification results. The first, second, and third study areas achieved 88%, 85%, and 85% overall accuracies, respectively. Results from the investigation indicate that DM was an efficient method to provide the optimal and transferable classification rules for OBIA, which accelerates the rule-sets creation stage in the OBIA classification domain.

Advancing reservoir operation description in physically based hydrological models

NASA Astrophysics Data System (ADS)

Anghileri, Daniela; Giudici, Federico; Castelletti, Andrea; Burlando, Paolo

2016-04-01

Last decades have seen significant advances in our capacity of characterizing and reproducing hydrological processes within physically based models. Yet, when the human component is considered (e.g. reservoirs, water distribution systems), the associated decisions are generally modeled with very simplistic rules, which might underperform in reproducing the actual operators' behaviour on a daily or sub-daily basis. For example, reservoir operations are usually described by a target-level rule curve, which represents the level that the reservoir should track during normal operating conditions. The associated release decision is determined by the current state of the reservoir relative to the rule curve. This modeling approach can reasonably reproduce the seasonal water volume shift due to reservoir operation. Still, it cannot capture more complex decision making processes in response, e.g., to the fluctuations of energy prices and demands, the temporal unavailability of power plants or varying amount of snow accumulated in the basin. In this work, we link a physically explicit hydrological model with detailed hydropower behavioural models describing the decision making process by the dam operator. In particular, we consider two categories of behavioural models: explicit or rule-based behavioural models, where reservoir operating rules are empirically inferred from observational data, and implicit or optimization based behavioural models, where, following a normative economic approach, the decision maker is represented as a rational agent maximising a utility function. We compare these two alternate modelling approaches on the real-world water system of Lake Como catchment in the Italian Alps. The water system is characterized by the presence of 18 artificial hydropower reservoirs generating almost 13% of the Italian hydropower production. Results show to which extent the hydrological regime in the catchment is affected by different behavioural models and reservoir operating strategies.

Predicting the need for CT imaging in children with minor head injury using an ensemble of Naive Bayes classifiers.

PubMed

Klement, William; Wilk, Szymon; Michalowski, Wojtek; Farion, Ken J; Osmond, Martin H; Verter, Vedat

2012-03-01

Using an automatic data-driven approach, this paper develops a prediction model that achieves more balanced performance (in terms of sensitivity and specificity) than the Canadian Assessment of Tomography for Childhood Head Injury (CATCH) rule, when predicting the need for computed tomography (CT) imaging of children after a minor head injury. CT is widely considered an effective tool for evaluating patients with minor head trauma who have potentially suffered serious intracranial injury. However, its use poses possible harmful effects, particularly for children, due to exposure to radiation. Safety concerns, along with issues of cost and practice variability, have led to calls for the development of effective methods to decide when CT imaging is needed. Clinical decision rules represent such methods and are normally derived from the analysis of large prospectively collected patient data sets. The CATCH rule was created by a group of Canadian pediatric emergency physicians to support the decision of referring children with minor head injury to CT imaging. The goal of the CATCH rule was to maximize the sensitivity of predictions of potential intracranial lesion while keeping specificity at a reasonable level. After extensive analysis of the CATCH data set, characterized by severe class imbalance, and after a thorough evaluation of several data mining methods, we derived an ensemble of multiple Naive Bayes classifiers as the prediction model for CT imaging decisions. In the first phase of the experiment we compared the proposed ensemble model to other ensemble models employing rule-, tree- and instance-based member classifiers. Our prediction model demonstrated the best performance in terms of AUC, G-mean and sensitivity measures. In the second phase, using a bootstrapping experiment similar to that reported by the CATCH investigators, we showed that the proposed ensemble model achieved a more balanced predictive performance than the CATCH rule with an average sensitivity of 82.8% and an average specificity of 74.4% (vs. 98.1% and 50.0% for the CATCH rule respectively). Automatically derived prediction models cannot replace a physician's acumen. However, they help establish reference performance indicators for the purpose of developing clinical decision rules so the trade-off between prediction sensitivity and specificity is better understood. Copyright © 2011 Elsevier B.V. All rights reserved.

Methods and decision making on a Mars rover for identification of fossils

NASA Technical Reports Server (NTRS)

Eberlein, Susan; Yates, Gigi

1989-01-01

A system for automated fusion and interpretation of image data from multiple sensors, including multispectral data from an imaging spectrometer is being developed. Classical artificial intelligence techniques and artificial neural networks are employed to make real time decision based on current input and known scientific goals. Emphasis is placed on identifying minerals which could indicate past life activity or an environment supportive of life. Multispectral data can be used for geological analysis because different minerals have characteristic spectral reflectance in the visible and near infrared range. Classification of each spectrum into a broad class, based on overall spectral shape and locations of absorption bands is possible in real time using artificial neural networks. The goal of the system is twofold: multisensor and multispectral data must be interpreted in real time so that potentially interesting sites can be flagged and investigated in more detail while the rover is near those sites; and the sensed data must be reduced to the most compact form possible without loss of crucial information. Autonomous decision making will allow a rover to achieve maximum scientific benefit from a mission. Both a classical rule based approach and a decision neural network for making real time choices are being considered. Neural nets may work well for adaptive decision making. A neural net can be trained to work in two steps. First, the actual input state is mapped to the closest of a number of memorized states. After weighing the importance of various input parameters, the net produces an output decision based on the matched memory state. Real time, autonomous image data analysis and decision making capabilities are required for achieving maximum scientific benefit from a rover mission. The system under development will enhance the chances of identifying fossils or environments capable of supporting life on Mars

Emergency physicians' knowledge and attitudes of clinical decision support in the electronic health record: a survey-based study.

PubMed

Ballard, Dustin W; Rauchwerger, Adina S; Reed, Mary E; Vinson, David R; Mark, Dustin G; Offerman, Steven R; Chettipally, Uli K; Graetz, Ilana; Dayan, Peter; Kuppermann, Nathan

2013-04-01

The objective was to investigate clinician knowledge of and attitudes toward clinical decision support (CDS) and its incorporation into the electronic health record (EHR). This was an electronic survey of emergency physicians (EPs) within an integrated health care delivery system that uses a complete EHR. Randomly assigned respondents completed one of two questionnaires, both including a hypothetical vignette and self-reported knowledge of and attitudes about CDS. One vignette version included CDS, and the other did not (NCDS). The vignette described a scenario in which a cranial computed tomography (CCT) is not recommended by validated prediction rules (the Pediatric Emergency Care Applied Research Network [PECARN] rules). In both survey versions, subjects responded first with their likely approach to evaluation and then again after receiving either CDS (the PECARN prediction rules) or no additional support. Descriptive statistics were used for self-reported responses and multivariate logistic regression was used to identify predictors of self-reported knowledge and use of the PECARN rules, as well as use of vignette responses. There were 339 respondents (68% response rate), with 172 of 339 (51%) randomized to the CDS version. Initially, 25% of respondents to each version indicated they would order CCTs. After CDS, 30 of 43 (70%) of respondents who initially would order CCTs changed their management decisions to no CCT versus two of 41 (5%) with the NCDS version (chi-square, p = 0.003). In response to self-report questions, 81 of 338 respondents (24%) reported having never heard of the PECARN prediction rules, 122 of 338 (36%) were aware of the rules but not their specifics, and 135 of 338 (40%) reported knowing the rules and their specifics. Respondents agreed with favorable statements about CDS (75% to 96% agreement across seven statements) and approaches to its implementation into the EHR (60% to 93% agreement across seven statements). In multivariable analyses, EPs with tenure of 5 to 14 years (odds ratio [AOR] = 0.51, 95% confidence interval [CI] = 0.30 to 0.86) and for 15 years or more (AOR = 0.37, 95% CI = 0.20 to 0.70) were significantly less likely to report knowing the specifics of the PECARN prediction rules compared with EPs who practiced for fewer than 5 years. In addition, in the initial vignette responses (across both versions), physicians with ≥15 years of ED tenure compared to those with fewer than 5 years of experience (AOR = 0.30, 95% CI = 0.13 to 0.69), and those reporting knowing the specifics of the PECARN prediction rules were less likely to order CCTs (AOR = 0.53, 95% CI = 0.30 to 0.92). EPs incorporated pediatric head trauma CDS via the EHR into their clinical judgment in a hypothetical scenario and reported favorable opinions of CDS in general and their inclusion into the EHR. © 2013 by the Society for Academic Emergency Medicine.

Proposed Clinical Decision Rules to Diagnose Acute Rhinosinusitis Among Adults in Primary Care.

PubMed

Ebell, Mark H; Hansen, Jens Georg

2017-07-01

To reduce inappropriate antibiotic prescribing, we sought to develop a clinical decision rule for the diagnosis of acute rhinosinusitis and acute bacterial rhinosinusitis. Multivariate analysis and classification and regression tree (CART) analysis were used to develop clinical decision rules for the diagnosis of acute rhinosinusitis, defined using 3 different reference standards (purulent antral puncture fluid or abnormal finding on a computed tomographic (CT) scan; for acute bacterial rhinosinusitis, we used a positive bacterial culture of antral fluid). Signs, symptoms, C-reactive protein (CRP), and reference standard tests were prospectively recorded in 175 Danish patients aged 18 to 65 years seeking care for suspected acute rhinosinusitis. For each reference standard, we developed 2 clinical decision rules: a point score based on a logistic regression model and an algorithm based on a CART model. We identified low-, moderate-, and high-risk groups for acute rhinosinusitis or acute bacterial rhinosinusitis for each clinical decision rule. The point scores each had between 5 and 6 predictors, and an area under the receiver operating characteristic curve (AUROCC) between 0.721 and 0.767. For positive bacterial culture as the reference standard, low-, moderate-, and high-risk groups had a 16%, 49%, and 73% likelihood of acute bacterial rhinosinusitis, respectively. CART models had an AUROCC ranging from 0.783 to 0.827. For positive bacterial culture as the reference standard, low-, moderate-, and high-risk groups had a likelihood of acute bacterial rhinosinusitis of 6%, 31%, and 59% respectively. We have developed a series of clinical decision rules integrating signs, symptoms, and CRP to diagnose acute rhinosinusitis and acute bacterial rhinosinusitis with good accuracy. They now require prospective validation and an assessment of their effect on clinical and process outcomes. © 2017 Annals of Family Medicine, Inc.

Systematic Analysis of the Decision Rules of Traditional Chinese Medicine

PubMed Central

Bin-Rong, Ma; Xi-Yuan, Jiang; Su-Ming, Liso; Huai-ning, Zhu; Xiu-ru, Lin

1981-01-01

Chinese traditional medicine has evolved over many centuries, and has accumulated a body of observed relationships between symptoms, signs and prognoses, and the efficacy of alternative treatments and prescriptions. With the assistance of a computer-based clinical data base for recording the diagnostic and therapeutic practice of skilled practitioners of Chinese traditional medicine, a systematic program is being conducted to identify and define the clinical decision-making rules that underlie current practice.

The impact of the Rasouli decision: a Survey of Canadian intensivists.

PubMed

Cape, David; Fox-Robichaud, Alison; Turgeon, Alexis F; Seely, Andrew; Hall, Richard; Burns, Karen; Singal, Rohit K; Dodek, Peter; Bagshaw, Sean; Sibbald, Robert; Downar, James

2016-03-01

In a landmark 2013 decision, the Supreme Court of Canada (SCC) ruled that the withdrawal of life support in certain circumstances is a treatment requiring patient or substitute decision maker (SDM) consent. How intensive care unit (ICU) physicians perceive this ruling is unknown. To determine physician knowledge of and attitudes towards the SCC decision, as well as the self-reported changes in practice attributed to the decision. We surveyed intensivists at university hospitals across Canada. We used a knowledge test and Likert-scale questions to measure respondent knowledge of and attitudes towards the ruling. We used vignettes to assess decision making in cases of intractable physician-SDM conflict over the management of patients with very poor prognoses. We compared management choices pre-SCC decision versus post-SCC decision versus the subjective, respondent-defined most appropriate choice. Responses were compared across predefined subgroups. We performed qualitative analysis on free-text responses. We received 82 responses (response rate=42%). Respondents reported providing high levels of self-defined inappropriate treatment. Although most respondents reported no change in practice, there was a significant overall shift towards higher intensity and less subjectively appropriate management after the SCC decision. Attitudes to the SCC decision and approaches to disputes over end-of-life (EoL) care in the ICU were highly variable. There were no significant differences among predefined subgroups. Many Canadian ICU physicians report providing a higher intensity of treatment, and less subjectively appropriate treatment, in situations of dispute over EoL care after the Supreme Court of Canada's ruling in Cuthbertson versus Rasouli. 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/

Simply criminal: predicting burglars' occupancy decisions with a simple heuristic.

PubMed

Snook, Brent; Dhami, Mandeep K; Kavanagh, Jennifer M

2011-08-01

Rational choice theories of criminal decision making assume that offenders weight and integrate multiple cues when making decisions (i.e., are compensatory). We tested this assumption by comparing how well a compensatory strategy called Franklin's Rule captured burglars' decision policies regarding residence occupancy compared to a non-compensatory strategy (i.e., Matching Heuristic). Forty burglars each decided on the occupancy of 20 randomly selected photographs of residences (for which actual occupancy was known when the photo was taken). Participants also provided open-ended reports on the cues that influenced their decisions in each case, and then rated the importance of eight cues (e.g., deadbolt visible) over all decisions. Burglars predicted occupancy beyond chance levels. The Matching Heuristic was a significantly better predictor of burglars' decisions than Franklin's Rule, and cue use in the Matching Heuristic better corresponded to the cue ecological validities in the environment than cue use in Franklin's Rule. The most important cue in burglars' models was also the most ecologically valid or predictive of actual occupancy (i.e., vehicle present). The majority of burglars correctly identified the most important cue in their models, and the open-ended technique showed greater correspondence between self-reported and captured cue use than the rating over decision technique. Our findings support a limited rationality perspective to understanding criminal decision making, and have implications for crime prevention.

The Relative Success of Recognition-Based Inference in Multichoice Decisions

ERIC Educational Resources Information Center

McCloy, Rachel; Beaman, C. Philip; Smith, Philip T.

2008-01-01

The utility of an "ecologically rational" recognition-based decision rule in multichoice decision problems is analyzed, varying the type of judgment required (greater or lesser). The maximum size and range of a counterintuitive advantage associated with recognition-based judgment (the "less-is-more effect") is identified for a range of cue…

Description and status update on GELLO: a proposed standardized object-oriented expression language for clinical decision support.

PubMed

Sordo, Margarita; Boxwala, Aziz A; Ogunyemi, Omolola; Greenes, Robert A

2004-01-01

A major obstacle to sharing computable clinical knowledge is the lack of a common language for specifying expressions and criteria. Such a language could be used to specify decision criteria, formulae, and constraints on data and action. Al-though the Arden Syntax addresses this problem for clinical rules, its generalization to HL7's object-oriented data model is limited. The GELLO Expression language is an object-oriented language used for expressing logical conditions and computations in the GLIF3 (GuideLine Interchange Format, v. 3) guideline modeling language. It has been further developed under the auspices of the HL7 Clinical Decision Support Technical Committee, as a proposed HL7 standard., GELLO is based on the Object Constraint Language (OCL), because it is vendor-independent, object-oriented, and side-effect-free. GELLO expects an object-oriented data model. Although choice of model is arbitrary, standardization is facilitated by ensuring that the data model is compatible with the HL7 Reference Information Model (RIM).

The Effect of a Supreme Court Decision Regarding Gay Marriage on Social Norms and Personal Attitudes.

PubMed

Tankard, Margaret E; Paluck, Elizabeth Levy

2017-09-01

We propose that institutions such as the U.S. Supreme Court can lead individuals to update their perceptions of social norms, in contrast to the mixed evidence on whether institutions shape individuals' personal opinions. We studied reactions to the June 2015 U.S. Supreme Court ruling in favor of same-sex marriage. In a controlled experimental setting, we found that a favorable ruling, when presented as likely, shifted perceived norms and personal attitudes toward increased support for gay marriage and gay people. Next, a five-wave longitudinal time-series study using a sample of 1,063 people found an increase in perceived social norms supporting gay marriage after the ruling but no change in personal attitudes. This pattern was replicated in a separate between-subjects data set. These findings provide the first experimental evidence that an institutional decision can change perceptions of social norms, which have been shown to guide behavior, even when individual opinions are unchanged.

Calyx{trademark} EA implementation at AECB

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

NONE

1997-12-31

This report describes a project to examine the applicability of a knowledge-based decision support software for environmental assessment (Calyx) to assist the Atomic Energy Control Board in environmental screenings, assessment, management, and database searches. The report begins with background on the Calyx software and then reviews activities with regard to modification of the Calyx knowledge base for application to the nuclear sector. This is followed by lists of standard activities handled by the software and activities specific to the Board; the hierarchy of environmental components developed for the Board; details of impact rules that describe the conditions under which environmentalmore » impacts will occur (the bulk of the report); information on mitigation and monitoring rules and on instance data; and considerations for future work on implementing Calyx at the Board. Appendices include an introduction to expert systems and an overview of the Calyx knowledge base structure.« less

Prediction of Disease Case Severity Level To Determine INA CBGs Rate

NASA Astrophysics Data System (ADS)

Puspitorini, Sukma; Kusumadewi, Sri; Rosita, Linda

2017-03-01

Indonesian Case-Based Groups (INA CBGs) is case-mix payment system using software grouper application. INA CBGs consisting of four digits code where the last digits indicating the severity level of disease cases. Severity level influence by secondary diagnosis (complications and co-morbidity) related to resource intensity level. It is medical resources used to treat a hospitalized patient. Objectives of this research is developing decision support system to predict severity level of disease cases and illustrate INA CBGs rate by using data mining decision tree classification model. Primary diagnosis (DU), first secondary diagnosis (DS 1), and second secondary diagnosis (DS 2) are attributes that used as input of severity level. The training process using C4.5 algorithm and the rules will represent in the IF-THEN form. Credibility of the system analyzed through testing process and confusion matrix present the results. Outcome of this research shows that first secondary diagnosis influence significant to form severity level predicting rules from new disease cases and INA CBGs rate illustration.

Assessing the structure of non-routine decision processes in Airline Operations Control.

PubMed

Richters, Floor; Schraagen, Jan Maarten; Heerkens, Hans

2016-03-01

Unfamiliar severe disruptions challenge Airline Operations Control professionals most, as their expertise is stretched to its limits. This study has elicited the structure of Airline Operations Control professionals' decision process during unfamiliar disruptions by mapping three macrocognitive activities on the decision ladder: sensemaking, option evaluation and action planning. The relationship between this structure and decision quality was measured. A simulated task was staged, based on which think-aloud protocols were obtained. Results show that the general decision process structure resembles the structure of experts working under routine conditions, in terms of the general structure of the macrocognitive activities, and the rule-based approach used to identify options and actions. Surprisingly, high quality of decision outcomes was found to relate to the use of rule-based strategies. This implies that successful professionals are capable of dealing with unfamiliar problems by reframing them into familiar ones, rather than to engage in knowledge-based processing. Practitioner Summary: We examined the macrocognitive structure of Airline Operations Control professionals' decision process during a simulated unfamiliar disruption in relation to decision quality. Results suggest that successful professionals are capable of dealing with unfamiliar problems by reframing them into familiar ones, rather than to engage in knowledge-based processing.

Determining rules for closing customer service centers: A public utility company's fuzzy decision

NASA Technical Reports Server (NTRS)

Dekorvin, Andre; Shipley, Margaret F.; Lea, Robert N.

1992-01-01

In the present work, we consider the general problem of knowledge acquisition under uncertainty. Simply stated, the problem reduces to the following: how can we capture the knowledge of an expert when the expert is unable to clearly formulate how he or she arrives at a decision? A commonly used method is to learn by examples. We observe how the expert solves specific cases and from this infer some rules by which the decision may have been made. Unique to our work is the fuzzy set representation of the conditions or attributes upon which the expert may possibly base his fuzzy decision. From our examples, we infer certain and possible fuzzy rules for closing a customer service center and illustrate the importance of having the decision closely relate to the conditions under consideration.

HERB: A production system for programming with hierarchical expert rule bases: User's manual, HERB Version 1. 0

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

Hummel, K.E.

1987-12-01

Expert systems are artificial intelligence programs that solve problems requiring large amounts of heuristic knowledge, based on years of experience and tradition. Production systems are domain-independent tools that support the development of rule-based expert systems. This document describes a general purpose production system known as HERB. This system was developed to support the programming of expert systems using hierarchically structured rule bases. HERB encourages the partitioning of rules into multiple rule bases and supports the use of multiple conflict resolution strategies. Multiple rule bases can also be placed on a system stack and simultaneously searched during each interpreter cycle. Bothmore » backward and forward chaining rules are supported by HERB. The condition portion of each rule can contain both patterns, which are matched with facts in a data base, and LISP expressions, which are explicitly evaluated in the LISP environment. Properties of objects can also be stored in the HERB data base and referenced within the scope of each rule. This document serves both as an introduction to the principles of LISP-based production systems and as a user's manual for the HERB system. 6 refs., 17 figs.« less

50 CFR 17.84 - Special rules-vertebrates.

Code of Federal Regulations, 2011 CFR

2011-10-01

... too steep to support populations of woundfin. (7) The reintroduced populations will be checked... population grows from the point of being established toward the maximum number that its habitat can support... decision to terminate the translocated population. A joint State-Service consultation will determine when...

For an indeterministic ethics. The emptiness of the rule in dubio pro vita and life cessation decisions

PubMed Central

Pavlovic, Dragan; Lehmann, Christian; Wendt, Michael

2009-01-01

It is generally claimed that there exist exceptional circumstances when taking human life may be approved and when such actions may be justified on moral grounds. Precise guidelines in the medical field for making such decisions concerning patients who are terminally ill or have irreparable injuries incompatible with a bearable life, are difficult to establish. Recommendations that take the particular logical form of a rule, such as "in dubio pro vita", "when in doubt favour life") have been suggested and in some countries incorporated into legal texts (Germany). We claim here that such a rule is of no value since it is open-ended and always allows for doubt, and a decision to employ measures that would support human life could always be argued to be a valid choice. Preservation of this rule could be encouraged, but giving it the force of law may put physicians at risk, as they may be challenged for choosing to terminate life in otherwise ethically and medically uncontroversial circumstances. PMID:19442284

Software tool for data mining and its applications

NASA Astrophysics Data System (ADS)

Yang, Jie; Ye, Chenzhou; Chen, Nianyi

2002-03-01

A software tool for data mining is introduced, which integrates pattern recognition (PCA, Fisher, clustering, hyperenvelop, regression), artificial intelligence (knowledge representation, decision trees), statistical learning (rough set, support vector machine), computational intelligence (neural network, genetic algorithm, fuzzy systems). It consists of nine function models: pattern recognition, decision trees, association rule, fuzzy rule, neural network, genetic algorithm, Hyper Envelop, support vector machine, visualization. The principle and knowledge representation of some function models of data mining are described. The software tool of data mining is realized by Visual C++ under Windows 2000. Nonmonotony in data mining is dealt with by concept hierarchy and layered mining. The software tool of data mining has satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma.

Decision-Making in Agent-Based Models of Migration: State of the Art and Challenges.

PubMed

Klabunde, Anna; Willekens, Frans

We review agent-based models (ABM) of human migration with respect to their decision-making rules. The most prominent behavioural theories used as decision rules are the random utility theory, as implemented in the discrete choice model, and the theory of planned behaviour. We identify the critical choices that must be made in developing an ABM, namely the modelling of decision processes and social networks. We also discuss two challenges that hamper the widespread use of ABM in the study of migration and, more broadly, demography and the social sciences: (a) the choice and the operationalisation of a behavioural theory (decision-making and social interaction) and (b) the selection of empirical evidence to validate the model. We offer advice on how these challenges might be overcome.

Collective intelligence meets medical decision-making: the collective outperforms the best radiologist.

PubMed

Wolf, Max; Krause, Jens; Carney, Patricia A; Bogart, Andy; Kurvers, Ralf H J M

2015-01-01

While collective intelligence (CI) is a powerful approach to increase decision accuracy, few attempts have been made to unlock its potential in medical decision-making. Here we investigated the performance of three well-known collective intelligence rules ("majority", "quorum", and "weighted quorum") when applied to mammography screening. For any particular mammogram, these rules aggregate the independent assessments of multiple radiologists into a single decision (recall the patient for additional workup or not). We found that, compared to single radiologists, any of these CI-rules both increases true positives (i.e., recalls of patients with cancer) and decreases false positives (i.e., recalls of patients without cancer), thereby overcoming one of the fundamental limitations to decision accuracy that individual radiologists face. Importantly, we find that all CI-rules systematically outperform even the best-performing individual radiologist in the respective group. Our findings demonstrate that CI can be employed to improve mammography screening; similarly, CI may have the potential to improve medical decision-making in a much wider range of contexts, including many areas of diagnostic imaging and, more generally, diagnostic decisions that are based on the subjective interpretation of evidence.

Integrated modelling of stormwater treatment systems uptake.

PubMed

Castonguay, A C; Iftekhar, M S; Urich, C; Bach, P M; Deletic, A

2018-05-24

Nature-based solutions provide a variety of benefits in growing cities, ranging from stormwater treatment to amenity provision such as aesthetics. However, the decision-making process involved in the installation of such green infrastructure is not straightforward, as much uncertainty around the location, size, costs and benefits impedes systematic decision-making. We developed a model to simulate decision rules used by local municipalities to install nature-based stormwater treatment systems, namely constructed wetlands, ponds/basins and raingardens. The model was used to test twenty-four scenarios of policy-making, by combining four asset selection, two location selection and three budget constraint decision rules. Based on the case study of a local municipality in Metropolitan Melbourne, Australia, the modelled uptake of stormwater treatment systems was compared with attributes of real-world systems for the simulation period. Results show that the actual budgeted funding is not reliable to predict systems' uptake and that policy-makers are more likely to plan expenditures based on installation costs. The model was able to replicate the cumulative treatment capacity and the location of systems. As such, it offers a novel approach to investigate the impact of using different decision rules to provide environmental services considering biophysical and economic factors. Copyright © 2018 Elsevier Ltd. All rights reserved.

Counseling Model Application: A Student Career Development Guidance for Decision Maker and Consultation

NASA Astrophysics Data System (ADS)

Irwan; Gustientiedina; Sunarti; Desnelita, Yenny

2017-12-01

The purpose of this study is to design a counseling model application for a decision-maker and consultation system. This application as an alternative guidance and individual career development for students, that include career knowledge, planning and alternative options from an expert tool based on knowledge and rule to provide the solutions on student’s career decisions. This research produces a counseling model application to obtain the important information about student career development and facilitating individual student’s development through the service form, to connect their plan with their career according to their talent, interest, ability, knowledge, personality and other supporting factors. This application model can be used as tool to get information faster and flexible for the student’s guidance and counseling. So, it can help students in doing selection and making decision that appropriate with their choice of works.

Developing an International Register of Clinical Prediction Rules for Use in Primary Care: A Descriptive Analysis

PubMed Central

Keogh, Claire; Wallace, Emma; O’Brien, Kirsty K.; Galvin, Rose; Smith, Susan M.; Lewis, Cliona; Cummins, Anthony; Cousins, Grainne; Dimitrov, Borislav D.; Fahey, Tom

2014-01-01

PURPOSE We describe the methodology used to create a register of clinical prediction rules relevant to primary care. We also summarize the rules included in the register according to various characteristics. METHODS To identify relevant articles, we searched the MEDLINE database (PubMed) for the years 1980 to 2009 and supplemented the results with searches of secondary sources (books on clinical prediction rules) and personal resources (eg, experts in the field). The rules described in relevant articles were classified according to their clinical domain, the stage of development, and the clinical setting in which they were studied. RESULTS Our search identified clinical prediction rules reported between 1965 and 2009. The largest share of rules (37.2%) were retrieved from PubMed. The number of published rules increased substantially over the study decades. We included 745 articles in the register; many contained more than 1 clinical prediction rule study (eg, both a derivation study and a validation study), resulting in 989 individual studies. In all, 434 unique rules had gone through derivation; however, only 54.8% had been validated and merely 2.8% had undergone analysis of their impact on either the process or outcome of clinical care. The rules most commonly pertained to cardiovascular disease, respiratory, and musculoskeletal conditions. They had most often been studied in the primary care or emergency department settings. CONCLUSIONS Many clinical prediction rules have been derived, but only about half have been validated and few have been assessed for clinical impact. This lack of thorough evaluation for many rules makes it difficult to retrieve and identify those that are ready for use at the point of patient care. We plan to develop an international web-based register of clinical prediction rules and computer-based clinical decision support systems. PMID:25024245

A decision-analytic approach to the optimal allocation of resources for endangered species consultation

USGS Publications Warehouse

Converse, Sarah J.; Shelley, Kevin J.; Morey, Steve; Chan, Jeffrey; LaTier, Andrea; Scafidi, Carolyn; Crouse, Deborah T.; Runge, Michael C.

2011-01-01

The resources available to support conservation work, whether time or money, are limited. Decision makers need methods to help them identify the optimal allocation of limited resources to meet conservation goals, and decision analysis is uniquely suited to assist with the development of such methods. In recent years, a number of case studies have been described that examine optimal conservation decisions under fiscal constraints; here we develop methods to look at other types of constraints, including limited staff and regulatory deadlines. In the US, Section Seven consultation, an important component of protection under the federal Endangered Species Act, requires that federal agencies overseeing projects consult with federal biologists to avoid jeopardizing species. A benefit of consultation is negotiation of project modifications that lessen impacts on species, so staff time allocated to consultation supports conservation. However, some offices have experienced declining staff, potentially reducing the efficacy of consultation. This is true of the US Fish and Wildlife Service's Washington Fish and Wildlife Office (WFWO) and its consultation work on federally-threatened bull trout (Salvelinus confluentus). To improve effectiveness, WFWO managers needed a tool to help allocate this work to maximize conservation benefits. We used a decision-analytic approach to score projects based on the value of staff time investment, and then identified an optimal decision rule for how scored projects would be allocated across bins, where projects in different bins received different time investments. We found that, given current staff, the optimal decision rule placed 80% of informal consultations (those where expected effects are beneficial, insignificant, or discountable) in a short bin where they would be completed without negotiating changes. The remaining 20% would be placed in a long bin, warranting an investment of seven days, including time for negotiation. For formal consultations (those where expected effects are significant), 82% of projects would be placed in a long bin, with an average time investment of 15. days. The WFWO is using this decision-support tool to help allocate staff time. Because workload allocation decisions are iterative, we describe a monitoring plan designed to increase the tool's efficacy over time. This work has general application beyond Section Seven consultation, in that it provides a framework for efficient investment of staff time in conservation when such time is limited and when regulatory deadlines prevent an unconstrained approach. ?? 2010.

Method to integrate clinical guidelines into the electronic health record (EHR) by applying the archetypes approach.

PubMed

Garcia, Diego; Moro, Claudia Maria Cabral; Cicogna, Paulo Eduardo; Carvalho, Deborah Ribeiro

2013-01-01

Clinical guidelines are documents that assist healthcare professionals, facilitating and standardizing diagnosis, management, and treatment in specific areas. Computerized guidelines as decision support systems (DSS) attempt to increase the performance of tasks and facilitate the use of guidelines. Most DSS are not integrated into the electronic health record (EHR), ordering some degree of rework especially related to data collection. This study's objective was to present a method for integrating clinical guidelines into the EHR. The study developed first a way to identify data and rules contained in the guidelines, and then incorporate rules into an archetype-based EHR. The proposed method tested was anemia treatment in the Chronic Kidney Disease Guideline. The phases of the method are: data and rules identification; archetypes elaboration; rules definition and inclusion in inference engine; and DSS-EHR integration and validation. The main feature of the proposed method is that it is generic and can be applied toany type of guideline.

[Clinical economics: a concept to optimize healthcare services].

PubMed

Porzsolt, F; Bauer, K; Henne-Bruns, D

2012-03-01

Clinical economics strives to support healthcare decisions by economic considerations. Making economic decisions does not mean saving costs but rather comparing the gained added value with the burden which has to be accepted. The necessary rules are offered in various disciplines, such as economy, epidemiology and ethics. Medical doctors have recognized these rules but are not applying them in daily clinical practice. This lacking orientation leads to preventable errors. Examples of these errors are shown for diagnosis, screening, prognosis and therapy. As these errors can be prevented by application of clinical economic principles the possible consequences for optimization of healthcare are discussed.

Boosting standard order sets utilization through clinical decision support.

PubMed

Li, Haomin; Zhang, Yinsheng; Cheng, Haixia; Lu, Xudong; Duan, Huilong

2013-01-01

Well-designed standard order sets have the potential to integrate and coordinate care by communicating best practices through multiple disciplines, levels of care, and services. However, there are several challenges which certainly affected the benefits expected from standard order sets. To boost standard order sets utilization, a problem-oriented knowledge delivery solution was proposed in this study to facilitate access of standard order sets and evaluation of its treatment effect. In this solution, standard order sets were created along with diagnostic rule sets which can trigger a CDS-based reminder to help clinician quickly discovery hidden clinical problems and corresponding standard order sets during ordering. Those rule set also provide indicators for targeted evaluation of standard order sets during treatment. A prototype system was developed based on this solution and will be presented at Medinfo 2013.

Making sense of information in noisy networks: human communication, gossip, and distortion.

PubMed

Laidre, Mark E; Lamb, Alex; Shultz, Susanne; Olsen, Megan

2013-01-21

Information from others can be unreliable. Humans nevertheless act on such information, including gossip, to make various social calculations, thus raising the question of whether individuals can sort through social information to identify what is, in fact, true. Inspired by empirical literature on people's decision-making when considering gossip, we built an agent-based simulation model to examine how well simple decision rules could make sense of information as it propagated through a network. Our simulations revealed that a minimalistic decision-rule 'Bit-wise mode' - which compared information from multiple sources and then sought a consensus majority for each component bit within the message - was consistently the most successful at converging upon the truth. This decision rule attained high relative fitness even in maximally noisy networks, composed entirely of nodes that distorted the message. The rule was also superior to other decision rules regardless of its frequency in the population. Simulations carried out with variable agent memory constraints, different numbers of observers who initiated information propagation, and a variety of network types suggested that the single most important factor in making sense of information was the number of independent sources that agents could consult. Broadly, our model suggests that despite the distortion information is subject to in the real world, it is nevertheless possible to make sense of it based on simple Darwinian computations that integrate multiple sources. Copyright © 2012 Elsevier Ltd. All rights reserved.

Clinical Decision Support Tools for Selecting Interventions for Patients with Disabling Musculoskeletal Disorders: A Scoping Review.

PubMed

Gross, Douglas P; Armijo-Olivo, Susan; Shaw, William S; Williams-Whitt, Kelly; Shaw, Nicola T; Hartvigsen, Jan; Qin, Ziling; Ha, Christine; Woodhouse, Linda J; Steenstra, Ivan A

2016-09-01

Purpose We aimed to identify and inventory clinical decision support (CDS) tools for helping front-line staff select interventions for patients with musculoskeletal (MSK) disorders. Methods We used Arksey and O'Malley's scoping review framework which progresses through five stages: (1) identifying the research question; (2) identifying relevant studies; (3) selecting studies for analysis; (4) charting the data; and (5) collating, summarizing and reporting results. We considered computer-based, and other available tools, such as algorithms, care pathways, rules and models. Since this research crosses multiple disciplines, we searched health care, computing science and business databases. Results Our search resulted in 4605 manuscripts. Titles and abstracts were screened for relevance. The reliability of the screening process was high with an average percentage of agreement of 92.3 %. Of the located articles, 123 were considered relevant. Within this literature, there were 43 CDS tools located. These were classified into 3 main areas: computer-based tools/questionnaires (n = 8, 19 %), treatment algorithms/models (n = 14, 33 %), and clinical prediction rules/classification systems (n = 21, 49 %). Each of these areas and the associated evidence are described. The state of evidentiary support for CDS tools is still preliminary and lacks external validation, head-to-head comparisons, or evidence of generalizability across different populations and settings. Conclusions CDS tools, especially those employing rapidly advancing computer technologies, are under development and of potential interest to health care providers, case management organizations and funders of care. Based on the results of this scoping review, we conclude that these tools, models and systems should be subjected to further validation before they can be recommended for large-scale implementation for managing patients with MSK disorders.

Decision support from local data: creating adaptive order menus from past clinician behavior.

PubMed

Klann, Jeffrey G; Szolovits, Peter; Downs, Stephen M; Schadow, Gunther

2014-04-01

Reducing care variability through guidelines has significantly benefited patients. Nonetheless, guideline-based Clinical Decision Support (CDS) systems are not widely implemented or used, are frequently out-of-date, and cannot address complex care for which guidelines do not exist. Here, we develop and evaluate a complementary approach - using Bayesian Network (BN) learning to generate adaptive, context-specific treatment menus based on local order-entry data. These menus can be used as a draft for expert review, in order to minimize development time for local decision support content. This is in keeping with the vision outlined in the US Health Information Technology Strategic Plan, which describes a healthcare system that learns from itself. We used the Greedy Equivalence Search algorithm to learn four 50-node domain-specific BNs from 11,344 encounters: abdominal pain in the emergency department, inpatient pregnancy, hypertension in the Urgent Visit Clinic, and altered mental state in the intensive care unit. We developed a system to produce situation-specific, rank-ordered treatment menus from these networks. We evaluated this system with a hospital-simulation methodology and computed Area Under the Receiver-Operator Curve (AUC) and average menu position at time of selection. We also compared this system with a similar association-rule-mining approach. A short order menu on average contained the next order (weighted average length 3.91-5.83 items). Overall predictive ability was good: average AUC above 0.9 for 25% of order types and overall average AUC .714-.844 (depending on domain). However, AUC had high variance (.50-.99). Higher AUC correlated with tighter clusters and more connections in the graphs, indicating importance of appropriate contextual data. Comparison with an Association Rule Mining approach showed similar performance for only the most common orders with dramatic divergence as orders are less frequent. This study demonstrates that local clinical knowledge can be extracted from treatment data for decision support. This approach is appealing because: it reflects local standards; it uses data already being captured; and it produces human-readable treatment-diagnosis networks that could be curated by a human expert to reduce workload in developing localized CDS content. The BN methodology captured transitive associations and co-varying relationships, which existing approaches do not. It also performs better as orders become less frequent and require more context. This system is a step forward in harnessing local, empirical data to enhance decision support. Copyright © 2013 Elsevier Inc. All rights reserved.

Decision Support from Local Data: Creating Adaptive Order Menus from Past Clinician Behavior

PubMed Central

Klann, Jeffrey G.; Szolovits, Peter; Downs, Stephen; Schadow, Gunther

2014-01-01

Objective Reducing care variability through guidelines has significantly benefited patients. Nonetheless, guideline-based clinical decision support (CDS) systems are not widely implemented or used, are frequently out-of-date, and cannot address complex care for which guidelines do not exist. Here, we develop and evaluate a complementary approach - using Bayesian network (BN) learning to generate adaptive, context-specific treatment menus based on local order-entry data. These menus can be used as a draft for expert review, in order to minimize development time for local decision support content. This is in keeping with the vision outlined in the US Health Information Technology Strategic Plan, which describes a healthcare system that learns from itself. Materials and Methods We used the Greedy Equivalence Search algorithm to learn four 50-node domain-specific BNs from 11,344 encounters: abdominal pain in the emergency department, inpatient pregnancy, hypertension in the urgent visit clinic, and altered mental state in the intensive care unit. We developed a system to produce situation-specific, rank-ordered treatment menus from these networks. We evaluated this system with a hospital-simulation methodology and computed Area Under the Receiver-Operator Curve (AUC) and average menu position at time of selection. We also compared this system with a similar association-rule-mining approach. Results A short order menu on average contained the next order (weighted average length 3.91–5.83 items). Overall predictive ability was good: average AUC above 0.9 for 25% of order types and overall average AUC .714–.844 (depending on domain). However, AUC had high variance (.50–.99). Higher AUC correlated with tighter clusters and more connections in the graphs, indicating importance of appropriate contextual data. Comparison with an association rule mining approach showed similar performance for only the most common orders with dramatic divergence as orders are less frequent. Discussion and Conclusion This study demonstrates that local clinical knowledge can be extracted from treatment data for decision support. This approach is appealing because: it reflects local standards; it uses data already being captured; and it produces human-readable treatment-diagnosis networks that could be curated by a human expert to reduce workload in developing localized CDS content. The BN methodology captured transitive associations and co-varying relationships, which existing approaches do not. It also performs better as orders become less frequent and require more context. This system is a step forward in harnessing local, empirical data to enhance decision support. PMID:24355978

Comparison of rule induction, decision trees and formal concept analysis approaches for classification

NASA Astrophysics Data System (ADS)

Kotelnikov, E. V.; Milov, V. R.

2018-05-01

Rule-based learning algorithms have higher transparency and easiness to interpret in comparison with neural networks and deep learning algorithms. These properties make it possible to effectively use such algorithms to solve descriptive tasks of data mining. The choice of an algorithm depends also on its ability to solve predictive tasks. The article compares the quality of the solution of the problems with binary and multiclass classification based on the experiments with six datasets from the UCI Machine Learning Repository. The authors investigate three algorithms: Ripper (rule induction), C4.5 (decision trees), In-Close (formal concept analysis). The results of the experiments show that In-Close demonstrates the best quality of classification in comparison with Ripper and C4.5, however the latter two generate more compact rule sets.

A Fuzzy-Decision Based Approach for Composite Event Detection in Wireless Sensor Networks

PubMed Central

Zhang, Shukui; Chen, Hao; Zhu, Qiaoming

2014-01-01

The event detection is one of the fundamental researches in wireless sensor networks (WSNs). Due to the consideration of various properties that reflect events status, the Composite event is more consistent with the objective world. Thus, the research of the Composite event becomes more realistic. In this paper, we analyze the characteristics of the Composite event; then we propose a criterion to determine the area of the Composite event and put forward a dominating set based network topology construction algorithm under random deployment. For the unreliability of partial data in detection process and fuzziness of the event definitions in nature, we propose a cluster-based two-dimensional τ-GAS algorithm and fuzzy-decision based composite event decision mechanism. In the case that the sensory data of most nodes are normal, the two-dimensional τ-GAS algorithm can filter the fault node data effectively and reduce the influence of erroneous data on the event determination. The Composite event judgment mechanism which is based on fuzzy-decision holds the superiority of the fuzzy-logic based algorithm; moreover, it does not need the support of a huge rule base and its computational complexity is small. Compared to CollECT algorithm and CDS algorithm, this algorithm improves the detection accuracy and reduces the traffic. PMID:25136690

Failure detection system design methodology. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Chow, E. Y.

1980-01-01

The design of a failure detection and identification system consists of designing a robust residual generation process and a high performance decision making process. The design of these two processes are examined separately. Residual generation is based on analytical redundancy. Redundancy relations that are insensitive to modelling errors and noise effects are important for designing robust residual generation processes. The characterization of the concept of analytical redundancy in terms of a generalized parity space provides a framework in which a systematic approach to the determination of robust redundancy relations are developed. The Bayesian approach is adopted for the design of high performance decision processes. The FDI decision problem is formulated as a Bayes sequential decision problem. Since the optimal decision rule is incomputable, a methodology for designing suboptimal rules is proposed. A numerical algorithm is developed to facilitate the design and performance evaluation of suboptimal rules.

Textural features for image classification

NASA Technical Reports Server (NTRS)

Haralick, R. M.; Dinstein, I.; Shanmugam, K.

1973-01-01

Description of some easily computable textural features based on gray-tone spatial dependances, and illustration of their application in category-identification tasks of three different kinds of image data - namely, photomicrographs of five kinds of sandstones, 1:20,000 panchromatic aerial photographs of eight land-use categories, and ERTS multispectral imagery containing several land-use categories. Two kinds of decision rules are used - one for which the decision regions are convex polyhedra (a piecewise-linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89% for the photomicrographs, 82% for the aerial photographic imagery, and 83% for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.

Use of Attribute Driven Incremental Discretization and Logic Learning Machine to build a prognostic classifier for neuroblastoma patients.

PubMed

Cangelosi, Davide; Muselli, Marco; Parodi, Stefano; Blengio, Fabiola; Becherini, Pamela; Versteeg, Rogier; Conte, Massimo; Varesio, Luigi

2014-01-01

Cancer patient's outcome is written, in part, in the gene expression profile of the tumor. We previously identified a 62-probe sets signature (NB-hypo) to identify tissue hypoxia in neuroblastoma tumors and showed that NB-hypo stratified neuroblastoma patients in good and poor outcome 1. It was important to develop a prognostic classifier to cluster patients into risk groups benefiting of defined therapeutic approaches. Novel classification and data discretization approaches can be instrumental for the generation of accurate predictors and robust tools for clinical decision support. We explored the application to gene expression data of Rulex, a novel software suite including the Attribute Driven Incremental Discretization technique for transforming continuous variables into simplified discrete ones and the Logic Learning Machine model for intelligible rule generation. We applied Rulex components to the problem of predicting the outcome of neuroblastoma patients on the bases of 62 probe sets NB-hypo gene expression signature. The resulting classifier consisted in 9 rules utilizing mainly two conditions of the relative expression of 11 probe sets. These rules were very effective predictors, as shown in an independent validation set, demonstrating the validity of the LLM algorithm applied to microarray data and patients' classification. The LLM performed as efficiently as Prediction Analysis of Microarray and Support Vector Machine, and outperformed other learning algorithms such as C4.5. Rulex carried out a feature selection by selecting a new signature (NB-hypo-II) of 11 probe sets that turned out to be the most relevant in predicting outcome among the 62 of the NB-hypo signature. Rules are easily interpretable as they involve only few conditions. Our findings provided evidence that the application of Rulex to the expression values of NB-hypo signature created a set of accurate, high quality, consistent and interpretable rules for the prediction of neuroblastoma patients' outcome. We identified the Rulex weighted classification as a flexible tool that can support clinical decisions. For these reasons, we consider Rulex to be a useful tool for cancer classification from microarray gene expression data.

Supreme Court Rulings on Abortion: Roe v. Wade and Selected Progeny

ERIC Educational Resources Information Center

Uerling, Donald F.

2006-01-01

Abortion is one of the most controversial and contentious issues of our time. Few topics generate as much public debate or leave as little room for political compromise. This article presents a discussion of selected United States Supreme Court decisions on abortion and the legal reasoning supporting those decisions. It should be noted initially…

78 FR 4079 - Privacy Act of 1974: Implementation of Exemptions; Department of Homeland Security U.S. Customs...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-18

... #0;notices is to give interested persons an opportunity to participate in #0;the rule making prior to... number references to law enforcement databases used to support or deny the membership decision for GES trusted traveler programs, as well as the membership decision for trusted traveler programs with foreign...

Comparison of two expert-based assessments of diesel exhaust exposure in a case-control study: programmable decision rules versus expert review of individual jobs.

PubMed

Pronk, Anjoeka; Stewart, Patricia A; Coble, Joseph B; Katki, Hormuzd A; Wheeler, David C; Colt, Joanne S; Baris, Dalsu; Schwenn, Molly; Karagas, Margaret R; Johnson, Alison; Waddell, Richard; Verrill, Castine; Cherala, Sai; Silverman, Debra T; Friesen, Melissa C

2012-10-01

Professional judgment is necessary to assess occupational exposure in population-based case-control studies; however, the assessments lack transparency and are time-consuming to perform. To improve transparency and efficiency, we systematically applied decision rules to questionnaire responses to assess diesel exhaust exposure in the population-based case-control New England Bladder Cancer Study. 2631 participants reported 14 983 jobs; 2749 jobs were administered questionnaires ('modules') with diesel-relevant questions. We applied decision rules to assign exposure metrics based either on the occupational history (OH) responses (OH estimates) or on the module responses (module estimates); we then combined the separate OH and module estimates (OH/module estimates). Each job was also reviewed individually to assign exposure (one-by-one review estimates). We evaluated the agreement between the OH, OH/module and one-by-one review estimates. The proportion of exposed jobs was 20-25% for all jobs, depending on approach, and 54-60% for jobs with diesel-relevant modules. The OH/module and one-by-one review estimates had moderately high agreement for all jobs (κ(w)=0.68-0.81) and for jobs with diesel-relevant modules (κ(w)=0.62-0.78) for the probability, intensity and frequency metrics. For exposed subjects, the Spearman correlation statistic was 0.72 between the cumulative OH/module and one-by-one review estimates. The agreement seen here may represent an upper level of agreement because the algorithm and one-by-one review estimates were not fully independent. This study shows that applying decision-based rules can reproduce a one-by-one review, increase transparency and efficiency, and provide a mechanism to replicate exposure decisions in other studies.

A clinical decision rule to prioritize polysomnography in patients with suspected sleep apnea.

PubMed

Rodsutti, Julvit; Hensley, Michael; Thakkinstian, Ammarin; D'Este, Catherine; Attia, John

2004-06-15

To derive and validate a clinical decision rule that can help to prioritize patients who are on waiting lists for polysomnography, Prospective data collection on consecutive patients referred to a sleep center. The Newcastle Sleep Disorders Centre, University of Newcastle, NSW, Australia. Consecutive adult patients who had been scheduled for initial diagnostic polysomnography. Eight hundred and thirty-seven patients were used for derivation of the decision rule. An apnea-hypopnoea index of at least 5 was used as the cutoff point to diagnose sleep apnea. Fifteen clinical features were included in the analyses using logistic regression to construct a model from the derivation data set. Only 5 variables--age, sex, body mass index, snoring, and stopping breathing during sleep--were significantly associated with sleep apnea. A scoring scheme based on regression coefficients was developed, and the total score was trichotomized into low-, moderate-, and high-risk groups with prevalence of sleep apnea of 8%, 51%, and 82%, respectively. Color-coded tables were developed for ease of use. The clinical decision rule was validated on a separate set of 243 patients. Receiver operating characteristic analysis confirmed that the decision rule performed well, with the area under the curve being similar for both the derivation and validation sets: 0.81 and 0.79, P =.612. We conclude that this decision rule was able to accurately classify the risk of sleep apnea and will be useful for prioritizing patients with suspected sleep apnea who are on waiting lists for polysomnography.

Pricing and Enrollment Planning.

ERIC Educational Resources Information Center

Martin, Robert E.

2003-01-01

Presents a management model for pricing and enrollment planning that yields optimal pricing decisions relative to student fees and average scholarship, the institution's financial ability to support students, and an average cost-pricing rule. (SLD)

14 CFR 13.223 - Standard of proof.

Code of Federal Regulations, 2011 CFR

2011-01-01

... only if the decision or ruling is supported by, and in accordance with, the reliable, probative, and... prove the party's case or defense by a preponderance of reliable, probative, and substantial evidence. ...

14 CFR 13.223 - Standard of proof.

Code of Federal Regulations, 2010 CFR

2010-01-01

... only if the decision or ruling is supported by, and in accordance with, the reliable, probative, and... prove the party's case or defense by a preponderance of reliable, probative, and substantial evidence. ...

Fuzzy inference game approach to uncertainty in business decisions and market competitions.

PubMed

Oderanti, Festus Oluseyi

2013-01-01

The increasing challenges and complexity of business environments are making business decisions and operations more difficult for entrepreneurs to predict the outcomes of these processes. Therefore, we developed a decision support scheme that could be used and adapted to various business decision processes. These involve decisions that are made under uncertain situations such as business competition in the market or wage negotiation within a firm. The scheme uses game strategies and fuzzy inference concepts to effectively grasp the variables in these uncertain situations. The games are played between human and fuzzy players. The accuracy of the fuzzy rule base and the game strategies help to mitigate the adverse effects that a business may suffer from these uncertain factors. We also introduced learning which enables the fuzzy player to adapt over time. We tested this scheme in different scenarios and discover that it could be an invaluable tool in the hand of entrepreneurs that are operating under uncertain and competitive business environments.

A mobile asset sharing policy for hospitals with real time locating systems.

PubMed

Demircan-Yıldız, Ece Arzu; Fescioglu-Unver, Nilgun

2016-01-01

Each year, hospitals lose a considerable amount of time and money due to misplaced mobile assets. In addition the assets which remain in departments that frequently use them depreciate early, while other assets of the same type in different departments are rarely used. A real time locating system can prevent these losses when used with appropriate asset sharing policies. This research quantifies the amount of time a medium size hospital saves by using real time locating system and proposes an asset selection rule to eliminate the asset usage imbalance problem. The asset selection rule proposed is based on multi objective optimization techniques. The effectiveness of this rule on asset to patient time and asset utilization rate variance performance measures were tested using discrete event simulation method. Results show that the proposed asset selection rule improved the usage balance significantly. Sensitivity analysis showed that the proposed rule is robust to changes in demand rates and user preferences. Real time locating systems enable saving considerable amount of time in hospitals, and they can still be improved by integrating decision support mechanisms. Combining tracking technology and asset selection rules helps improve healthcare services.

Integrating clinicians, knowledge and data: expert-based cooperative analysis in healthcare decision support

PubMed Central

2010-01-01

Background Decision support in health systems is a highly difficult task, due to the inherent complexity of the process and structures involved. Method This paper introduces a new hybrid methodology Expert-based Cooperative Analysis (EbCA), which incorporates explicit prior expert knowledge in data analysis methods, and elicits implicit or tacit expert knowledge (IK) to improve decision support in healthcare systems. EbCA has been applied to two different case studies, showing its usability and versatility: 1) Bench-marking of small mental health areas based on technical efficiency estimated by EbCA-Data Envelopment Analysis (EbCA-DEA), and 2) Case-mix of schizophrenia based on functional dependency using Clustering Based on Rules (ClBR). In both cases comparisons towards classical procedures using qualitative explicit prior knowledge were made. Bayesian predictive validity measures were used for comparison with expert panels results. Overall agreement was tested by Intraclass Correlation Coefficient in case "1" and kappa in both cases. Results EbCA is a new methodology composed by 6 steps:. 1) Data collection and data preparation; 2) acquisition of "Prior Expert Knowledge" (PEK) and design of the "Prior Knowledge Base" (PKB); 3) PKB-guided analysis; 4) support-interpretation tools to evaluate results and detect inconsistencies (here Implicit Knowledg -IK- might be elicited); 5) incorporation of elicited IK in PKB and repeat till a satisfactory solution; 6) post-processing results for decision support. EbCA has been useful for incorporating PEK in two different analysis methods (DEA and Clustering), applied respectively to assess technical efficiency of small mental health areas and for case-mix of schizophrenia based on functional dependency. Differences in results obtained with classical approaches were mainly related to the IK which could be elicited by using EbCA and had major implications for the decision making in both cases. Discussion This paper presents EbCA and shows the convenience of completing classical data analysis with PEK as a mean to extract relevant knowledge in complex health domains. One of the major benefits of EbCA is iterative elicitation of IK.. Both explicit and tacit or implicit expert knowledge are critical to guide the scientific analysis of very complex decisional problems as those found in health system research. PMID:20920289

Integrating clinicians, knowledge and data: expert-based cooperative analysis in healthcare decision support.

PubMed

Gibert, Karina; García-Alonso, Carlos; Salvador-Carulla, Luis

2010-09-30

Decision support in health systems is a highly difficult task, due to the inherent complexity of the process and structures involved. This paper introduces a new hybrid methodology Expert-based Cooperative Analysis (EbCA), which incorporates explicit prior expert knowledge in data analysis methods, and elicits implicit or tacit expert knowledge (IK) to improve decision support in healthcare systems. EbCA has been applied to two different case studies, showing its usability and versatility: 1) Bench-marking of small mental health areas based on technical efficiency estimated by EbCA-Data Envelopment Analysis (EbCA-DEA), and 2) Case-mix of schizophrenia based on functional dependency using Clustering Based on Rules (ClBR). In both cases comparisons towards classical procedures using qualitative explicit prior knowledge were made. Bayesian predictive validity measures were used for comparison with expert panels results. Overall agreement was tested by Intraclass Correlation Coefficient in case "1" and kappa in both cases. EbCA is a new methodology composed by 6 steps:. 1) Data collection and data preparation; 2) acquisition of "Prior Expert Knowledge" (PEK) and design of the "Prior Knowledge Base" (PKB); 3) PKB-guided analysis; 4) support-interpretation tools to evaluate results and detect inconsistencies (here Implicit Knowledg -IK- might be elicited); 5) incorporation of elicited IK in PKB and repeat till a satisfactory solution; 6) post-processing results for decision support. EbCA has been useful for incorporating PEK in two different analysis methods (DEA and Clustering), applied respectively to assess technical efficiency of small mental health areas and for case-mix of schizophrenia based on functional dependency. Differences in results obtained with classical approaches were mainly related to the IK which could be elicited by using EbCA and had major implications for the decision making in both cases. This paper presents EbCA and shows the convenience of completing classical data analysis with PEK as a mean to extract relevant knowledge in complex health domains. One of the major benefits of EbCA is iterative elicitation of IK.. Both explicit and tacit or implicit expert knowledge are critical to guide the scientific analysis of very complex decisional problems as those found in health system research.

Semantics-based plausible reasoning to extend the knowledge coverage of medical knowledge bases for improved clinical decision support.

PubMed

Mohammadhassanzadeh, Hossein; Van Woensel, William; Abidi, Samina Raza; Abidi, Syed Sibte Raza

2017-01-01

Capturing complete medical knowledge is challenging-often due to incomplete patient Electronic Health Records (EHR), but also because of valuable, tacit medical knowledge hidden away in physicians' experiences. To extend the coverage of incomplete medical knowledge-based systems beyond their deductive closure, and thus enhance their decision-support capabilities, we argue that innovative, multi-strategy reasoning approaches should be applied. In particular, plausible reasoning mechanisms apply patterns from human thought processes, such as generalization, similarity and interpolation, based on attributional, hierarchical, and relational knowledge. Plausible reasoning mechanisms include inductive reasoning , which generalizes the commonalities among the data to induce new rules, and analogical reasoning , which is guided by data similarities to infer new facts. By further leveraging rich, biomedical Semantic Web ontologies to represent medical knowledge, both known and tentative, we increase the accuracy and expressivity of plausible reasoning, and cope with issues such as data heterogeneity, inconsistency and interoperability. In this paper, we present a Semantic Web-based, multi-strategy reasoning approach, which integrates deductive and plausible reasoning and exploits Semantic Web technology to solve complex clinical decision support queries. We evaluated our system using a real-world medical dataset of patients with hepatitis, from which we randomly removed different percentages of data (5%, 10%, 15%, and 20%) to reflect scenarios with increasing amounts of incomplete medical knowledge. To increase the reliability of the results, we generated 5 independent datasets for each percentage of missing values, which resulted in 20 experimental datasets (in addition to the original dataset). The results show that plausibly inferred knowledge extends the coverage of the knowledge base by, on average, 2%, 7%, 12%, and 16% for datasets with, respectively, 5%, 10%, 15%, and 20% of missing values. This expansion in the KB coverage allowed solving complex disease diagnostic queries that were previously unresolvable, without losing the correctness of the answers. However, compared to deductive reasoning, data-intensive plausible reasoning mechanisms yield a significant performance overhead. We observed that plausible reasoning approaches, by generating tentative inferences and leveraging domain knowledge of experts, allow us to extend the coverage of medical knowledge bases, resulting in improved clinical decision support. Second, by leveraging OWL ontological knowledge, we are able to increase the expressivity and accuracy of plausible reasoning methods. Third, our approach is applicable to clinical decision support systems for a range of chronic diseases.

Optimal Sequential Rules for Computer-Based Instruction.

ERIC Educational Resources Information Center

Vos, Hans J.

1998-01-01

Formulates sequential rules for adapting the appropriate amount of instruction to learning needs in the context of computer-based instruction. Topics include Bayesian decision theory, threshold and linear-utility structure, psychometric model, optimal sequential number of test questions, and an empirical example of sequential instructional…

Granular support vector machines with association rules mining for protein homology prediction.

PubMed

Tang, Yuchun; Jin, Bo; Zhang, Yan-Qing

2005-01-01

Protein homology prediction between protein sequences is one of critical problems in computational biology. Such a complex classification problem is common in medical or biological information processing applications. How to build a model with superior generalization capability from training samples is an essential issue for mining knowledge to accurately predict/classify unseen new samples and to effectively support human experts to make correct decisions. A new learning model called granular support vector machines (GSVM) is proposed based on our previous work. GSVM systematically and formally combines the principles from statistical learning theory and granular computing theory and thus provides an interesting new mechanism to address complex classification problems. It works by building a sequence of information granules and then building support vector machines (SVM) in some of these information granules on demand. A good granulation method to find suitable granules is crucial for modeling a GSVM with good performance. In this paper, we also propose an association rules-based granulation method. For the granules induced by association rules with high enough confidence and significant support, we leave them as they are because of their high "purity" and significant effect on simplifying the classification task. For every other granule, a SVM is modeled to discriminate the corresponding data. In this way, a complex classification problem is divided into multiple smaller problems so that the learning task is simplified. The proposed algorithm, here named GSVM-AR, is compared with SVM by KDDCUP04 protein homology prediction data. The experimental results show that finding the splitting hyperplane is not a trivial task (we should be careful to select the association rules to avoid overfitting) and GSVM-AR does show significant improvement compared to building one single SVM in the whole feature space. Another advantage is that the utility of GSVM-AR is very good because it is easy to be implemented. More importantly and more interestingly, GSVM provides a new mechanism to address complex classification problems.

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

Scolozzi, Rocco, E-mail: rocco.scolozzi@fmach.it; Geneletti, Davide, E-mail: geneletti@ing.unitn.it

Habitat loss and fragmentation are often concurrent to land conversion and urbanization. Simple application of GIS-based landscape pattern indicators may be not sufficient to support meaningful biodiversity impact assessment. A review of the literature reveals that habitat definition and habitat fragmentation are frequently inadequately considered in environmental assessment, notwithstanding the increasing number of tools and approaches reported in the landscape ecology literature. This paper presents an approach for assessing impacts on habitats on a local scale, where availability of species data is often limited, developed for an alpine valley in northern Italy. The perspective of the methodology is multiple scalemore » and species-oriented, and provides both qualitative and quantitative definitions of impact significance. A qualitative decision model is used to assess ecological values in order to support land-use decisions at the local level. Building on recent studies in the same region, the methodology integrates various approaches, such as landscape graphs, object-oriented rule-based habitat assessment and expert knowledge. The results provide insights into future habitat loss and fragmentation caused by land-use changes, and aim at supporting decision-making in planning and suggesting possible ecological compensation. - Highlights: Black-Right-Pointing-Pointer Many environmental assessments inadequately consider habitat loss and fragmentation. Black-Right-Pointing-Pointer Species-perspective for defining habitat quality and connectivity is claimed. Black-Right-Pointing-Pointer Species-based tools are difficult to be applied with limited availability of data. Black-Right-Pointing-Pointer We propose a species-oriented and multiple scale-based qualitative approach. Black-Right-Pointing-Pointer Advantages include being species-oriented and providing value-based information.« less

An overview of bipolar qualitative decision rules

NASA Astrophysics Data System (ADS)

Bonnefon, Jean-Francois; Dubois, Didier; Fargier, Hélène

Making a good decision is often a matter of listing and comparing positive and negative arguments, as studies in cognitive psychology have shown. In such cases, the evaluation scale should be considered bipolar, that is, negative and positive values are explicitly distinguished. Generally, positive and negative features are evaluated separately, as done in Cumulative Prospect Theory. However, contrary to the latter framework that presupposes genuine numerical assessments, decisions are often made on the basis of an ordinal ranking of the pros and the cons, and focusing on the most salient features, i.e., the decision process is qualitative. In this paper, we report on a project aiming at characterizing several decision rules, based on possibilistic order of magnitude reasoning, and tailored for the joint handling of positive and negative affects, and at testing their empirical validity. The simplest rules can be viewed as extensions of the maximin and maximax criteria to the bipolar case and, like them, suffer from a lack of discrimination power. More decisive rules that refine them are also proposed. They account for both the principle of Pareto-efficiency and the notion of order of magnitude reasoning. The most decisive one uses a lexicographic ranking of the pros and cons. It comes down to a special case of Cumulative Prospect Theory, and subsumes the “Take the best” heuristic.

Haunted by a doppelgänger: irrelevant facial similarity affects rule-based judgments.

PubMed

von Helversen, Bettina; Herzog, Stefan M; Rieskamp, Jörg

2014-01-01

Judging other people is a common and important task. Every day professionals make decisions that affect the lives of other people when they diagnose medical conditions, grant parole, or hire new employees. To prevent discrimination, professional standards require that decision makers render accurate and unbiased judgments solely based on relevant information. Facial similarity to previously encountered persons can be a potential source of bias. Psychological research suggests that people only rely on similarity-based judgment strategies if the provided information does not allow them to make accurate rule-based judgments. Our study shows, however, that facial similarity to previously encountered persons influences judgment even in situations in which relevant information is available for making accurate rule-based judgments and where similarity is irrelevant for the task and relying on similarity is detrimental. In two experiments in an employment context we show that applicants who looked similar to high-performing former employees were judged as more suitable than applicants who looked similar to low-performing former employees. This similarity effect was found despite the fact that the participants used the relevant résumé information about the applicants by following a rule-based judgment strategy. These findings suggest that similarity-based and rule-based processes simultaneously underlie human judgment.

Characterizing Rule-Based Category Learning Deficits in Patients with Parkinson's Disease

ERIC Educational Resources Information Center

Filoteo, J. Vincent; Maddox, W. Todd; Ing, A. David; Song, David D.

2007-01-01

Parkinson's disease (PD) patients and normal controls were tested in three category learning experiments to determine if previously observed rule-based category learning impairments in PD patients were due to deficits in selective attention or working memory. In Experiment 1, optimal categorization required participants to base their decision on a…

Dose Transition Pathways: The Missing Link Between Complex Dose-Finding Designs and Simple Decision-Making.

PubMed

Yap, Christina; Billingham, Lucinda J; Cheung, Ying Kuen; Craddock, Charlie; O'Quigley, John

2017-12-15

The ever-increasing pace of development of novel therapies mandates efficient methodologies for assessment of their tolerability and activity. Evidence increasingly support the merits of model-based dose-finding designs in identifying the recommended phase II dose compared with conventional rule-based designs such as the 3 + 3 but despite this, their use remains limited. Here, we propose a useful tool, dose transition pathways (DTP), which helps overcome several commonly faced practical and methodologic challenges in the implementation of model-based designs. DTP projects in advance the doses recommended by a model-based design for subsequent patients (stay, escalate, de-escalate, or stop early), using all the accumulated information. After specifying a model with favorable statistical properties, we utilize the DTP to fine-tune the model to tailor it to the trial's specific requirements that reflect important clinical judgments. In particular, it can help to determine how stringent the stopping rules should be if the investigated therapy is too toxic. Its use to design and implement a modified continual reassessment method is illustrated in an acute myeloid leukemia trial. DTP removes the fears of model-based designs as unknown, complex systems and can serve as a handbook, guiding decision-making for each dose update. In the illustrated trial, the seamless, clear transition for each dose recommendation aided the investigators' understanding of the design and facilitated decision-making to enable finer calibration of a tailored model. We advocate the use of the DTP as an integral procedure in the co-development and successful implementation of practical model-based designs by statisticians and investigators. Clin Cancer Res; 23(24); 7440-7. ©2017 AACR . ©2017 American Association for Cancer Research.

Extracting decision rules from police accident reports through decision trees.

PubMed

de Oña, Juan; López, Griselda; Abellán, Joaquín

2013-01-01

Given the current number of road accidents, the aim of many road safety analysts is to identify the main factors that contribute to crash severity. To pinpoint those factors, this paper shows an application that applies some of the methods most commonly used to build decision trees (DTs), which have not been applied to the road safety field before. An analysis of accidents on rural highways in the province of Granada (Spain) between 2003 and 2009 (both inclusive) showed that the methods used to build DTs serve our purpose and may even be complementary. Applying these methods has enabled potentially useful decision rules to be extracted that could be used by road safety analysts. For instance, some of the rules may indicate that women, contrary to men, increase their risk of severity under bad lighting conditions. The rules could be used in road safety campaigns to mitigate specific problems. This would enable managers to implement priority actions based on a classification of accidents by types (depending on their severity). However, the primary importance of this proposal is that other databases not used here (i.e. other infrastructure, roads and countries) could be used to identify unconventional problems in a manner easy for road safety managers to understand, as decision rules. Copyright © 2012 Elsevier Ltd. All rights reserved.

An informatics strategy for cancer care

PubMed Central

Wright, J; Shogan, A; McCune, J; Stevens, S

2008-01-01

Whether transitioning from paper to electronic records or attempting to leverage data from existing systems for outcome studies, oncology practices face many challenges in defining and executing an informatics strategy. With the increasing costs of oncology treatments and expected changes in reimbursement rules, including requirements for evidence that supports physician decisions, it will become essential to collect data on treatment decisions and treatment efficacy to run a successful program. This study evaluates the current state of informatics systems available for use in oncology programs and focuses on developing an informatics strategy to meet the challenges introduced by expected changes in reimbursement rules and in medical and information technologies. PMID:21611003

Improving ambulatory prescribing safety with a handheld decision support system: a randomized controlled trial.

PubMed

Berner, Eta S; Houston, Thomas K; Ray, Midge N; Allison, Jeroan J; Heudebert, Gustavo R; Chatham, W Winn; Kennedy, John I; Glandon, Gerald L; Norton, Patricia A; Crawford, Myra A; Maisiak, Richard S

2006-01-01

To evaluate the effectiveness of a personal digital assistant (PDA)-based clinical decision support system (CDSS) on nonsteroidal anti-inflammatory drug (NSAID) prescribing safety in the outpatient setting. The design was a randomized, controlled trial conducted in a university-based resident clinic. Internal medicine residents received a PDA-based CDSS suite. For intervention residents, the CDSS included a prediction rule for NSAID-related gastrointestinal risk assessment and treatment recommendations. Unannounced standardized patients (SPs) trained to portray musculoskeletal symptoms presented to study physicians. Safety outcomes were assessed from the prescriptions given to the SPs. Each prescription was reviewed by a committee of clinicians blinded to participant, intervention group assignment, and baseline or follow-up status. Prescriptions were judged as safe or unsafe. The main outcome measure was the differential change in unsafe prescribing of NSAIDs for the intervention versus the control group. At baseline, the mean proportion of cases per physician with unsafe prescriptions for the two groups was similar (0.27 vs. 0.29, p > 0.05). Controlling for baseline performance, intervention participants prescribed more safely than controls after receiving the CDSS (0.23 vs. 0.45 [F = 4.24, p < 0.05]). With the CDSS, intervention participants documented more complete assessment of patient gastrointestinal risk from NSAIDs. PARTICIPANTS provided with a PDA-based CDSS for NSAID prescribing made fewer unsafe treatment decisions than participants without the CDSS.

Distributed decision support for the 21st century mission space

NASA Astrophysics Data System (ADS)

McQuay, William K.

2002-07-01

The past decade has produced significant changes in the conduct of military operations: increased humanitarian missions, asymmetric warfare, the reliance on coalitions and allies, stringent rules of engagement, concern about casualties, and the need for sustained air operations. Future mission commanders will need to assimilate a tremendous amount of information, make quick-response decisions, and quantify the effects of those decisions in the face of uncertainty. Integral to this process is creating situational assessment-understanding the mission space, simulation to analyze alternative futures, current capabilities, planning assessments, course-of-action assessments, and a common operational picture-keeping everyone on the same sheet of paper. Decision support tools in a distributed collaborative environment offer the capability of decomposing these complex multitask processes and distributing them over a dynamic set of execution assets. Decision support technologies can semi-automate activities, such as planning an operation, that have a reasonably well-defined process and provide machine-level interfaces to refine the myriad of information that is not currently fused. The marriage of information and simulation technologies provides the mission commander with a collaborative virtual environment for planning and decision support.

Learning temporal rules to forecast instability in continuously monitored patients

PubMed Central

Dubrawski, Artur; Wang, Donghan; Hravnak, Marilyn; Clermont, Gilles; Pinsky, Michael R

2017-01-01

Inductive machine learning, and in particular extraction of association rules from data, has been successfully used in multiple application domains, such as market basket analysis, disease prognosis, fraud detection, and protein sequencing. The appeal of rule extraction techniques stems from their ability to handle intricate problems yet produce models based on rules that can be comprehended by humans, and are therefore more transparent. Human comprehension is a factor that may improve adoption and use of data-driven decision support systems clinically via face validity. In this work, we explore whether we can reliably and informatively forecast cardiorespiratory instability (CRI) in step-down unit (SDU) patients utilizing data from continuous monitoring of physiologic vital sign (VS) measurements. We use a temporal association rule extraction technique in conjunction with a rule fusion protocol to learn how to forecast CRI in continuously monitored patients. We detail our approach and present and discuss encouraging empirical results obtained using continuous multivariate VS data from the bedside monitors of 297 SDU patients spanning 29 346 hours (3.35 patient-years) of observation. We present example rules that have been learned from data to illustrate potential benefits of comprehensibility of the extracted models, and we analyze the empirical utility of each VS as a potential leading indicator of an impending CRI event. PMID:27274020

Combined rule extraction and feature elimination in supervised classification.

PubMed

Liu, Sheng; Patel, Ronak Y; Daga, Pankaj R; Liu, Haining; Fu, Gang; Doerksen, Robert J; Chen, Yixin; Wilkins, Dawn E

2012-09-01

There are a vast number of biology related research problems involving a combination of multiple sources of data to achieve a better understanding of the underlying problems. It is important to select and interpret the most important information from these sources. Thus it will be beneficial to have a good algorithm to simultaneously extract rules and select features for better interpretation of the predictive model. We propose an efficient algorithm, Combined Rule Extraction and Feature Elimination (CRF), based on 1-norm regularized random forests. CRF simultaneously extracts a small number of rules generated by random forests and selects important features. We applied CRF to several drug activity prediction and microarray data sets. CRF is capable of producing performance comparable with state-of-the-art prediction algorithms using a small number of decision rules. Some of the decision rules are biologically significant.

Retrospective checking of compliance with practice guidelines for acute stroke care: a novel experiment using openEHR’s Guideline Definition Language

PubMed Central

2014-01-01

Background Providing scalable clinical decision support (CDS) across institutions that use different electronic health record (EHR) systems has been a challenge for medical informatics researchers. The lack of commonly shared EHR models and terminology bindings has been recognised as a major barrier to sharing CDS content among different organisations. The openEHR Guideline Definition Language (GDL) expresses CDS content based on openEHR archetypes and can support any clinical terminologies or natural languages. Our aim was to explore in an experimental setting the practicability of GDL and its underlying archetype formalism. A further aim was to report on the artefacts produced by this new technological approach in this particular experiment. We modelled and automatically executed compliance checking rules from clinical practice guidelines for acute stroke care. Methods We extracted rules from the European clinical practice guidelines as well as from treatment contraindications for acute stroke care and represented them using GDL. Then we executed the rules retrospectively on 49 mock patient cases to check the cases’ compliance with the guidelines, and manually validated the execution results. We used openEHR archetypes, GDL rules, the openEHR reference information model, reference terminologies and the Data Archetype Definition Language. We utilised the open-sourced GDL Editor for authoring GDL rules, the international archetype repository for reusing archetypes, the open-sourced Ocean Archetype Editor for authoring or modifying archetypes and the CDS Workbench for executing GDL rules on patient data. Results We successfully represented clinical rules about 14 out of 19 contraindications for thrombolysis and other aspects of acute stroke care with 80 GDL rules. These rules are based on 14 reused international archetypes (one of which was modified), 2 newly created archetypes and 51 terminology bindings (to three terminologies). Our manual compliance checks for 49 mock patients were a complete match versus the automated compliance results. Conclusions Shareable guideline knowledge for use in automated retrospective checking of guideline compliance may be achievable using GDL. Whether the same GDL rules can be used for at-the-point-of-care CDS remains unknown. PMID:24886468

A systematic review and summarization of the recommendations and research surrounding Curriculum-Based Measurement of oral reading fluency (CBM-R) decision rules.

PubMed

Ardoin, Scott P; Christ, Theodore J; Morena, Laura S; Cormier, Damien C; Klingbeil, David A

2013-02-01

Research and policy have established that data are necessary to guide decisions within education. Many of these decisions are made within problem solving and response to intervention frameworks for service delivery. Curriculum-Based Measurement in Reading (CBM-R) is a widely used data collection procedure within those models of service delivery. Although the evidence for CBM-R as a screening and benchmarking procedure has been summarized multiple times in the literature, there is no comprehensive review of the evidence for its application to monitor and evaluate individual student progress. The purpose of this study was to identify and summarize the psychometric and empirical evidence for CBM-R as it is used to monitor and evaluate student progress. There was an emphasis on the recommended number of data points collected during progress monitoring and interpretive guidelines. The review identified 171 journal articles, chapters, and instructional manuals using online search engines and research databases. Recommendations and evidence from 102 documents that met the study criteria were evaluated and summarized. Results indicate that most decision-making practices are based on expert opinion and that there is very limited psychometric or empirical support for such practices. There is a lack of published evidence to support program evaluation and progress monitoring with CBM-R. More research is required to inform data collection procedures and interpretive guidelines. Copyright © 2012 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

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 which were designed to automate functions and decisions associated with a combat aircraft's subsystems, are discussed. 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. Simulation and comparative workload results for two mission scenarios are given. The scenarios are inbound surface-to-air-missile attack on the aircraft and pilot incapacitation. The methodology used to develop the AUTOCREW knowledge bases is summarized. Issues involved in designing the navigation sensor selection expert in AUTOCREW's NAVIGATOR knowledge base are discussed in detail. The performance of seven navigation systems aiding a medium-accuracy INS was investigated using Kalman filter covariance analyses. A navigation sensor management (NSM) expert system was formulated from covariance simulation data using the analysis of variance (ANOVA) method and the ID3 algorithm. ANOVA results show that statistically different position accuracies are obtained when different navaids are used, the number of navaids aiding the INS is varied, the aircraft's trajectory is varied, and the performance history is varied. The ID3 algorithm determines the NSM expert's classification rules in the form of decision trees. The performance of these decision trees was assessed on two arbitrary trajectories, and the results demonstrate that the NSM expert adapts to new situations and provides reasonable estimates of the expected hybrid performance.

Lessons for health care rationing from the case of child B.

PubMed

Price, D

1996-01-20

More details have emerged about the child B leukaemia case with the publication of the All England Law Report on the Appeal Court decision. At the time the view was widely held that the controversy might have been avoided if the responsible health authority had consulted the public. The law report reveals, however, that the courts adopted a moral language widely at variance with that of the patient's doctor. The courts were concerned to support a utilitarian decision procedure based on calculations of the greatest overall good; the doctor was concerned with the best interests of a sick child. The doctor-patient relationship may be damaged when public consideration transforms the issue in this way. Also, the Appeal Court supported a decision which claimed to have "weighed" opposing evaluations, but it excused the health authority from describing how that weighing took place. One of the main criticisms of the utilitarian approach, however, is that weighing of this type is extremely difficult to justify. By its ruling the court has made legal challenge on the grounds of inadequate consultation virtually impossible to substantiate.

Lessons for health care rationing from the case of child B.

PubMed Central

Price, D.

1996-01-01

More details have emerged about the child B leukaemia case with the publication of the All England Law Report on the Appeal Court decision. At the time the view was widely held that the controversy might have been avoided if the responsible health authority had consulted the public. The law report reveals, however, that the courts adopted a moral language widely at variance with that of the patient's doctor. The courts were concerned to support a utilitarian decision procedure based on calculations of the greatest overall good; the doctor was concerned with the best interests of a sick child. The doctor-patient relationship may be damaged when public consideration transforms the issue in this way. Also, the Appeal Court supported a decision which claimed to have "weighed" opposing evaluations, but it excused the health authority from describing how that weighing took place. One of the main criticisms of the utilitarian approach, however, is that weighing of this type is extremely difficult to justify. By its ruling the court has made legal challenge on the grounds of inadequate consultation virtually impossible to substantiate. Images p168-a PMID:8563539

A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making.

PubMed

Prezenski, Sabine; Brechmann, André; Wolff, Susann; Russwinkel, Nele

2017-01-01

Decision-making is a high-level cognitive process based on cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of cognitive modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar-based and a rule-based approach which incorporates perceptual-motor and metacognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the modeling data. However, we found a larger variance and a lower overall performance of the modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional information about the underlying cognitive processes from behavioral, psychobiological and neurophysiological data may help to optimize future applications of this model such that it can be transferred to other domains of comparable dynamic decision tasks.

A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making

PubMed Central

Prezenski, Sabine; Brechmann, André; Wolff, Susann; Russwinkel, Nele

2017-01-01

Decision-making is a high-level cognitive process based on cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of cognitive modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar-based and a rule-based approach which incorporates perceptual-motor and metacognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the modeling data. However, we found a larger variance and a lower overall performance of the modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional information about the underlying cognitive processes from behavioral, psychobiological and neurophysiological data may help to optimize future applications of this model such that it can be transferred to other domains of comparable dynamic decision tasks. PMID:28824512

The impact of loss sensitivity on a mobile phone supply chain system stability based on the chaos theory

NASA Astrophysics Data System (ADS)

Ma, Junhai; Xie, Lei

2018-02-01

This paper, based on the China's communications and the current situation of the mobile phone industry, focuses on the stability of a supply chain system that consists of one supplier and one bounded rational retailer. We explore the influence of the decision makers' loss sensitivity and decision adjustment speed on the stability of the supply chain. It is found that when the retailer is not sensitive to the loss or adjusts decisions cautiously, the system can be stable. The single-retailer model is extended to a multi-retailer one to study the influence of competition on the system stability. The results show that the market share of each retailer does not affect the system stability when it is fixed. The decision of each retailer does not affect that of any other retailer and the system stability. We present two decision adjustment rules (;bounded rationality expectation (BRE); and "adaptive exponential smoothing (AES)") and compare their performances on the system stability, and find that the AES rule does not affect the system stability, while the BRE rule will make the system stability be sensitive to the retailers' loss sensitivity and the decision adjustment speed. We also reveal the unstable system's negative impact on the retailers' decisions and profits, to emphasize the importance to maintain the system stability.

Phonological reduplication in sign language: Rules rule

PubMed Central

Berent, Iris; Dupuis, Amanda; Brentari, Diane

2014-01-01

Productivity—the hallmark of linguistic competence—is typically attributed to algebraic rules that support broad generalizations. Past research on spoken language has documented such generalizations in both adults and infants. But whether algebraic rules form part of the linguistic competence of signers remains unknown. To address this question, here we gauge the generalization afforded by American Sign Language (ASL). As a case study, we examine reduplication (X→XX)—a rule that, inter alia, generates ASL nouns from verbs. If signers encode this rule, then they should freely extend it to novel syllables, including ones with features that are unattested in ASL. And since reduplicated disyllables are preferred in ASL, such a rule should favor novel reduplicated signs. Novel reduplicated signs should thus be preferred to nonreduplicative controls (in rating), and consequently, such stimuli should also be harder to classify as nonsigns (in the lexical decision task). The results of four experiments support this prediction. These findings suggest that the phonological knowledge of signers includes powerful algebraic rules. The convergence between these conclusions and previous evidence for phonological rules in spoken language suggests that the architecture of the phonological mind is partly amodal. PMID:24959158

An Interactive Decision Support System for Scheduling Fighter Pilot Training

DTIC Science & Technology

2002-03-26

Deitel , H.M. and Deitel , P.J. C: How to Program , 2nd ed., Prentice Hall, 1994. 8. Deitel , H.M. and Deitel , P.J. How to Program Java...Visual Basic Programming language, the Excel tool is modified in several ways. Scheduling Dispatch rules are implemented to automatically generate... programming language, the Excel tool was modified in several ways. Scheduling dispatch rules are implemented to automatically generate

Comparison of two expert-based assessments of diesel exhaust exposure in a case-control study: Programmable decision rules versus expert review of individual jobs

PubMed Central

Pronk, Anjoeka; Stewart, Patricia A.; Coble, Joseph B.; Katki, Hormuzd A.; Wheeler, David C.; Colt, Joanne S.; Baris, Dalsu; Schwenn, Molly; Karagas, Margaret R.; Johnson, Alison; Waddell, Richard; Verrill, Castine; Cherala, Sai; Silverman, Debra T.; Friesen, Melissa C.

2012-01-01

Objectives Professional judgment is necessary to assess occupational exposure in population-based case-control studies; however, the assessments lack transparency and are time-consuming to perform. To improve transparency and efficiency, we systematically applied decision rules to the questionnaire responses to assess diesel exhaust exposure in the New England Bladder Cancer Study, a population-based case-control study. Methods 2,631 participants reported 14,983 jobs; 2,749 jobs were administered questionnaires (‘modules’) with diesel-relevant questions. We applied decision rules to assign exposure metrics based solely on the occupational history responses (OH estimates) and based on the module responses (module estimates); we combined the separate OH and module estimates (OH/module estimates). Each job was also reviewed one at a time to assign exposure (one-by-one review estimates). We evaluated the agreement between the OH, OH/module, and one-by-one review estimates. Results The proportion of exposed jobs was 20–25% for all jobs, depending on approach, and 54–60% for jobs with diesel-relevant modules. The OH/module and one-by-one review had moderately high agreement for all jobs (κw=0.68–0.81) and for jobs with diesel-relevant modules (κw=0.62–0.78) for the probability, intensity, and frequency metrics. For exposed subjects, the Spearman correlation statistic was 0.72 between the cumulative OH/module and one-by-one review estimates. Conclusions The agreement seen here may represent an upper level of agreement because the algorithm and one-by-one review estimates were not fully independent. This study shows that applying decision-based rules can reproduce a one-by-one review, increase transparency and efficiency, and provide a mechanism to replicate exposure decisions in other studies. PMID:22843440

Traditional versus rule-based programming techniques - Application to the control of optional flight information

NASA Technical Reports Server (NTRS)

Ricks, Wendell R.; Abbott, Kathy H.

1987-01-01

A traditional programming technique for controlling the display of optional flight information in a civil transport cockpit is compared to a rule-based technique for the same function. This application required complex decision logic and a frequently modified rule base. The techniques are evaluated for execution efficiency and implementation ease; the criterion used to calculate the execution efficiency is the total number of steps required to isolate hypotheses that were true and the criteria used to evaluate the implementability are ease of modification and verification and explanation capability. It is observed that the traditional program is more efficient than the rule-based program; however, the rule-based programming technique is more applicable for improving programmer productivity.

Application of Domain Knowledge to Software Quality Assurance

NASA Technical Reports Server (NTRS)

Wild, Christian W.

1997-01-01

This work focused on capturing, using, and evolving a qualitative decision support structure across the life cycle of a project. The particular application of this study was towards business process reengineering and the representation of the business process in a set of Business Rules (BR). In this work, we defined a decision model which captured the qualitative decision deliberation process. It represented arguments both for and against proposed alternatives to a problem. It was felt that the subjective nature of many critical business policy decisions required a qualitative modeling approach similar to that of Lee and Mylopoulos. While previous work was limited almost exclusively to the decision capture phase, which occurs early in the project life cycle, we investigated the use of such a model during the later stages as well. One of our significant developments was the use of the decision model during the operational phase of a project. By operational phase, we mean the phase in which the system or set of policies which were earlier decided are deployed and put into practice. By making the decision model available to operational decision makers, they would have access to the arguments pro and con for a variety of actions and can thus make a more informed decision which balances the often conflicting criteria by which the value of action is measured. We also developed the concept of a 'monitored decision' in which metrics of performance were identified during the decision making process and used to evaluate the quality of that decision. It is important to monitor those decision which seem at highest risk of not meeting their stated objectives. Operational decisions are also potentially high risk decisions. Finally, we investigated the use of performance metrics for monitored decisions and audit logs of operational decisions in order to feed an evolutionary phase of the the life cycle. During evolution, decisions are revisisted, assumptions verified or refuted, and possible reassessments resulting in new policy are made. In this regard we implemented a machine learning algorithm which automatically defined business rules based on expert assessment of the quality of operational decisions as recorded during deployment.

Design of operating rules in complex water resources systems using historical records, expert criteria and fuzzy logic

NASA Astrophysics Data System (ADS)

Pulido-Velazquez, Manuel; Macian-Sorribes, Hector; María Benlliure-Moreno, Jose; Fullana-Montoro, Juan

2015-04-01

Water resources systems in areas with a strong tradition in water use are complex to manage by the high amount of constraints that overlap in time and space, creating a complicated framework in which past, present and future collide between them. In addition, it is usual to find "hidden constraints" in system operations, which condition operation decisions being unnoticed by anyone but the river managers and users. Being aware of those hidden constraints requires usually years of experience and a degree of involvement in that system's management operations normally beyond the possibilities of technicians. However, their impact in the management decisions is strongly imprinted in the historical data records available. The purpose of this contribution is to present a methodology capable of assessing operating rules in complex water resources systems combining historical records and expert criteria. Both sources are coupled using fuzzy logic. The procedure stages are: 1) organize expert-technicians preliminary meetings to let the first explain how they manage the system; 2) set up a fuzzy rule-based system (FRB) structure according to the way the system is managed; 3) use the historical records available to estimate the inputs' fuzzy numbers, to assign preliminary output values to the FRB rules and to train and validate these rules; 4) organize expert-technician meetings to discuss the rule structure and the input's quantification, returning if required to the second stage; 5) once the FRB structure is accepted, its output values must be refined and completed with the aid of the experts by using meetings, workshops or surveys; 6) combine the FRB with a Decision Support System (DSS) to simulate the effect of those management decisions; 7) compare its results with the ones offered by the historical records and/or simulation or optimization models; and 8) discuss with the stakeholders the model performance returning, if it's required, to the fifth or the second stage. The methodology proposed has been applied to the Jucar River Basin (Spain). This basin has 3 reservoirs, 4 headwaters, 11 demands and 5 environmental flows; which form together a complex constraint set. After the preliminary meetings, one 81-rule FRB was created, using as inputs the system state variables at the start of the hydrologic year, and as outputs the target reservoir release schedule. The inputs' fuzzy numbers were estimated jointly using surveys. Fifteen years of historical records were used to train the system's outputs. The obtained FRB was then refined during additional expert-technician meetings. After that, the resulting FRB was introduced into a DSS simulating the effect of those management rules for different hydrological conditions. Three additional FRB's were created using: 1) exclusively the historical records; 2) a stochastic optimization model; and 3) a deterministic optimization model. The results proved to be consistent with the expectations, with the stakeholder's FRB performance located between the data-driven simulation and the stochastic optimization FRB's; and reflect the stakeholders' major goals and concerns about the river management. ACKNOWLEDGEMENT: This study has been partially supported by the IMPADAPT project (CGL2013-48424-C2-1-R) with Spanish MINECO (Ministerio de Economía y Competitividad) funds.

A new intuitionistic fuzzy rule-based decision-making system for an operating system process scheduler.

PubMed

Butt, Muhammad Arif; Akram, Muhammad

2016-01-01

We present a new intuitionistic fuzzy rule-based decision-making system based on intuitionistic fuzzy sets for a process scheduler of a batch operating system. Our proposed intuitionistic fuzzy scheduling algorithm, inputs the nice value and burst time of all available processes in the ready queue, intuitionistically fuzzify the input values, triggers appropriate rules of our intuitionistic fuzzy inference engine and finally calculates the dynamic priority (dp) of all the processes in the ready queue. Once the dp of every process is calculated the ready queue is sorted in decreasing order of dp of every process. The process with maximum dp value is sent to the central processing unit for execution. Finally, we show complete working of our algorithm on two different data sets and give comparisons with some standard non-preemptive process schedulers.

The precautionary principle is incoherent.

PubMed

Peterson, Martin

2006-06-01

This article argues that no version of the precautionary principle can be reasonably applied to decisions that may lead to fatal outcomes. In support of this strong claim, a number of desiderata are proposed, which reasonable rules for rational decision making ought to satisfy. Thereafter, two impossibility theorems are proved, showing that no version of the precautionary principle can satisfy the proposed desiderata. These theorems are directly applicable to recent discussions of the precautionary principle in medicine, biotechnology, environmental management, and related fields. The impossibility theorems do not imply, however, that the precautionary principle is of no relevance at all in policy discussions. Even if it is not a reasonable rule for rational decision making, it is possible to interpret the precautionary principle in other ways, e.g., as an argumentative tool or as an epistemic principle favoring a reversed burden of proof.

Error-associated behaviors and error rates for robotic geology

NASA Technical Reports Server (NTRS)

Anderson, Robert C.; Thomas, Geb; Wagner, Jacob; Glasgow, Justin

2004-01-01

This study explores human error as a function of the decision-making process. One of many models for human decision-making is Rasmussen's decision ladder [9]. The decision ladder identifies the multiple tasks and states of knowledge involved in decision-making. The tasks and states of knowledge can be classified by the level of cognitive effort required to make the decision, leading to the skill, rule, and knowledge taxonomy (Rasmussen, 1987). Skill based decisions require the least cognitive effort and knowledge based decisions require the greatest cognitive effort. Errors can occur at any of the cognitive levels.

Microcomputer-based classification of environmental data in municipal areas

NASA Astrophysics Data System (ADS)

Thiergärtner, H.

1995-10-01

Multivariate data-processing methods used in mineral resource identification can be used to classify urban regions. Using elements of expert systems, geographical information systems, as well as known classification and prognosis systems, it is possible to outline a single model that consists of resistant and of temporary parts of a knowledge base including graphical input and output treatment and of resistant and temporary elements of a bank of methods and algorithms. Whereas decision rules created by experts will be stored in expert systems directly, powerful classification rules in form of resistant but latent (implicit) decision algorithms may be implemented in the suggested model. The latent functions will be transformed into temporary explicit decision rules by learning processes depending on the actual task(s), parameter set(s), pixels selection(s), and expert control(s). This takes place both at supervised and nonsupervised classification of multivariately described pixel sets representing municipal subareas. The model is outlined briefly and illustrated by results obtained in a target area covering a part of the city of Berlin (Germany).

An Expert-System Engine With Operative Probabilities

NASA Technical Reports Server (NTRS)

Orlando, N. E.; Palmer, M. T.; Wallace, R. S.

1986-01-01

Program enables proof-of-concepts tests of expert systems under development. AESOP is rule-based inference engine for expert system, which makes decisions about particular situation given user-supplied hypotheses, rules, and answers to questions drawn from rules. If knowledge base containing hypotheses and rules governing environment is available to AESOP, almost any situation within that environment resolved by answering questions asked by AESOP. Questions answered with YES, NO, MAYBE, DON'T KNOW, DON'T CARE, or with probability factor ranging from 0 to 10. AESOP written in Franz LISP for interactive execution.

Research of Litchi Diseases Diagnosis Expertsystem Based on Rbr and Cbr

NASA Astrophysics Data System (ADS)

Xu, Bing; Liu, Liqun

To conquer the bottleneck problems existing in the traditional rule-based reasoning diseases diagnosis system, such as low reasoning efficiency and lack of flexibility, etc.. It researched the integrated case-based reasoning (CBR) and rule-based reasoning (RBR) technology, and put forward a litchi diseases diagnosis expert system (LDDES) with integrated reasoning method. The method use data mining and knowledge obtaining technology to establish knowledge base and case library. It adopt rules to instruct the retrieval and matching for CBR, and use association rule and decision trees algorithm to calculate case similarity.The experiment shows that the method can increase the system's flexibility and reasoning ability, and improve the accuracy of litchi diseases diagnosis.

A Compensatory Approach to Optimal Selection with Mastery Scores. Research Report 94-2.

ERIC Educational Resources Information Center

van der Linden, Wim J.; Vos, Hans J.

This paper presents some Bayesian theories of simultaneous optimization of decision rules for test-based decisions. Simultaneous decision making arises when an institution has to make a series of selection, placement, or mastery decisions with respect to subjects from a population. An obvious example is the use of individualized instruction in…

Development of a Just-in-Time Adaptive Intervention for Smoking Cessation Among Korean American Emerging Adults

PubMed Central

Cerrada, Christian Jules; Dzubur, Eldin; Blackman, Kacie C. A.; Mays, Vickie; Shoptaw, Steven; Huh, Jimi

2017-01-01

Purpose Cigarette smoking is a preventable risk factor that contributes to unnecessary lung cancer burden among Korean Americans and there is limited research on effective smoking cessation strategies for this population. Smartphone-based smoking cessation apps that leverage just-in-time adaptive interventions (JITAIs) hold promise for smokers attempting to quit. However, little is known about how to develop and tailor a smoking cessation JITAI for Korean American emerging adult (KAEA) smokers. Method This paper documents the development process of MyQuit USC according to design guidelines for JITAI. Our development process builds on findings from a prior ecological momentary assessment study by using qualitative research methods. Semi-structured interviews and a focus group were conducted to inform which intervention options to offer and the decision rules that dictate their delivery. Results Qualitative findings highlighted that (1) smoking episodes are highly context-driven and that (2) KAEA smokers believe they need personalized cessation strategies tailored to different contexts. Thus, MyQuit USC operates via decision rules that guide the delivery of personalized implementation intentions, which are contingent on dynamic factors, to be delivered “just in time” at user-scheduled, high-risk smoking situations. Conclusion Through an iterative design process, informed by quantitative and qualitative formative research, we developed a smoking cessation JITAI tailored specifically for KAEA smokers. Further testing is under way to optimize future versions of the app with the most effective intervention strategies and decision rules. MyQuit USC has the potential to provide cessation support in real-world settings, when KAEAs need them the most. PMID:28070868

Development of a Just-in-Time Adaptive Intervention for Smoking Cessation Among Korean American Emerging Adults.

PubMed

Cerrada, Christian Jules; Dzubur, Eldin; Blackman, Kacie C A; Mays, Vickie; Shoptaw, Steven; Huh, Jimi

2017-10-01

Cigarette smoking is a preventable risk factor that contributes to unnecessary lung cancer burden among Korean Americans and there is limited research on effective smoking cessation strategies for this population. Smartphone-based smoking cessation apps that leverage just-in-time adaptive interventions (JITAIs) hold promise for smokers attempting to quit. However, little is known about how to develop and tailor a smoking cessation JITAI for Korean American emerging adult (KAEA) smokers. This paper documents the development process of MyQuit USC according to design guidelines for JITAI. Our development process builds on findings from a prior ecological momentary assessment study by using qualitative research methods. Semi-structured interviews and a focus group were conducted to inform which intervention options to offer and the decision rules that dictate their delivery. Qualitative findings highlighted that (1) smoking episodes are highly context-driven and that (2) KAEA smokers believe they need personalized cessation strategies tailored to different contexts. Thus, MyQuit USC operates via decision rules that guide the delivery of personalized implementation intentions, which are contingent on dynamic factors, to be delivered "just in time" at user-scheduled, high-risk smoking situations. Through an iterative design process, informed by quantitative and qualitative formative research, we developed a smoking cessation JITAI tailored specifically for KAEA smokers. Further testing is under way to optimize future versions of the app with the most effective intervention strategies and decision rules. MyQuit USC has the potential to provide cessation support in real-world settings, when KAEAs need them the most.

Distributed semantic networks and CLIPS

NASA Technical Reports Server (NTRS)

Snyder, James; Rodriguez, Tony

1991-01-01

Semantic networks of frames are commonly used as a method of reasoning in many problems. In most of these applications the semantic network exists as a single entity in a single process environment. Advances in workstation hardware provide support for more sophisticated applications involving multiple processes, interacting in a distributed environment. In these applications the semantic network may well be distributed over several concurrently executing tasks. This paper describes the design and implementation of a frame based, distributed semantic network in which frames are accessed both through C Language Integrated Production System (CLIPS) expert systems and procedural C++ language programs. The application area is a knowledge based, cooperative decision making model utilizing both rule based and procedural experts.

Decision support methodology to establish priorities on the inspection of structures

NASA Astrophysics Data System (ADS)

Cortes, V. Juliette; Sterlacchini, Simone; Bogaard, Thom; Frigerio, Simone; Schenato, Luca; Pasuto, Alessandro

2014-05-01

For hydro-meteorological hazards in mountain areas, the regular inspection of check dams and bridges is important due to the effect of their functional status on water-sediment processes. Moreover, the inspection of these structures is time consuming for organizations due to their extensive number in many regions. However, trained citizen-volunteers can support civil protection and technical services in the frequency, timeliness and coverage of monitoring the functional status of hydraulic structures. Technicians should evaluate and validate these reports to get an index for the status of the structure. Thus, preventive actions could initiate such as the cleaning of obstructions or to pre-screen potential problems for a second level inspection. This study proposes a decision support methodology that technicians can use to assess an index for three parameters representing the functional status of the structure: a) condition of the structure at the opening of the stream flow, b) level of obstruction at the structure and c) the level of erosion in the stream bank. The calculation of the index for each parameter is based upon fuzzy logic theory to handle ranges in precision of the reports and to convert the linguistic rating scales into numbers representing the structure's status. A weighting method and multi-criteria method (Analytic Hierarchy Process- AHP and TOPSIS), can be used by technicians to combine the different ratings according to the component elements of the structure and the completeness of the reports. Finally, technicians can set decision rules based on the worst rating and a threshold for the functional indexes. The methodology was implemented as a prototype web-based tool to be tested with technicians of the Civil Protection in the Fella basin, Northern Italy. Results at this stage comprise the design and implementation of the web-based tool with GIS interaction to evaluate available reports and to set priorities on the inspection of structures. Keywords Decision-making, Multi-criteria methods, Torrent control structures, Web-based tools.

A Systematic Review and Summarization of the Recommendations and Research Surrounding Curriculum-Based Measurement of Oral Reading Fluency (CBM-R) Decision Rules

ERIC Educational Resources Information Center

Ardoin, Scott P.; Christ, Theodore J.; Morena, Laura S.; Cormier, Damien C.; Klingbeil, David A.

2013-01-01

Research and policy have established that data are necessary to guide decisions within education. Many of these decisions are made within problem solving and response to intervention frameworks for service delivery. Curriculum-Based Measurement in Reading (CBM-R) is a widely used data collection procedure within those models of service delivery.…

Ethical Decisions in Experience-Based Training and Development Programs.

ERIC Educational Resources Information Center

Gass, Michael A.; Wurdinger, Scott

1993-01-01

Illustrates how principle and virtue ethics can be applied to decision-making processes in experience-based training and development programs. Principle ethics is guided by predetermined rules and assumes that issues being examined are somewhat similar in context, whereas virtue ethics assumes that "correct behavior" is determined from…

Biometric image enhancement using decision rule based image fusion techniques

NASA Astrophysics Data System (ADS)

Sagayee, G. Mary Amirtha; Arumugam, S.

2010-02-01

Introducing biometrics into information systems may result in considerable benefits. Most of the researchers confirmed that the finger print is widely used than the iris or face and more over it is the primary choice for most privacy concerned applications. For finger prints applications, choosing proper sensor is at risk. The proposed work deals about, how the image quality can be improved by introducing image fusion technique at sensor levels. The results of the images after introducing the decision rule based image fusion technique are evaluated and analyzed with its entropy levels and root mean square error.

Prediction of vesicoureteral reflux after a first febrile urinary tract infection in children: validation of a clinical decision rule.

PubMed

Leroy, S; Marc, E; Adamsbaum, C; Gendrel, D; Bréart, G; Chalumeau, M

2006-03-01

To test the reproducibility of a highly sensitive clinical decision rule proposed to predict vesicoureteral reflux (VUR) after a first febrile urinary tract infection in children. This rule combines clinical (family history of uropathology, male gender, young age), biological (raised C reactive protein), and radiological (urinary tract dilation on renal ultrasound) predictors in a score, and provides 100% sensitivity. A retrospective hospital based cohort study included all children, 1 month to 4 years old, with a first febrile urinary tract infection. The sensitivities and specificities of the rule at the two previously proposed score thresholds (< or =0 and < or =5) to predict respectively, all-grade or grade > or =3 VUR, were calculated. A total of 149 children were included. VUR prevalence was 25%. The rule yielded 100% sensitivity and 3% specificity for all-grade VUR, and 93% sensitivity and 13% specificity for grade > or =3 VUR. Some methodological weaknesses explain this lack of reproducibility. The reproducibility of the previously proposed decision rule was poor and its potential contribution to clinical management of children with febrile urinary tract infection seems to be modest.

Prediction of vesicoureteral reflux after a first febrile urinary tract infection in children: validation of a clinical decision rule

PubMed Central

Leroy, S; Marc, E; Adamsbaum, C; Gendrel, D; Bréart, G; Chalumeau, M

2006-01-01

Aims To test the reproducibility of a highly sensitive clinical decision rule proposed to predict vesicoureteral reflux (VUR) after a first febrile urinary tract infection in children. This rule combines clinical (family history of uropathology, male gender, young age), biological (raised C reactive protein), and radiological (urinary tract dilation on renal ultrasound) predictors in a score, and provides 100% sensitivity. Methods A retrospective hospital based cohort study included all children, 1 month to 4 years old, with a first febrile urinary tract infection. The sensitivities and specificities of the rule at the two previously proposed score thresholds (⩽0 and ⩽5) to predict respectively, all‐grade or grade ⩾3 VUR, were calculated. Results A total of 149 children were included. VUR prevalence was 25%. The rule yielded 100% sensitivity and 3% specificity for all‐grade VUR, and 93% sensitivity and 13% specificity for grade ⩾3 VUR. Some methodological weaknesses explain this lack of reproducibility. Conclusions The reproducibility of the previously proposed decision rule was poor and its potential contribution to clinical management of children with febrile urinary tract infection seems to be modest. PMID:15890693

Decision Rules Used in Academic Program Closure: Where the Rubber Meets the Road.

ERIC Educational Resources Information Center

Eckel, Peter D.

2002-01-01

Adopted a decision/action rationality framework to explore the criteria used to close academic programs at four universities. Findings suggested that decisions are based upon criteria other than those usually stated (e.g., cost, quality, and centrality), and that process leads to criteria generation. (EV)

Hazard Avoidance Products for Convectively-Induced Turbulence in Support of High-Altitude Global Hawk Aircraft Missions

NASA Astrophysics Data System (ADS)

Griffin, Sarah M.; Velden, Christopher S.

2018-01-01

A combination of satellite-based and ground-based information is used to identify regions of intense convection that may act as a hazard to high-altitude aircraft. Motivated by concerns that Global Hawk pilotless aircraft, flying near 60,000 feet, might encounter significant convectively-induced turbulence during research overflights of tropical cyclones, strict rules were put in place to avoid such hazards. However, these rules put constraints on science missions focused on sampling convection with onboard sensors. To address these concerns, three hazard avoidance tools to aid in real-time mission decision support are used to more precisely identify areas of potential turbulence: Satellite-derived Cloud-top height and tropical overshooting tops, and ground-based global network lightning flashes. These tools are used to compare an ER-2 aircraft overflight of tropical cyclone Emily in 2005, which experienced severe turbulence, to Global Hawk overflights of tropical cyclones Karl and Matthew in 2010 that experienced no turbulence. It is found that the ER-2 overflew the lowest cloud tops and had the largest vertical separation from them compared to the Global Hawk flights. Therefore, cold cloud tops alone cannot predict turbulence. Unlike the overflights of Matthew and Karl, Emily exhibited multiple lightning flashes and a distinct overshooting top coincident with the observed turbulence. Therefore, these tools in tandem can better assist in identifying likely regions/periods of intense active convection. The primary outcome of this study is an altering of the Global Hawk overflight rules to be more flexible based on the analyzed conditions.

Toward sensor-based context aware systems.

PubMed

Sakurai, Yoshitaka; Takada, Kouhei; Anisetti, Marco; Bellandi, Valerio; Ceravolo, Paolo; Damiani, Ernesto; Tsuruta, Setsuo

2012-01-01

This paper proposes a methodology for sensor data interpretation that can combine sensor outputs with contexts represented as sets of annotated business rules. Sensor readings are interpreted to generate events labeled with the appropriate type and level of uncertainty. Then, the appropriate context is selected. Reconciliation of different uncertainty types is achieved by a simple technique that moves uncertainty from events to business rules by generating combs of standard Boolean predicates. Finally, context rules are evaluated together with the events to take a decision. The feasibility of our idea is demonstrated via a case study where a context-reasoning engine has been connected to simulated heartbeat sensors using prerecorded experimental data. We use sensor outputs to identify the proper context of operation of a system and trigger decision-making based on context information.

A work-centered cognitively based architecture for decision support: the work-centered infomediary layer (WIL) model

NASA Astrophysics Data System (ADS)

Zachary, Wayne; Eggleston, Robert; Donmoyer, Jason; Schremmer, Serge

2003-09-01

Decision-making is strongly shaped and influenced by the work context in which decisions are embedded. This suggests that decision support needs to be anchored by a model (implicit or explicit) of the work process, in contrast to traditional approaches that anchor decision support to either context free decision models (e.g., utility theory) or to detailed models of the external (e.g., battlespace) environment. An architecture for cognitively-based, work centered decision support called the Work-centered Informediary Layer (WIL) is presented. WIL separates decision support into three overall processes that build and dynamically maintain an explicit context model, use the context model to identify opportunities for decision support and tailor generic decision-support strategies to the current context and offer them to the system-user/decision-maker. The generic decision support strategies include such things as activity/attention aiding, decision process structuring, work performance support (selective, contextual automation), explanation/ elaboration, infosphere data retrieval, and what if/action-projection and visualization. A WIL-based application is a work-centered decision support layer that provides active support without intent inferencing, and that is cognitively based without requiring classical cognitive task analyses. Example WIL applications are detailed and discussed.

Governator vs. Hunter and Aggregator: A simulation of party competition with vote-seeking and office-seeking rules.

PubMed

Lehrer, Roni; Schumacher, Gijs

2018-01-01

The policy positions parties choose are central to both attracting voters and forming coalition governments. How then should parties choose positions to best represent voters? Laver and Sergenti show that in an agent-based model with boundedly rational actors a decision rule (Aggregator) that takes the mean policy position of its supporters is the best rule to achieve high congruence between voter preferences and party positions. But this result only pertains to representation by the legislature, not representation by the government. To evaluate this we add a coalition formation procedure with boundedly rational parties to the Laver and Sergenti model of party competition. We also add two new decision rules that are sensitive to government formation outcomes rather than voter positions. We develop two simulations: a single-rule one in which parties with the same rule compete and an evolutionary simulation in which parties with different rules compete. In these simulations we analyze party behavior under a large number of different parameters that describe real-world variance in political parties' motives and party system characteristics. Our most important conclusion is that Aggregators also produce the best match between government policy and voter preferences. Moreover, even though citizens often frown upon politicians' interest in the prestige and rents that come with winning political office (office pay-offs), we find that citizens actually receive better representation by the government if politicians are motivated by these office pay-offs in contrast to politicians with ideological motivations (policy pay-offs). Finally, we show that while more parties are linked to better political representation, how parties choose policy positions affects political representation as well. Overall, we conclude that to understand variation in the quality of political representation scholars should look beyond electoral systems and take into account variation in party behavior as well.

Governator vs. Hunter and Aggregator: A simulation of party competition with vote-seeking and office-seeking rules

PubMed Central

2018-01-01

The policy positions parties choose are central to both attracting voters and forming coalition governments. How then should parties choose positions to best represent voters? Laver and Sergenti show that in an agent-based model with boundedly rational actors a decision rule (Aggregator) that takes the mean policy position of its supporters is the best rule to achieve high congruence between voter preferences and party positions. But this result only pertains to representation by the legislature, not representation by the government. To evaluate this we add a coalition formation procedure with boundedly rational parties to the Laver and Sergenti model of party competition. We also add two new decision rules that are sensitive to government formation outcomes rather than voter positions. We develop two simulations: a single-rule one in which parties with the same rule compete and an evolutionary simulation in which parties with different rules compete. In these simulations we analyze party behavior under a large number of different parameters that describe real-world variance in political parties’ motives and party system characteristics. Our most important conclusion is that Aggregators also produce the best match between government policy and voter preferences. Moreover, even though citizens often frown upon politicians’ interest in the prestige and rents that come with winning political office (office pay-offs), we find that citizens actually receive better representation by the government if politicians are motivated by these office pay-offs in contrast to politicians with ideological motivations (policy pay-offs). Finally, we show that while more parties are linked to better political representation, how parties choose policy positions affects political representation as well. Overall, we conclude that to understand variation in the quality of political representation scholars should look beyond electoral systems and take into account variation in party behavior as well. PMID:29394268

The Mental Capacity Act 2005: a new framework for healthcare decision making.

PubMed

Johnston, Carolyn; Liddle, Jane

2007-02-01

The Mental Capacity Act received Royal Assent on 7 April 2005, and it will be implemented in 2007. The Act defines when someone lacks capacity and it supports people with limited decision-making ability to make as many decisions as possible for themselves. The Act lays down rules for substitute decision making. Someone taking decisions on behalf of the person lacking capacity must act in the best interests of the person concerned and choose the options least restrictive of his or her rights and freedoms. Decision making will be allowed without any formal procedure unless specific provisions apply, such as a written advance decision, lasting powers of attorney or a decision by the court of protection.

A novel association rule mining approach using TID intermediate itemset.

PubMed

Aqra, Iyad; Herawan, Tutut; Abdul Ghani, Norjihan; Akhunzada, Adnan; Ali, Akhtar; Bin Razali, Ramdan; Ilahi, Manzoor; Raymond Choo, Kim-Kwang

2018-01-01

Designing an efficient association rule mining (ARM) algorithm for multilevel knowledge-based transactional databases that is appropriate for real-world deployments is of paramount concern. However, dynamic decision making that needs to modify the threshold either to minimize or maximize the output knowledge certainly necessitates the extant state-of-the-art algorithms to rescan the entire database. Subsequently, the process incurs heavy computation cost and is not feasible for real-time applications. The paper addresses efficiently the problem of threshold dynamic updation for a given purpose. The paper contributes by presenting a novel ARM approach that creates an intermediate itemset and applies a threshold to extract categorical frequent itemsets with diverse threshold values. Thus, improving the overall efficiency as we no longer needs to scan the whole database. After the entire itemset is built, we are able to obtain real support without the need of rebuilding the itemset (e.g. Itemset list is intersected to obtain the actual support). Moreover, the algorithm supports to extract many frequent itemsets according to a pre-determined minimum support with an independent purpose. Additionally, the experimental results of our proposed approach demonstrate the capability to be deployed in any mining system in a fully parallel mode; consequently, increasing the efficiency of the real-time association rules discovery process. The proposed approach outperforms the extant state-of-the-art and shows promising results that reduce computation cost, increase accuracy, and produce all possible itemsets.

A novel association rule mining approach using TID intermediate itemset

PubMed Central

Ali, Akhtar; Bin Razali, Ramdan; Ilahi, Manzoor; Raymond Choo, Kim-Kwang

2018-01-01

Designing an efficient association rule mining (ARM) algorithm for multilevel knowledge-based transactional databases that is appropriate for real-world deployments is of paramount concern. However, dynamic decision making that needs to modify the threshold either to minimize or maximize the output knowledge certainly necessitates the extant state-of-the-art algorithms to rescan the entire database. Subsequently, the process incurs heavy computation cost and is not feasible for real-time applications. The paper addresses efficiently the problem of threshold dynamic updation for a given purpose. The paper contributes by presenting a novel ARM approach that creates an intermediate itemset and applies a threshold to extract categorical frequent itemsets with diverse threshold values. Thus, improving the overall efficiency as we no longer needs to scan the whole database. After the entire itemset is built, we are able to obtain real support without the need of rebuilding the itemset (e.g. Itemset list is intersected to obtain the actual support). Moreover, the algorithm supports to extract many frequent itemsets according to a pre-determined minimum support with an independent purpose. Additionally, the experimental results of our proposed approach demonstrate the capability to be deployed in any mining system in a fully parallel mode; consequently, increasing the efficiency of the real-time association rules discovery process. The proposed approach outperforms the extant state-of-the-art and shows promising results that reduce computation cost, increase accuracy, and produce all possible itemsets. PMID:29351287

The prefabricated building risk decision research of DM technology on the basis of Rough Set

NASA Astrophysics Data System (ADS)

Guo, Z. L.; Zhang, W. B.; Ma, L. H.

2017-08-01

With the resources crises and more serious pollution, the green building has been strongly advocated by most countries and become a new building style in the construction field. Compared with traditional building, the prefabricated building has its own irreplaceable advantages but is influenced by many uncertainties. So far, a majority of scholars have been studying based on qualitative researches from all of the word. This paper profoundly expounds its significance about the prefabricated building. On the premise of the existing research methods, combined with rough set theory, this paper redefines the factors which affect the prefabricated building risk. Moreover, it quantifies risk factors and establish an expert knowledge base through assessing. And then reduced risk factors about the redundant attributes and attribute values, finally form the simplest decision rule. This simplest decision rule, which is based on the DM technology of rough set theory, provides prefabricated building with a controllable new decision-making method.

Decision Rules and Group Rationality: Cognitive Gain or Standstill?

PubMed Central

Curşeu, Petru Lucian; Jansen, Rob J. G.; Chappin, Maryse M. H.

2013-01-01

Recent research in group cognition points towards the existence of collective cognitive competencies that transcend individual group members’ cognitive competencies. Since rationality is a key cognitive competence for group decision making, and group cognition emerges from the coordination of individual cognition during social interactions, this study tests the extent to which collaborative and consultative decision rules impact the emergence of group rationality. Using a set of decision tasks adapted from the heuristics and biases literature, we evaluate rationality as the extent to which individual choices are aligned with a normative ideal. We further operationalize group rationality as cognitive synergy (the extent to which collective rationality exceeds average or best individual rationality in the group), and we test the effect of collaborative and consultative decision rules in a sample of 176 groups. Our results show that the collaborative decision rule has superior synergic effects as compared to the consultative decision rule. The ninety one groups working in a collaborative fashion made more rational choices (above and beyond the average rationality of their members) than the eighty five groups working in a consultative fashion. Moreover, the groups using a collaborative decision rule were closer to the rationality of their best member than groups using consultative decision rules. Nevertheless, on average groups did not outperformed their best member. Therefore, our results reveal how decision rules prescribing interpersonal interactions impact on the emergence of collective cognitive competencies. They also open potential venues for further research on the emergence of collective rationality in human decision-making groups. PMID:23451050

Decision rules and group rationality: cognitive gain or standstill?

PubMed

Curşeu, Petru Lucian; Jansen, Rob J G; Chappin, Maryse M H

2013-01-01

Recent research in group cognition points towards the existence of collective cognitive competencies that transcend individual group members' cognitive competencies. Since rationality is a key cognitive competence for group decision making, and group cognition emerges from the coordination of individual cognition during social interactions, this study tests the extent to which collaborative and consultative decision rules impact the emergence of group rationality. Using a set of decision tasks adapted from the heuristics and biases literature, we evaluate rationality as the extent to which individual choices are aligned with a normative ideal. We further operationalize group rationality as cognitive synergy (the extent to which collective rationality exceeds average or best individual rationality in the group), and we test the effect of collaborative and consultative decision rules in a sample of 176 groups. Our results show that the collaborative decision rule has superior synergic effects as compared to the consultative decision rule. The ninety one groups working in a collaborative fashion made more rational choices (above and beyond the average rationality of their members) than the eighty five groups working in a consultative fashion. Moreover, the groups using a collaborative decision rule were closer to the rationality of their best member than groups using consultative decision rules. Nevertheless, on average groups did not outperformed their best member. Therefore, our results reveal how decision rules prescribing interpersonal interactions impact on the emergence of collective cognitive competencies. They also open potential venues for further research on the emergence of collective rationality in human decision-making groups.

FUDS Military Munitions Response Program

DTIC Science & Technology

2010-06-01

supporting decision rules - Phytoremediation of Arsenic -Advanced EMI and Multi-component Sensors (4 types) -Advanced Anomaly Classifications (4 types...Culebra, PR  Frankford Arsenal , PA  Orlando Range and Chemical Yard, FL  Pinecastle Jeep Range, FL  Spring Valley, DC  Waikoloa Maneuver

Design of a decision-support architecture for management of remotely monitored patients.

PubMed

Basilakis, Jim; Lovell, Nigel H; Redmond, Stephen J; Celler, Branko G

2010-09-01

Telehealth is the provision of health services at a distance. Typically, this occurs in unsupervised or remote environments, such as a patient's home. We describe one such telehealth system and the integration of extracted clinical measurement parameters with a decision-support system (DSS). An enterprise application-server framework, combined with a rules engine and statistical analysis tools, is used to analyze the acquired telehealth data, searching for trends and shifts in parameter values, as well as identifying individual measurements that exceed predetermined or adaptive thresholds. An overarching business process engine is used to manage the core DSS knowledge base and coordinate workflow outputs of the DSS. The primary role for such a DSS is to provide an effective means to reduce the data overload and to provide a means of health risk stratification to allow appropriate targeting of clinical resources to best manage the health of the patient. In this way, the system may ultimately influence changes in workflow by targeting scarce clinical resources to patients of most need. A single case study extracted from an initial pilot trial of the system, in patients with chronic obstructive pulmonary disease and chronic heart failure, will be reviewed to illustrate the potential benefit of integrating telehealth and decision support in the management of both acute and chronic disease.

Induction of belief decision trees from data

NASA Astrophysics Data System (ADS)

AbuDahab, Khalil; Xu, Dong-ling; Keane, John

2012-09-01

In this paper, a method for acquiring belief rule-bases by inductive inference from data is described and evaluated. Existing methods extract traditional rules inductively from data, with consequents that are believed to be either 100% true or 100% false. Belief rules can capture uncertain or incomplete knowledge using uncertain belief degrees in consequents. Instead of using singled-value consequents, each belief rule deals with a set of collectively exhaustive and mutually exclusive consequents. The proposed method extracts belief rules from data which contain uncertain or incomplete knowledge.

Integration of Hospital Information and Clinical Decision Support Systems to Enable the Reuse of Electronic Health Record Data.

PubMed

Kopanitsa, Georgy

2017-05-18

The efficiency and acceptance of clinical decision support systems (CDSS) can increase if they reuse medical data captured during health care delivery. High heterogeneity of the existing legacy data formats has become the main barrier for the reuse of data. Thus, we need to apply data modeling mechanisms that provide standardization, transformation, accumulation and querying medical data to allow its reuse. In this paper, we focus on the interoperability issues of the hospital information systems (HIS) and CDSS data integration. Our study is based on the approach proposed by Marcos et al. where archetypes are used as a standardized mechanism for the interaction of a CDSS with an electronic health record (EHR). We build an integration tool to enable CDSSs collect data from various institutions without a need for modifications in the implementation. The approach implies development of a conceptual level as a set of archetypes representing concepts required by a CDSS. Treatment case data from Regional Clinical Hospital in Tomsk, Russia was extracted, transformed and loaded to the archetype database of a clinical decision support system. Test records' normalization has been performed by defining transformation and aggregation rules between the EHR data and the archetypes. These mapping rules were used to automatically generate openEHR compliant data. After the transformation, archetype data instances were loaded into the CDSS archetype based data storage. The performance times showed acceptable performance for the extraction stage with a mean of 17.428 s per year (3436 case records). The transformation times were also acceptable with 136.954 s per year (0.039 s per one instance). The accuracy evaluation showed the correctness and applicability of the method for the wide range of HISes. These operations were performed without interrupting the HIS workflow to prevent the HISes from disturbing the service provision to the users. The project results have proven that archetype based technologies are mature enough to be applied in routine operations that require extraction, transformation, loading and querying medical data from heterogeneous EHR systems. Inference models in clinical research and CDSS can benefit from this by defining queries to a valid data set with known structure and constraints. The standard based nature of the archetype approach allows an easy integration of CDSSs with existing EHR systems.

46 CFR 201.3 - Authentication of rules, orders, determinations and decisions of the Administration.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 46 Shipping 8 2010-10-01 2010-10-01 false Authentication of rules, orders, determinations and decisions of the Administration. 201.3 Section 201.3 Shipping MARITIME ADMINISTRATION, DEPARTMENT OF....3 Authentication of rules, orders, determinations and decisions of the Administration. All rules...

Collaborative Brain-Computer Interface for Aiding Decision-Making

PubMed Central

Poli, Riccardo; Valeriani, Davide; Cinel, Caterina

2014-01-01

We look at the possibility of integrating the percepts from multiple non-communicating observers as a means of achieving better joint perception and better group decisions. Our approach involves the combination of a brain-computer interface with human behavioural responses. To test ideas in controlled conditions, we asked observers to perform a simple matching task involving the rapid sequential presentation of pairs of visual patterns and the subsequent decision as whether the two patterns in a pair were the same or different. We recorded the response times of observers as well as a neural feature which predicts incorrect decisions and, thus, indirectly indicates the confidence of the decisions made by the observers. We then built a composite neuro-behavioural feature which optimally combines the two measures. For group decisions, we uses a majority rule and three rules which weigh the decisions of each observer based on response times and our neural and neuro-behavioural features. Results indicate that the integration of behavioural responses and neural features can significantly improve accuracy when compared with the majority rule. An analysis of event-related potentials indicates that substantial differences are present in the proximity of the response for correct and incorrect trials, further corroborating the idea of using hybrids of brain-computer interfaces and traditional strategies for improving decision making. PMID:25072739

Environmental impact assessment of transportation projects: An analysis using an integrated GIS, remote sensing, and spatial modeling approach

NASA Astrophysics Data System (ADS)

El-Gafy, Mohamed Anwar

Transportation projects will have impact on the environment. The general environmental pollution and damage caused by roads is closely associated with the level of economic activity. Although Environmental Impact Assessments (EIAs) are dependent on geo-spatial information in order to make an assessment, there are no rules per se how to conduct an environmental assessment. Also, the particular objective of each assessment is dictated case-by-case, based on what information and analyses are required. The conventional way of Environmental Impact Assessment (EIA) study is a time consuming process because it has large number of dependent and independent variables which have to be taken into account, which also have different consequences. With the emergence of satellite remote sensing technology and Geographic Information Systems (GIS), this research presents a new framework for the analysis phase of the Environmental Impact Assessment (EIA) for transportation projects based on the integration between remote sensing technology, geographic information systems, and spatial modeling. By integrating the merits of the map overlay method and the matrix method, the framework analyzes comprehensively the environmental vulnerability around the road and its impact on the environment. This framework is expected to: (1) improve the quality of the decision making process, (2) be applied both to urban and inter-urban projects, regardless of transport mode, and (3) present the data and make the appropriate analysis to support the decision of the decision-makers and allow them to present these data to the public hearings in a simple manner. Case studies, transportation projects in the State of Florida, were analyzed to illustrate the use of the decision support framework and demonstrate its capabilities. This cohesive and integrated system will facilitate rational decisions through cost effective coordination of environmental information and data management that can be tailored to specific projects. The framework would facilitate collecting, organizing, analyzing, archiving, and coordinating the information and data necessary to support technical and policy transportation decisions.

Cost-effectiveness thresholds: pros and cons.

PubMed

Bertram, Melanie Y; Lauer, Jeremy A; De Joncheere, Kees; Edejer, Tessa; Hutubessy, Raymond; Kieny, Marie-Paule; Hill, Suzanne R

2016-12-01

Cost-effectiveness analysis is used to compare the costs and outcomes of alternative policy options. Each resulting cost-effectiveness ratio represents the magnitude of additional health gained per additional unit of resources spent. Cost-effectiveness thresholds allow cost-effectiveness ratios that represent good or very good value for money to be identified. In 2001, the World Health Organization's Commission on Macroeconomics in Health suggested cost-effectiveness thresholds based on multiples of a country's per-capita gross domestic product (GDP). In some contexts, in choosing which health interventions to fund and which not to fund, these thresholds have been used as decision rules. However, experience with the use of such GDP-based thresholds in decision-making processes at country level shows them to lack country specificity and this - in addition to uncertainty in the modelled cost-effectiveness ratios - can lead to the wrong decision on how to spend health-care resources. Cost-effectiveness information should be used alongside other considerations - e.g. budget impact and feasibility considerations - in a transparent decision-making process, rather than in isolation based on a single threshold value. Although cost-effectiveness ratios are undoubtedly informative in assessing value for money, countries should be encouraged to develop a context-specific process for decision-making that is supported by legislation, has stakeholder buy-in, for example the involvement of civil society organizations and patient groups, and is transparent, consistent and fair.

Democratization in the Arab World: Prospects and Lessons from Around the Globe

DTIC Science & Technology

2012-01-01

Zagreb : Konrad-Adenauer-Stiftung, 2007. Egyptian Decision and Information Support Center The Role of Religion in Egyptian Society, June 2011 (in Arabic...in V. Dvořáková and A. Milardović, eds., Lustration and Consolidation of Democracy and the Rule of Law in Central and Eastern Europe, Zagreb ...Lustration and Consolidation of Democracy and the Rule of Law in Central and Eastern Europe, Zagreb : Konrad-Adenauer-Stiftung, 2007. Pew Research

Backlash or a Positive Response?: Public Opinion of LGB Issues After Obergefell v. Hodges.

PubMed

Kazyak, Emily; Stange, Mathew

2018-01-10

Following Obergefell v. Hodges, same-sex marriage remains controversial and anti-LGBT state legislation has been passed, which raises questions about whether the Supreme Court's ruling may have created a backlash. We use data from two waves of a general population survey of Nebraskans conducted before and after the decision to answer three questions. First, we test three theories of how the court decision influenced public opinion. We find that support for same-sex marriage was significantly higher following the ruling, suggesting that there was not a backlash to it. Second, we assess whether people perceive that the court accurately reflects the public's opinion. We find that people who favor same-sex marriage are more likely to think that the ruling refects public opinion very well; those who oppose same-sex marriage are more likely to think that the ruling does not at all reflect public opinion. Third, we examine the association between discussing gay rights and support for same-sex marriage, finding that those who talk about LGB issues very often are more likely to favor same-sex marriage. We discuss the implications of these findings in relation to two of the themes of this special issue: the influence of marriage equality on Americans' understandings of marriage and the impact of marriage equality on future LGBT activism.

18 CFR 385.602 - Submission of settlement offers (Rule 602).

Code of Federal Regulations, 2010 CFR

2010-04-01

... settlement issues, if the record contains substantial evidence upon which to base a reasoned decision or the... reasonably be based; or (B) Take other action which the Commission determines to be appropriate. (iii) If... contains substantial evidence from which the Commission may reach a reasoned decision on the merits of the...

iRENEX: a clinically informed decision support system for the interpretation of ⁹⁹mTc-MAG3 scans to detect renal obstruction.

PubMed

Garcia, Ernest V; Taylor, Andrew; Folks, Russell; Manatunga, Daya; Halkar, Raghuveer; Savir-Baruch, Bital; Dubovsky, Eva

2012-09-01

Decision support systems for imaging analysis and interpretation are rapidly being developed and will have an increasing impact on the practice of medicine. RENEX is a renal expert system to assist physicians evaluate suspected obstruction in patients undergoing mercaptoacetyltriglycine (MAG3) renography. RENEX uses quantitative parameters extracted from the dynamic renal scan data using QuantEM™II and heuristic rules in the form of a knowledge base gleaned from experts to determine if a kidney is obstructed; however, RENEX does not have access to and could not consider the clinical information available to diagnosticians interpreting these studies. We designed and implemented a methodology to incorporate clinical information into RENEX, implemented motion detection and evaluated this new comprehensive system (iRENEX) in a pilot group of 51 renal patients. To reach a conclusion as to whether a kidney is obstructed, 56 new clinical rules were added to the previously reported 60 rules used to interpret quantitative MAG3 parameters. All the clinical rules were implemented after iRENEX reached a conclusion on obstruction based on the quantitative MAG3 parameters, and the evidence of obstruction was then modified by the new clinical rules. iRENEX consisted of a library to translate parameter values to certainty factors, a knowledge base with 116 heuristic interpretation rules, a forward chaining inference engine to determine obstruction and a justification engine. A clinical database was developed containing patient histories and imaging report data obtained from the hospital information system associated with the pertinent MAG3 studies. The system was fine-tuned and tested using a pilot group of 51 patients (21 men, mean age 58.2 ± 17.1 years, 100 kidneys) deemed by an expert panel to have 61 unobstructed and 39 obstructed kidneys. iRENEX, using only quantitative MAG3 data agreed with the expert panel in 87 % (34/39) of obstructed and 90 % (55/61) of unobstructed kidneys. iRENEX, using both quantitative and clinical data agreed with the expert panel in 95 % (37/39) of obstructed and 92 % (56/61) of unobstructed kidneys. The clinical information significantly (p < 0.001) increased iRENEX certainty in detecting obstruction over using the quantitative data alone. Our renal expert system for detecting renal obstruction has been substantially expanded to incorporate the clinical information available to physicians as well as advanced quality control features and was shown to interpret renal studies in a pilot group at a standardized expert level. These encouraging results warrant a prospective study in a large population of patients with and without renal obstruction to establish the diagnostic performance of iRENEX.

Residential water demand model under block rate pricing: A case study of Beijing, China

NASA Astrophysics Data System (ADS)

Chen, H.; Yang, Z. F.

2009-05-01

In many cities, the inconsistency between water supply and water demand has become a critical problem because of deteriorating water shortage and increasing water demand. Uniform price of residential water cannot promote the efficient water allocation. In China, block water price will be put into practice in the future, but the outcome of such regulation measure is unpredictable without theory support. In this paper, the residential water is classified by the volume of water usage based on economic rules and block water is considered as different kinds of goods. A model based on extended linear expenditure system (ELES) is constructed to simulate the relationship between block water price and water demand, which provide theoretical support for the decision-makers. Finally, the proposed model is used to simulate residential water demand under block rate pricing in Beijing.

Ontology based decision system for breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Trabelsi Ben Ameur, Soumaya; Cloppet, Florence; Wendling, Laurent; Sellami, Dorra

2018-04-01

In this paper, we focus on analysis and diagnosis of breast masses inspired by expert concepts and rules. Accordingly, a Bag of Words is built based on the ontology of breast cancer diagnosis, accurately described in the Breast Imaging Reporting and Data System. To fill the gap between low level knowledge and expert concepts, a semantic annotation is developed using a machine learning tool. Then, breast masses are classified into benign or malignant according to expert rules implicitly modeled with a set of classifiers (KNN, ANN, SVM and Decision Tree). This semantic context of analysis offers a frame where we can include external factors and other meta-knowledge such as patient risk factors as well as exploiting more than one modality. Based on MRI and DECEDM modalities, our developed system leads a recognition rate of 99.7% with Decision Tree where an improvement of 24.7 % is obtained owing to semantic analysis.

Extraction of decision rules via imprecise probabilities

NASA Astrophysics Data System (ADS)

Abellán, Joaquín; López, Griselda; Garach, Laura; Castellano, Javier G.

2017-05-01

Data analysis techniques can be applied to discover important relations among features. This is the main objective of the Information Root Node Variation (IRNV) technique, a new method to extract knowledge from data via decision trees. The decision trees used by the original method were built using classic split criteria. The performance of new split criteria based on imprecise probabilities and uncertainty measures, called credal split criteria, differs significantly from the performance obtained using the classic criteria. This paper extends the IRNV method using two credal split criteria: one based on a mathematical parametric model, and other one based on a non-parametric model. The performance of the method is analyzed using a case study of traffic accident data to identify patterns related to the severity of an accident. We found that a larger number of rules is generated, significantly supplementing the information obtained using the classic split criteria.

Learning stage-dependent effect of M1 disruption on value-based motor decisions.

PubMed

Derosiere, Gerard; Vassiliadis, Pierre; Demaret, Sophie; Zénon, Alexandre; Duque, Julie

2017-11-15

The present study aimed at characterizing the impact of M1 disruption on the implementation of implicit value information in motor decisions, at both early stages (during reinforcement learning) and late stages (after consolidation) of action value encoding. Fifty subjects performed, over three consecutive days, a task that required them to select between two finger responses according to the color (instruction) and to the shape (implicit, undisclosed rule) of an imperative signal: considering the implicit rule in addition to the instruction allowed subjects to earn more money. We investigated the functional contribution of M1 to the implementation of the implicit rule in subjects' motor decisions. Continuous theta burst stimulation (cTBS) was applied over M1 either on Day 1 or on Day 3, producing a temporary lesion either during reinforcement learning (cTBS Learning group) or after consolidation of the implicit rule, during decision-making (cTBS Decision group), respectively. Interestingly, disrupting M1 activity on Day 1 improved the reliance on the implicit rule, plausibly because M1 cTBS increased dopamine release in the putamen in an indirect way. This finding corroborates the view that cTBS may affect activity in unstimulated areas, such as the basal ganglia. Notably, this effect was short-lasting; it did not persist overnight, suggesting that the functional integrity of M1 during learning is a prerequisite for the consolidation of implicit value information to occur. Besides, cTBS over M1 did not impact the use of the implicit rule when applied on Day 3, although it did so when applied on Day 2 in a recent study where the reliance on the implicit rule declined following cTBS (Derosiere et al., 2017). Overall, these findings indicate that the human M1 is functionally involved in the consolidation and implementation of implicit value information underlying motor decisions. However, M1 contribution seems to vanish as subjects become more experienced in using the implicit value information to make their motor decisions. Copyright © 2017 Elsevier Inc. All rights reserved.

The neural basis for novel semantic categorization.

PubMed

Koenig, Phyllis; Smith, Edward E; Glosser, Guila; DeVita, Chris; Moore, Peachie; McMillan, Corey; Gee, Jim; Grossman, Murray

2005-01-15

We monitored regional cerebral activity with BOLD fMRI during acquisition of a novel semantic category and subsequent categorization of test stimuli by a rule-based strategy or a similarity-based strategy. We observed different patterns of activation in direct comparisons of rule- and similarity-based categorization. During rule-based category acquisition, subjects recruited anterior cingulate, thalamic, and parietal regions to support selective attention to perceptual features, and left inferior frontal cortex to helps maintain rules in working memory. Subsequent rule-based categorization revealed anterior cingulate and parietal activation while judging stimuli whose conformity with the rules was readily apparent, and left inferior frontal recruitment during judgments of stimuli whose conformity was less apparent. By comparison, similarity-based category acquisition showed recruitment of anterior prefrontal and posterior cingulate regions, presumably to support successful retrieval of previously encountered exemplars from long-term memory, and bilateral temporal-parietal activation for perceptual feature integration. Subsequent similarity-based categorization revealed temporal-parietal, posterior cingulate, and anterior prefrontal activation. These findings suggest that large-scale networks support relatively distinct categorization processes during the acquisition and judgment of semantic category knowledge.

A Simulation Approach to Decision Making in IT Service Strategy

PubMed Central

2014-01-01

We propose to use simulation modeling to support decision making in IT service strategy scope. Our main contribution is a simulation model that helps service providers analyze the consequences of changes in both the service capacity assigned to their customers and the tendency of service requests received on the fulfillment of a business rule associated with the strategic goal of customer satisfaction. This business rule is set in the SLAs that service provider and its customers agree to, which determine the maximum percentage of service requests that are permitted to be abandoned because they have exceeded the waiting time allowed. To illustrate the use and applications of the model, we include some of the experiments conducted and describe our conclusions. PMID:24790583

Improving the human readability of Arden Syntax medical logic modules using a concept-oriented terminology and object-oriented programming expressions.

PubMed

Choi, Jeeyae; Bakken, Suzanne; Lussier, Yves A; Mendonça, Eneida A

2006-01-01

Medical logic modules are a procedural representation for sharing task-specific knowledge for decision support systems. Based on the premise that clinicians may perceive object-oriented expressions as easier to read than procedural rules in Arden Syntax-based medical logic modules, we developed a method for improving the readability of medical logic modules. Two approaches were applied: exploiting the concept-oriented features of the Medical Entities Dictionary and building an executable Java program to replace Arden Syntax procedural expressions. The usability evaluation showed that 66% of participants successfully mapped all Arden Syntax rules to Java methods. These findings suggest that these approaches can play an essential role in the creation of human readable medical logic modules and can potentially increase the number of clinical experts who are able to participate in the creation of medical logic modules. Although our approaches are broadly applicable, we specifically discuss the relevance to concept-oriented nursing terminologies and automated processing of task-specific nursing knowledge.

Bioethical aspects of end-of-life care.

PubMed

Zamperetti, N; Bellomo, R; Ronco, C

2008-01-01

Managing end-of-life care can be difficult because of the particular nature of intensive care support, which can separate the biological and the biographical aspects of life. Artificial organ support can temporarily delay death but, at the same time, may fail to restore a quality of life that the patient judges acceptable. For this reason, two concepts must be considered: that the mission of the healthcare system should be to care for patients according to their interests and wishes and that quality of care is related above all to the careful commitment of healthcare workers to the patient's best interests. Keeping these concepts in mind, the rule of the five Cs (competence, collegiality, communication, continuity of care and compassion) might be helpful in the management of end-of-life care. Unfortunately, neither the rule of the five Cs nor the careful use of moral principles in order to promote the patients' dignity can assure a universally acceptable decision. A reasonable level of 'moral certainty', however, might be achieved using a deliberative approach, which provides for the inclusion of all the different subjects involved in the decision-making process (patient, family, doctors, nurses and other carers), in order to reach the best possible decision in a specific situation.

Nursing process decision support system for urology ward.

PubMed

Hao, Angelica Te-Hui; Wu, Lee-Pin; Kumar, Ajit; Jian, Wen-Shan; Huang, Li-Fang; Kao, Ching-Chiu; Hsu, Chien-Yeh

2013-07-01

We developed a nursing process decision support system (NPDSS) based on three clinical pathways, including benign prostatic hypertrophy, inguinal hernia, and urinary tract stone. NPDSS included six major nursing diagnoses - acute pain, impaired urinary elimination, impaired skin integrity, anxiety, infection risk, and risk of falling. This paper aims to describe the design, development and validation process of the NPDSS. We deployed the Delphi method to reach consensus for decision support rules of NPDSS. A team of nine-member expert nurses from a medical center in Taiwan was involved in Delphi method. The Cronbach's α method was used for examining the reliability of the questionnaire used in the Delphi method. The Visual Basic 6.0 as front-end and Microsoft Access 2003 as back-end was used to develop the system. A team of six nursing experts was asked to evaluate the usability of the developed systems. A 5-point Likert scale questionnaire was used for the evaluation. The sensitivity and specificity of NPDSS were validated using 150 nursing chart. The study showed a consistency between the diagnoses of the developed system (NPDSS) and the nursing charts. The sensitivities of the nursing diagnoses including acute pain, impaired urinary elimination, risk of infection, and risk of falling were 96.9%, 98.1%, 94.9%, and 89.9% respectively; and the specificities were 88%, 49.5%, 62%, and 88% respectively. We did not calculate the sensitivity and specificity of impaired skin integrity and anxiety due to non-availability of enough sample size. NPDSS can help nurses in decision making of nursing diagnoses. Besides, it can help them to generate nursing diagnoses based on patient-specific data, individualized care plans, and implementation within their usual nursing workflow. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

The analysis of rapidly developing fog at the Kennedy Space Center

NASA Technical Reports Server (NTRS)

Wheeler, Mark M.; Atchison, Michael K.; Schumann, Robin; Taylor, Greg E.; Yersavich, Ann; Warburton, John D.

1994-01-01

This report documents fog precursors and fog climatology at Kennedy Space Center (KSC) Florida from 1986 to 1990. The major emphasis of this report focuses on rapidly developing fog events that would affect the less than 7-statute mile visibility rule for End-Of-Mission (EOM) Shuttle landing at KSC (Rule 4-64(A)). The Applied Meteorology Unit's (AMU's) work is to: develop a data base for study of fog associated weather conditions relating to violations of this landing constraint; develop forecast techniques or rules-of-thumb to determine whether or not current conditions are likely to result in an acceptable condition at landing; validate the forecast techniques; and transition techniques to operational use. As part of the analysis the fog events were categorized as either advection, pre-frontal or radiation. As a result of these analyses, the AMU developed a fog climatological data base, identified fog precursors and developed forecaster tools and decision trees. The fog climatological analysis indicates that during the fog season (October to April) there is a higher risk for a visibility violation at KSC during the early morning hours (0700 to 1200 UTC), while 95 percent of all fog events have dissipated by 1600 UTC. A high number of fog events are characterized by a westerly component to the surface wind at KSC (92 percent) and 83 percent of the fog events had fog develop west of KSC first (up to 2 hours). The AMU developed fog decision trees and forecaster tools that would help the forecaster identify fog precursors up to 12 hours in advance. Using the decision trees as process tools ensures the important meteorological data are not overlooked in the forecast process. With these tools and a better understanding of fog formation in the local KSC area, the Shuttle weather support forecaster should be able to give the Launch and Flight Directors a better KSC fog forecast with more confidence.

To t-Test or Not to t-Test? A p-Values-Based Point of View in the Receiver Operating Characteristic Curve Framework.

PubMed

Vexler, Albert; Yu, Jihnhee

2018-04-13

A common statistical doctrine supported by many introductory courses and textbooks is that t-test type procedures based on normally distributed data points are anticipated to provide a standard in decision-making. In order to motivate scholars to examine this convention, we introduce a simple approach based on graphical tools of receiver operating characteristic (ROC) curve analysis, a well-established biostatistical methodology. In this context, we propose employing a p-values-based method, taking into account the stochastic nature of p-values. We focus on the modern statistical literature to address the expected p-value (EPV) as a measure of the performance of decision-making rules. During the course of our study, we extend the EPV concept to be considered in terms of the ROC curve technique. This provides expressive evaluations and visualizations of a wide spectrum of testing mechanisms' properties. We show that the conventional power characterization of tests is a partial aspect of the presented EPV/ROC technique. We desire that this explanation of the EPV/ROC approach convinces researchers of the usefulness of the EPV/ROC approach for depicting different characteristics of decision-making procedures, in light of the growing interest regarding correct p-values-based applications.

CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation.

PubMed

Whiffin, Nicola; Walsh, Roddy; Govind, Risha; Edwards, Matthew; Ahmad, Mian; Zhang, Xiaolei; Tayal, Upasana; Buchan, Rachel; Midwinter, William; Wilk, Alicja E; Najgebauer, Hanna; Francis, Catherine; Wilkinson, Sam; Monk, Thomas; Brett, Laura; O'Regan, Declan P; Prasad, Sanjay K; Morris-Rosendahl, Deborah J; Barton, Paul J R; Edwards, Elizabeth; Ware, James S; Cook, Stuart A

2018-01-25

PurposeInternationally adopted variant interpretation guidelines from the American College of Medical Genetics and Genomics (ACMG) are generic and require disease-specific refinement. Here we developed CardioClassifier (http://www.cardioclassifier.org), a semiautomated decision-support tool for inherited cardiac conditions (ICCs).MethodsCardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, to support variant interpretation. Combining disease- and gene-specific knowledge with variant observations in large cohorts of cases and controls, we refined 14 computational ACMG criteria and created three ICC-specific rules.ResultsWe benchmarked CardioClassifier on 57 expertly curated variants and show full retrieval of all computational data, concordantly activating 87.3% of rules. A generic annotation tool identified fewer than half as many clinically actionable variants (64/219 vs. 156/219, Fisher's P = 1.1  ×  10 -18 ), with important false positives, illustrating the critical importance of disease and gene-specific annotations. CardioClassifier identified putatively disease-causing variants in 33.7% of 327 cardiomyopathy cases, comparable with leading ICC laboratories. Through addition of manually curated data, variants found in over 40% of cardiomyopathy cases are fully annotated, without requiring additional user-input data.ConclusionCardioClassifier is an ICC-specific decision-support tool that integrates expertly curated computational annotations with case-specific data to generate fast, reproducible, and interactive variant pathogenicity reports, according to best practice guidelines.GENETICS in MEDICINE advance online publication, 25 January 2018; doi:10.1038/gim.2017.258.

Computational mate choice: theory and empirical evidence.

PubMed

Castellano, Sergio; Cadeddu, Giorgia; Cermelli, Paolo

2012-06-01

The present review is based on the thesis that mate choice results from information-processing mechanisms governed by computational rules and that, to understand how females choose their mates, we should identify which are the sources of information and how they are used to make decisions. We describe mate choice as a three-step computational process and for each step we present theories and review empirical evidence. The first step is a perceptual process. It describes the acquisition of evidence, that is, how females use multiple cues and signals to assign an attractiveness value to prospective mates (the preference function hypothesis). The second step is a decisional process. It describes the construction of the decision variable (DV), which integrates evidence (private information by direct assessment), priors (public information), and value (perceived utility) of prospective mates into a quantity that is used by a decision rule (DR) to produce a choice. We make the assumption that females are optimal Bayesian decision makers and we derive a formal model of DV that can explain the effects of preference functions, mate copying, social context, and females' state and condition on the patterns of mate choice. The third step of mating decision is a deliberative process that depends on the DRs. We identify two main categories of DRs (absolute and comparative rules), and review the normative models of mate sampling tactics associated to them. We highlight the limits of the normative approach and present a class of computational models (sequential-sampling models) that are based on the assumption that DVs accumulate noisy evidence over time until a decision threshold is reached. These models force us to rethink the dichotomy between comparative and absolute decision rules, between discrimination and recognition, and even between rational and irrational choice. Since they have a robust biological basis, we think they may represent a useful theoretical tool for behavioural ecologist interested in integrating proximate and ultimate causes of mate choice. Copyright © 2012 Elsevier B.V. All rights reserved.

Feasibility of automatic evaluation of clinical rules in general practice.

PubMed

Opondo, Dedan; Visscher, Stefan; Eslami, Saied; Medlock, Stephanie; Verheij, Robert; Korevaar, Joke C; Abu-Hanna, Ameen

2017-04-01

To assess the extent to which clinical rules (CRs) can be implemented for automatic evaluation of quality of care in general practice. We assessed 81 clinical rules (CRs) adapted from a subset of Assessing Care of Vulnerable Elders (ACOVE) clinical rules, against Dutch College of General Practitioners (NHG) data model. Each CR was analyzed using the Logical Elements Rule METHOD: (LERM). LERM is a stepwise method of assessing and formalizing clinical rules for decision support. Clinical rules that satisfied the criteria outlined in the LERM method were judged to be implementable in automatic evaluation in general practice. Thirty-three out of 81 (40.7%) Dutch-translated ACOVE clinical rules can be automatically evaluated in electronic medical record systems. Seven out of 7 CRs (100%) in the domain of diabetes can be automatically evaluated, 9/17 (52.9%) in medication use, 5/10 (50%) in depression care, 3/6 (50%) in nutrition care, 6/13 (46.1%) in dementia care, 1/6 (16.6%) in end of life care, 2/13 (15.3%) in continuity of care, and 0/9 (0%) in the fall-related care. Lack of documentation of care activities between primary and secondary health facilities and ambiguous formulation of clinical rules were the main reasons for the inability to automate the clinical rules. Approximately two-fifths of the primary care Dutch ACOVE-based clinical rules can be automatically evaluated. Clear definition of clinical rules, improved GP database design and electronic linkage of primary and secondary healthcare facilities can improve prospects of automatic assessment of quality of care. These findings are relevant especially because the Netherlands has very high automation of primary care. Copyright © 2017 Elsevier B.V. All rights reserved.

SIRE: A Simple Interactive Rule Editor for NICBES

NASA Technical Reports Server (NTRS)

Bykat, Alex

1988-01-01

To support evolution of domain expertise, and its representation in an expert system knowledge base, a user-friendly rule base editor is mandatory. The Nickel Cadmium Battery Expert System (NICBES), a prototype of an expert system for the Hubble Space Telescope power storage management system, does not provide such an editor. In the following, a description of a Simple Interactive Rule Base Editor (SIRE) for NICBES is described. The SIRE provides a consistent internal representation of the NICBES knowledge base. It supports knowledge presentation and provides a user-friendly and code language independent medium for rule addition and modification. The SIRE is integrated with NICBES via an interface module. This module provides translation of the internal representation to Prolog-type rules (Horn clauses), latter rule assertion, and a simple mechanism for rule selection for its Prolog inference engine.

Sideline coverage: when to get radiographs? A review of clinical decision tools.

PubMed

Gould, Sara J; Cardone, Dennis A; Munyak, John; Underwood, Philipp J; Gould, Stephen A

2014-05-01

Sidelines coverage presents unique challenges in the evaluation of injured athletes. Health care providers may be confronted with the question of when to obtain radiographs following an injury. Given that most sidelines coverage occurs outside the elite level, radiographs are not readily available at the time of injury, and the decision of when to send a player for radiographs must be made based on physical examination. Clinical tools have been developed to aid in identifying injuries that are likely to result in radiographically important fractures or dislocations. A search for the keywords x-ray and decision rule along with the anatomic locations shoulder, elbow, wrist, knee, and ankle was performed using the PubMed database. No limits were set regarding year of publication. We selected meta-analyses, randomized controlled trials, and survey results. Our selection focused on the largest, most well-studied published reports. We also attempted to include studies that reported the application of the rules to the field of sports medicine. Retrospective literature review. Level 4. The Ottawa Foot and Ankle Rules have been validated and implemented and are appropriate for use in both pediatric and adult populations. The Ottawa Knee Rules have been widely studied, validated, and accepted for evaluation of knee injuries. There are promising studies of decision rules for clinically important fractures of the wrist, but these studies have not been validated. The elbow has been evaluated with good outcomes via the elbow extension test, which has been validated in both single and multicenter studies. Currently, there are no reliable clinical decision tools for traumatic sports injuries to the shoulder to aid in the decision of when to obtain radiographs. Clinical decision tools have been developed to aid in the diagnosis and management of injuries commonly sustained during sporting events. Tools that have been appropriately validated in populations outside the initial study population can assist sports medicine physicians in the decision of when to get radiographs from the sidelines.

Urban Rain Gauge Siting Selection Based on Gis-Multicriteria Analysis

NASA Astrophysics Data System (ADS)

Fu, Yanli; Jing, Changfeng; Du, Mingyi

2016-06-01

With the increasingly rapid growth of urbanization and climate change, urban rainfall monitoring as well as urban waterlogging has widely been paid attention. In the light of conventional siting selection methods do not take into consideration of geographic surroundings and spatial-temporal scale for the urban rain gauge site selection, this paper primarily aims at finding the appropriate siting selection rules and methods for rain gauge in urban area. Additionally, for optimization gauge location, a spatial decision support system (DSS) aided by geographical information system (GIS) has been developed. In terms of a series of criteria, the rain gauge optimal site-search problem can be addressed by a multicriteria decision analysis (MCDA). A series of spatial analytical techniques are required for MCDA to identify the prospective sites. With the platform of GIS, using spatial kernel density analysis can reflect the population density; GIS buffer analysis is used to optimize the location with the rain gauge signal transmission character. Experiment results show that the rules and the proposed method are proper for the rain gauge site selection in urban areas, which is significant for the siting selection of urban hydrological facilities and infrastructure, such as water gauge.

Distributed collaborative environments for predictive battlespace awareness

NASA Astrophysics Data System (ADS)

McQuay, William K.

2003-09-01

The past decade has produced significant changes in the conduct of military operations: asymmetric warfare, the reliance on dynamic coalitions, stringent rules of engagement, increased concern about collateral damage, and the need for sustained air operations. Mission commanders need to assimilate a tremendous amount of information, make quick-response decisions, and quantify the effects of those decisions in the face of uncertainty. Situational assessment is crucial in understanding the battlespace. Decision support tools in a distributed collaborative environment offer the capability of decomposing complex multitask processes and distributing them over a dynamic set of execution assets that include modeling, simulations, and analysis tools. Decision support technologies can semi-automate activities, such as analysis and planning, that have a reasonably well-defined process and provide machine-level interfaces to refine the myriad of information that the commander must fused. Collaborative environments provide the framework and integrate models, simulations, and domain specific decision support tools for the sharing and exchanging of data, information, knowledge, and actions. This paper describes ongoing AFRL research efforts in applying distributed collaborative environments to predictive battlespace awareness.

Testing decision rules for categorizing species' extinction risk to help develop quantitative listing criteria for the U.S. Endangered Species Act.

PubMed

Regan, Tracey J; Taylor, Barbara L; Thompson, Grant G; Cochrane, Jean Fitts; Ralls, Katherine; Runge, Michael C; Merrick, Richard

2013-08-01

Lack of guidance for interpreting the definitions of endangered and threatened in the U.S. Endangered Species Act (ESA) has resulted in case-by-case decision making leaving the process vulnerable to being considered arbitrary or capricious. Adopting quantitative decision rules would remedy this but requires the agency to specify the relative urgency concerning extinction events over time, cutoff risk values corresponding to different levels of protection, and the importance given to different types of listing errors. We tested the performance of 3 sets of decision rules that use alternative functions for weighting the relative urgency of future extinction events: a threshold rule set, which uses a decision rule of x% probability of extinction over y years; a concave rule set, where the relative importance of future extinction events declines exponentially over time; and a shoulder rule set that uses a sigmoid shape function, where relative importance declines slowly at first and then more rapidly. We obtained decision cutoffs by interviewing several biologists and then emulated the listing process with simulations that covered a range of extinction risks typical of ESA listing decisions. We evaluated performance of the decision rules under different data quantities and qualities on the basis of the relative importance of misclassification errors. Although there was little difference between the performance of alternative decision rules for correct listings, the distribution of misclassifications differed depending on the function used. Misclassifications for the threshold and concave listing criteria resulted in more overprotection errors, particularly as uncertainty increased, whereas errors for the shoulder listing criteria were more symmetrical. We developed and tested the framework for quantitative decision rules for listing species under the U.S. ESA. If policy values can be agreed on, use of this framework would improve the implementation of the ESA by increasing transparency and consistency. Conservation Biology © 2013 Society for Conservation Biology No claim to original US government works.

A Model for Evidence Accumulation in the Lexical Decision Task

ERIC Educational Resources Information Center

Wagenmakers, Eric-Jan; Steyvers, Mark; Raaijmakers, Jeroen G. W.; Shiffrin, Richard M.; van Rijn, Hedderik; Zeelenberg, Rene

2004-01-01

We present a new model for lexical decision, REM-LD, that is based on REM theory (e.g., Shiffrin & Steyvers, 1997). REM-LD uses a principled (i.e., Bayes' rule) decision process that simultaneously considers the diagnosticity of the evidence for the 'WORD' response and the 'NONWORD' response. The model calculates the odds ratio that the presented…

42 CFR 405.1046 - Notice of an ALJ decision.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., Reconsiderations, and Appeals Under Original Medicare (Part A and Part B) Alj Hearings § 405.1046 Notice of an ALJ decision. (a) General rule. Unless the ALJ dismisses the hearing, the ALJ will issue a written decision... must be based on evidence offered at the hearing or otherwise admitted into the record. The ALJ mails a...

78 FR 11939 - Social Security Ruling, SSR 13-2p.; Titles II and XVI: Evaluating Cases Involving Drug Addiction...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-20

... determination or decision. The type and number of CEs we purchase will depend on the claimant's allegations and... decisions relating to the Federal old-age, survivors, disability, supplemental security income, special veterans benefits, and black lung benefits programs. SSRs may be based on determinations or decisions made...

Efficient discovery of risk patterns in medical data.

PubMed

Li, Jiuyong; Fu, Ada Wai-chee; Fahey, Paul

2009-01-01

This paper studies a problem of efficiently discovering risk patterns in medical data. Risk patterns are defined by a statistical metric, relative risk, which has been widely used in epidemiological research. To avoid fruitless search in the complete exploration of risk patterns, we define optimal risk pattern set to exclude superfluous patterns, i.e. complicated patterns with lower relative risk than their corresponding simpler form patterns. We prove that mining optimal risk pattern sets conforms an anti-monotone property that supports an efficient mining algorithm. We propose an efficient algorithm for mining optimal risk pattern sets based on this property. We also propose a hierarchical structure to present discovered patterns for the easy perusal by domain experts. The proposed approach is compared with two well-known rule discovery methods, decision tree and association rule mining approaches on benchmark data sets and applied to a real world application. The proposed method discovers more and better quality risk patterns than a decision tree approach. The decision tree method is not designed for such applications and is inadequate for pattern exploring. The proposed method does not discover a large number of uninteresting superfluous patterns as an association mining approach does. The proposed method is more efficient than an association rule mining method. A real world case study shows that the method reveals some interesting risk patterns to medical practitioners. The proposed method is an efficient approach to explore risk patterns. It quickly identifies cohorts of patients that are vulnerable to a risk outcome from a large data set. The proposed method is useful for exploratory study on large medical data to generate and refine hypotheses. The method is also useful for designing medical surveillance systems.

Simulation of operating rules and discretional decisions using a fuzzy rule-based system integrated into a water resources management model

NASA Astrophysics Data System (ADS)

Macian-Sorribes, Hector; Pulido-Velazquez, Manuel

2013-04-01

Water resources systems are operated, mostly, using a set of pre-defined rules not regarding, usually, to an optimal allocation in terms of water use or economic benefits, but to historical and institutional reasons. These operating policies are reproduced, commonly, as hedging rules, pack rules or zone-based operations, and simulation models can be used to test their performance under a wide range of hydrological and/or socio-economic hypothesis. Despite the high degree of acceptation and testing that these models have achieved, the actual operation of water resources systems hardly follows all the time the pre-defined rules with the consequent uncertainty on the system performance. Real-world reservoir operation is very complex, affected by input uncertainty (imprecision in forecast inflow, seepage and evaporation losses, etc.), filtered by the reservoir operator's experience and natural risk-aversion, while considering the different physical and legal/institutional constraints in order to meet the different demands and system requirements. The aim of this work is to expose a fuzzy logic approach to derive and assess the historical operation of a system. This framework uses a fuzzy rule-based system to reproduce pre-defined rules and also to match as close as possible the actual decisions made by managers. After built up, the fuzzy rule-based system can be integrated in a water resources management model, making possible to assess the system performance at the basin scale. The case study of the Mijares basin (eastern Spain) is used to illustrate the method. A reservoir operating curve regulates the two main reservoir releases (operated in a conjunctive way) with the purpose of guaranteeing a high realiability of supply to the traditional irrigation districts with higher priority (more senior demands that funded the reservoir construction). A fuzzy rule-based system has been created to reproduce the operating curve's performance, defining the system state (total water stored in the reservoirs) and the month of the year as inputs; and the demand deliveries as outputs. The developed simulation management model integrates the fuzzy-ruled system of the operation of the two main reservoirs of the basin with the corresponding mass balance equations, the physical or boundary conditions and the water allocation rules among the competing demands. Historical information on inflow time series is used as inputs to the model simulation, being trained and validated using historical information on reservoir storage level and flow in several streams of the Mijares river. This methodology provides a more flexible and close to real policies approach. The model is easy to develop and to understand due to its rule-based structure, which mimics the human way of thinking. This can improve cooperation and negotiation between managers, decision-makers and stakeholders. The approach can be also applied to analyze the historical operation of the reservoir (what we have called a reservoir operation "audit").

Assessing Financial Education Methods: Principles vs. Rules-of-Thumb Approaches

ERIC Educational Resources Information Center

Skimmyhorn, William L.; Davies, Evan R.; Mun, David; Mitchell, Brian

2016-01-01

Despite thousands of programs and tremendous public and private interest in improving financial decision-making, little is known about how best to teach financial education. Using an experimental approach, the authors estimated the effects of two different education methodologies (principles-based and rules-of-thumb) on the knowledge,…

Effectiveness of Computerized Decision Support Systems Linked to Electronic Health Records: A Systematic Review and Meta-Analysis

PubMed Central

Kwag, Koren H.; Lytras, Theodore; Bertizzolo, Lorenzo; Brandt, Linn; Pecoraro, Valentina; Rigon, Giulio; Vaona, Alberto; Ruggiero, Francesca; Mangia, Massimo; Iorio, Alfonso; Kunnamo, Ilkka; Bonovas, Stefanos

2014-01-01

We systematically reviewed randomized controlled trials (RCTs) assessing the effectiveness of computerized decision support systems (CDSSs) featuring rule- or algorithm-based software integrated with electronic health records (EHRs) and evidence-based knowledge. We searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and Cochrane Database of Abstracts of Reviews of Effects. Information on system design, capabilities, acquisition, implementation context, and effects on mortality, morbidity, and economic outcomes were extracted. Twenty-eight RCTs were included. CDSS use did not affect mortality (16 trials, 37395 patients; 2282 deaths; risk ratio [RR] = 0.96; 95% confidence interval [CI] = 0.85, 1.08; I2 = 41%). A statistically significant effect was evident in the prevention of morbidity, any disease (9 RCTs; 13868 patients; RR = 0.82; 95% CI = 0.68, 0.99; I2 = 64%), but selective outcome reporting or publication bias cannot be excluded. We observed differences for costs and health service utilization, although these were often small in magnitude. Across clinical settings, new generation CDSSs integrated with EHRs do not affect mortality and might moderately improve morbidity outcomes. PMID:25322302

Optimal two-stage dynamic treatment regimes from a classification perspective with censored survival data.

PubMed

Hager, Rebecca; Tsiatis, Anastasios A; Davidian, Marie

2018-05-18

Clinicians often make multiple treatment decisions at key points over the course of a patient's disease. A dynamic treatment regime is a sequence of decision rules, each mapping a patient's observed history to the set of available, feasible treatment options at each decision point, and thus formalizes this process. An optimal regime is one leading to the most beneficial outcome on average if used to select treatment for the patient population. We propose a method for estimation of an optimal regime involving two decision points when the outcome of interest is a censored survival time, which is based on maximizing a locally efficient, doubly robust, augmented inverse probability weighted estimator for average outcome over a class of regimes. By casting this optimization as a classification problem, we exploit well-studied classification techniques such as support vector machines to characterize the class of regimes and facilitate implementation via a backward iterative algorithm. Simulation studies of performance and application of the method to data from a sequential, multiple assignment randomized clinical trial in acute leukemia are presented. © 2018, The International Biometric Society.

Water-Balance Model to Simulate Historical Lake Levels for Lake Merced, California

NASA Astrophysics Data System (ADS)

Maley, M. P.; Onsoy, S.; Debroux, J.; Eagon, B.

2009-12-01

Lake Merced is a freshwater lake located in southwestern San Francisco, California. In the late 1980s and early 1990s, an extended, severe drought impacted the area that resulted in significant declines in Lake Merced lake levels that raised concerns about the long-term health of the lake. In response to these concerns, the Lake Merced Water Level Restoration Project was developed to evaluate an engineered solution to increase and maintain Lake Merced lake levels. The Lake Merced Lake-Level Model was developed to support the conceptual engineering design to restore lake levels. It is a spreadsheet-based water-balance model that performs monthly water-balance calculations based on the hydrological conceptual model. The model independently calculates each water-balance component based on available climate and hydrological data. The model objective was to develop a practical, rule-based approach for the water balance and to calibrate the model results to measured lake levels. The advantage of a rule-based approach is that once the rules are defined, they enhance the ability to then adapt the model for use in future-case simulations. The model was calibrated to historical lake levels over a 70-year period from 1939 to 2009. Calibrating the model over this long historical range tested the model over a variety of hydrological conditions including wet, normal and dry precipitation years, flood events, and periods of high and low lake levels. The historical lake level range was over 16 feet. The model calibration of historical to simulated lake levels had a residual mean of 0.02 feet and an absolute residual mean of 0.42 feet. More importantly, the model demonstrated the ability to simulate both long-term and short-term trends with a strong correlation of the magnitude for both annual and seasonal fluctuations in lake levels. The calibration results demonstrate an improved conceptual understanding of the key hydrological factors that control lake levels, reduce uncertainty in the hydrological conceptual model, and increase confidence in the model’s ability to forecast future lake conditions. The Lake Merced Lake-Level Model will help decision-makers with a straightforward, practical analysis of the major contributions to lake-level declines that can be used to support engineering, environmental and other decisions.

Acute asthma severity identification of expert system flow in emergency department

NASA Astrophysics Data System (ADS)

Sharif, Nurul Atikah Mohd; Ahmad, Norazura; Ahmad, Nazihah; Desa, Wan Laailatul Hanim Mat

2017-11-01

Integration of computerized system in healthcare management help in smoothening the documentation of patient records, highly accesses of knowledge and clinical practices guideline, and advice on decision making. Exploit the advancement of artificial intelligent such as fuzzy logic and rule-based reasoning may improve the management of emergency department in terms of uncertainty condition and medical practices adherence towards clinical guideline. This paper presenting details of the emergency department flow for acute asthma severity identification with the embedding of acute asthma severity identification expert system (AASIES). Currently, AASIES is still in preliminary stage of system validation. However, the implementation of AASIES in asthma bay management is hope can reduce the usage of paper for manual documentation and be a pioneer for the development of a more complex decision support system to smoothen the ED management and more systematic.

The optimum decision rules for the oddity task.

PubMed

Versfeld, N J; Dai, H; Green, D M

1996-01-01

This paper presents the optimum decision rule for an m-interval oddity task in which m-1 intervals contain the same signal and one is different or odd. The optimum decision rule depends on the degree of correlation among observations. The present approach unifies the different strategies that occur with "roved" or "fixed" experiments (Macmillan & Creelman, 1991, p. 147). It is shown that the commonly used decision rule for an m-interval oddity task corresponds to the special case of highly correlated observations. However, as is also true for the same-different paradigm, there exists a different optimum decision rule when the observations are independent. The relation between the probability of a correct response and d' is derived for the three-interval oddity task. Tables are presented of this relation for the three-, four-, and five-interval oddity task. Finally, an experimental method is proposed that allows one to determine the decision rule used by the observer in an oddity experiment.

Virtue vs utility: Alternative foundations for computer ethics

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

Artz, J.M.

1994-12-31

Ethical decisions within the field of computers and information systems are made at two levels by two distinctly different groups of people. At the level of general principles, ethical issues are debated by academics and industry representatives in an attempt to decide what is proper behavior on issues such as hacking, privacy, and copying software. At another level, that of particular situations, individuals make ethical decisions regarding what is good and proper for them in their particular situation. They may use the general rules provided by the experts or they may decide that these rules do not apply in theirmore » particular situation. Currently, the literature on computer ethics provides some opinions regarding the general rules, and some guidance for developing further general rules. What is missing is guidance for individuals making ethical decisions in particular situations. For the past two hundred years, ethics has been dominated by conduct based ethical theories such as utilitarianism which attempt to describe how people must be behave in order to be moral individuals. Recently, weaknesses in conduct based approaches such as utilitarianism have led moral philosophers to reexamine character based ethical theories such as virtue ethics which dates back to the Greek philosophers Plato and Aristotle. This paper will compare utilitarianism and virtue ethics with respect to the foundations they provide for computer ethics. It will be argued that the very nature of computer ethics and the need to provide guidance to individuals making particular moral decisions points to the ethics of virtue as a superior philosophical foundation for computer ethics. The paper will conclude with the implications of this position for researchers, teachers and writers within the field of computer ethics.« less

Using Landscape-Based Decision Rules to Prioritize Locations of Fuel Treatments in the Boreal Mixedwood of Western Canada

Treesearch

Marc-André Parisien; Dave R. Junor; Victor G. Kafka

2006-01-01

This study used a rule-based approach to prioritize locations of fuel treatments in the boreal mixedwood forest of western Canada. The burn probability (BP) in and around Prince Albert National Park in Saskatchewan was mapped using the Burn-P3 (Probability, Prediction, and Planning) model. Fuel treatment locations were determined according to three scenarios and five...

Decision-making competence in younger and older adults: which cognitive abilities contribute to the application of decision rules?

PubMed

Rosi, Alessia; Bruine de Bruin, Wändi; Del Missier, Fabio; Cavallini, Elena; Russo, Riccardo

2017-12-28

Older adults perform worse than younger adults when applying decision rules to choose between options that vary along multiple attributes. Although previous studies have shown that general fluid cognitive abilities contribute to the accurate application of decision rules, relatively little is known about which specific cognitive abilities play the most important role. We examined the independent roles of working memory, verbal fluency, semantic knowledge, and components of executive functioning. We found that age-related decline in applying decision rules was statistically mediated by age-related decline in working memory and verbal fluency. Our results have implications for theories of aging and decision-making.

Ant groups optimally amplify the effect of transiently informed individuals

NASA Astrophysics Data System (ADS)

Gelblum, Aviram; Pinkoviezky, Itai; Fonio, Ehud; Ghosh, Abhijit; Gov, Nir; Feinerman, Ofer

2015-07-01

To cooperatively transport a large load, it is important that carriers conform in their efforts and align their forces. A downside of behavioural conformism is that it may decrease the group's responsiveness to external information. Combining experiment and theory, we show how ants optimize collective transport. On the single-ant scale, optimization stems from decision rules that balance individuality and compliance. Macroscopically, these rules poise the system at the transition between random walk and ballistic motion where the collective response to the steering of a single informed ant is maximized. We relate this peak in response to the divergence of susceptibility at a phase transition. Our theoretical models predict that the ant-load system can be transitioned through the critical point of this mesoscopic system by varying its size; we present experiments supporting these predictions. Our findings show that efficient group-level processes can arise from transient amplification of individual-based knowledge.

Metadata behind the Interoperability of Wireless Sensor Networks

PubMed Central

Ballari, Daniela; Wachowicz, Monica; Callejo, Miguel Angel Manso

2009-01-01

Wireless Sensor Networks (WSNs) produce changes of status that are frequent, dynamic and unpredictable, and cannot be represented using a linear cause-effect approach. Consequently, a new approach is needed to handle these changes in order to support dynamic interoperability. Our approach is to introduce the notion of context as an explicit representation of changes of a WSN status inferred from metadata elements, which in turn, leads towards a decision-making process about how to maintain dynamic interoperability. This paper describes the developed context model to represent and reason over different WSN status based on four types of contexts, which have been identified as sensing, node, network and organisational contexts. The reasoning has been addressed by developing contextualising and bridges rules. As a result, we were able to demonstrate how contextualising rules have been used to reason on changes of WSN status as a first step towards maintaining dynamic interoperability. PMID:22412330

Metadata behind the Interoperability of Wireless Sensor Networks.

PubMed

Ballari, Daniela; Wachowicz, Monica; Callejo, Miguel Angel Manso

2009-01-01

Wireless Sensor Networks (WSNs) produce changes of status that are frequent, dynamic and unpredictable, and cannot be represented using a linear cause-effect approach. Consequently, a new approach is needed to handle these changes in order to support dynamic interoperability. Our approach is to introduce the notion of context as an explicit representation of changes of a WSN status inferred from metadata elements, which in turn, leads towards a decision-making process about how to maintain dynamic interoperability. This paper describes the developed context model to represent and reason over different WSN status based on four types of contexts, which have been identified as sensing, node, network and organisational contexts. The reasoning has been addressed by developing contextualising and bridges rules. As a result, we were able to demonstrate how contextualising rules have been used to reason on changes of WSN status as a first step towards maintaining dynamic interoperability.

Learning pattern recognition and decision making in the insect brain

NASA Astrophysics Data System (ADS)

Huerta, R.

2013-01-01

We revise the current model of learning pattern recognition in the Mushroom Bodies of the insects using current experimental knowledge about the location of learning, olfactory coding and connectivity. We show that it is possible to have an efficient pattern recognition device based on the architecture of the Mushroom Bodies, sparse code, mutual inhibition and Hebbian leaning only in the connections from the Kenyon cells to the output neurons. We also show that despite the conventional wisdom that believes that artificial neural networks are the bioinspired model of the brain, the Mushroom Bodies actually resemble very closely Support Vector Machines (SVMs). The derived SVM learning rules are situated in the Mushroom Bodies, are nearly identical to standard Hebbian rules, and require inhibition in the output. A very particular prediction of the model is that random elimination of the Kenyon cells in the Mushroom Bodies do not impair the ability to recognize odorants previously learned.

Ant groups optimally amplify the effect of transiently informed individuals

PubMed Central

Gelblum, Aviram; Pinkoviezky, Itai; Fonio, Ehud; Ghosh, Abhijit; Gov, Nir; Feinerman, Ofer

2015-01-01

To cooperatively transport a large load, it is important that carriers conform in their efforts and align their forces. A downside of behavioural conformism is that it may decrease the group's responsiveness to external information. Combining experiment and theory, we show how ants optimize collective transport. On the single-ant scale, optimization stems from decision rules that balance individuality and compliance. Macroscopically, these rules poise the system at the transition between random walk and ballistic motion where the collective response to the steering of a single informed ant is maximized. We relate this peak in response to the divergence of susceptibility at a phase transition. Our theoretical models predict that the ant-load system can be transitioned through the critical point of this mesoscopic system by varying its size; we present experiments supporting these predictions. Our findings show that efficient group-level processes can arise from transient amplification of individual-based knowledge. PMID:26218613

A rule-based system for real-time analysis of control systems

NASA Astrophysics Data System (ADS)

Larson, Richard R.; Millard, D. Edward

1992-10-01

An approach to automate the real-time analysis of flight critical health monitoring and system status is being developed and evaluated at the NASA Dryden Flight Research Facility. A software package was developed in-house and installed as part of the extended aircraft interrogation and display system. This design features a knowledge-base structure in the form of rules to formulate interpretation and decision logic of real-time data. This technique has been applied for ground verification and validation testing and flight testing monitoring where quick, real-time, safety-of-flight decisions can be very critical. In many cases post processing and manual analysis of flight system data are not required. The processing is described of real-time data for analysis along with the output format which features a message stack display. The development, construction, and testing of the rule-driven knowledge base, along with an application using the X-31A flight test program, are presented.

Model-Based Anomaly Detection for a Transparent Optical Transmission System

NASA Astrophysics Data System (ADS)

Bengtsson, Thomas; Salamon, Todd; Ho, Tin Kam; White, Christopher A.

In this chapter, we present an approach for anomaly detection at the physical layer of networks where detailed knowledge about the devices and their operations is available. The approach combines physics-based process models with observational data models to characterize the uncertainties and derive the alarm decision rules. We formulate and apply three different methods based on this approach for a well-defined problem in optical network monitoring that features many typical challenges for this methodology. Specifically, we address the problem of monitoring optically transparent transmission systems that use dynamically controlled Raman amplification systems. We use models of amplifier physics together with statistical estimation to derive alarm decision rules and use these rules to automatically discriminate between measurement errors, anomalous losses, and pump failures. Our approach has led to an efficient tool for systematically detecting anomalies in the system behavior of a deployed network, where pro-active measures to address such anomalies are key to preventing unnecessary disturbances to the system's continuous operation.

A rule-based system for real-time analysis of control systems

NASA Technical Reports Server (NTRS)

Larson, Richard R.; Millard, D. Edward

1992-01-01

An approach to automate the real-time analysis of flight critical health monitoring and system status is being developed and evaluated at the NASA Dryden Flight Research Facility. A software package was developed in-house and installed as part of the extended aircraft interrogation and display system. This design features a knowledge-base structure in the form of rules to formulate interpretation and decision logic of real-time data. This technique has been applied for ground verification and validation testing and flight testing monitoring where quick, real-time, safety-of-flight decisions can be very critical. In many cases post processing and manual analysis of flight system data are not required. The processing is described of real-time data for analysis along with the output format which features a message stack display. The development, construction, and testing of the rule-driven knowledge base, along with an application using the X-31A flight test program, are presented.

In Search of Golden Rules: Comment on Hypothesis-Testing Approaches to Setting Cutoff Values for Fit Indexes and Dangers in Overgeneralizing Hu and Bentler's (1999) Findings

ERIC Educational Resources Information Center

Marsh, Herbert W.; Hau, Kit-Tai; Wen, Zhonglin

2004-01-01

Goodness-of-fit (GOF) indexes provide "rules of thumb"?recommended cutoff values for assessing fit in structural equation modeling. Hu and Bentler (1999) proposed a more rigorous approach to evaluating decision rules based on GOF indexes and, on this basis, proposed new and more stringent cutoff values for many indexes. This article discusses…

A Cognitive Architecture for Human Performance Process Model Research

DTIC Science & Technology

1992-11-01

individually defined, updatable world representation which is a description of the world as the operator knows it. It contains rules for decisions, an...operate it), and rules of engagement (knowledge about the operator’s expected behavior). The HPP model works in the following way. Information enters...based models depict the problem-solving processes of experts. The experts’ knowledge is represented in symbol structures, along with rules for

Verification and Validation of KBS with Neural Network Components

NASA Technical Reports Server (NTRS)

Wen, Wu; Callahan, John

1996-01-01

Artificial Neural Network (ANN) play an important role in developing robust Knowledge Based Systems (KBS). The ANN based components used in these systems learn to give appropriate predictions through training with correct input-output data patterns. Unlike traditional KBS that depends on a rule database and a production engine, the ANN based system mimics the decisions of an expert without specifically formulating the if-than type of rules. In fact, the ANNs demonstrate their superiority when such if-then type of rules are hard to generate by human expert. Verification of traditional knowledge based system is based on the proof of consistency and completeness of the rule knowledge base and correctness of the production engine.These techniques, however, can not be directly applied to ANN based components.In this position paper, we propose a verification and validation procedure for KBS with ANN based components. The essence of the procedure is to obtain an accurate system specification through incremental modification of the specifications using an ANN rule extraction algorithm.

Requiem for the max rule?

PubMed Central

Ma, Wei Ji; Shen, Shan; Dziugaite, Gintare; van den Berg, Ronald

2015-01-01

In tasks such as visual search and change detection, a key question is how observers integrate noisy measurements from multiple locations to make a decision. Decision rules proposed to model this process haven fallen into two categories: Bayes-optimal (ideal observer) rules and ad-hoc rules. Among the latter, the maximum-of-outputs (max) rule has been most prominent. Reviewing recent work and performing new model comparisons across a range of paradigms, we find that in all cases except for one, the optimal rule describes human data as well as or better than every max rule either previously proposed or newly introduced here. This casts doubt on the utility of the max rule for understanding perceptual decision-making. PMID:25584425

Learning temporal rules to forecast instability in continuously monitored patients.

PubMed

Guillame-Bert, Mathieu; Dubrawski, Artur; Wang, Donghan; Hravnak, Marilyn; Clermont, Gilles; Pinsky, Michael R

2017-01-01

Inductive machine learning, and in particular extraction of association rules from data, has been successfully used in multiple application domains, such as market basket analysis, disease prognosis, fraud detection, and protein sequencing. The appeal of rule extraction techniques stems from their ability to handle intricate problems yet produce models based on rules that can be comprehended by humans, and are therefore more transparent. Human comprehension is a factor that may improve adoption and use of data-driven decision support systems clinically via face validity. In this work, we explore whether we can reliably and informatively forecast cardiorespiratory instability (CRI) in step-down unit (SDU) patients utilizing data from continuous monitoring of physiologic vital sign (VS) measurements. We use a temporal association rule extraction technique in conjunction with a rule fusion protocol to learn how to forecast CRI in continuously monitored patients. We detail our approach and present and discuss encouraging empirical results obtained using continuous multivariate VS data from the bedside monitors of 297 SDU patients spanning 29 346 hours (3.35 patient-years) of observation. We present example rules that have been learned from data to illustrate potential benefits of comprehensibility of the extracted models, and we analyze the empirical utility of each VS as a potential leading indicator of an impending CRI event. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Complexity of line-seru conversion for different scheduling rules and two improved exact algorithms for the multi-objective optimization.

PubMed

Yu, Yang; Wang, Sihan; Tang, Jiafu; Kaku, Ikou; Sun, Wei

2016-01-01

Productivity can be greatly improved by converting the traditional assembly line to a seru system, especially in the business environment with short product life cycles, uncertain product types and fluctuating production volumes. Line-seru conversion includes two decision processes, i.e., seru formation and seru load. For simplicity, however, previous studies focus on the seru formation with a given scheduling rule in seru load. We select ten scheduling rules usually used in seru load to investigate the influence of different scheduling rules on the performance of line-seru conversion. Moreover, we clarify the complexities of line-seru conversion for ten different scheduling rules from the theoretical perspective. In addition, multi-objective decisions are often used in line-seru conversion. To obtain Pareto-optimal solutions of multi-objective line-seru conversion, we develop two improved exact algorithms based on reducing time complexity and space complexity respectively. Compared with the enumeration based on non-dominated sorting to solve multi-objective problem, the two improved exact algorithms saves computation time greatly. Several numerical simulation experiments are performed to show the performance improvement brought by the two proposed exact algorithms.

Decision tables and rule engines in organ allocation systems for optimal transparency and flexibility.

PubMed

Schaafsma, Murk; van der Deijl, Wilfred; Smits, Jacqueline M; Rahmel, Axel O; de Vries Robbé, Pieter F; Hoitsma, Andries J

2011-05-01

Organ allocation systems have become complex and difficult to comprehend. We introduced decision tables to specify the rules of allocation systems for different organs. A rule engine with decision tables as input was tested for the Kidney Allocation System (ETKAS). We compared this rule engine with the currently used ETKAS by running 11,000 historical match runs and by running the rule engine in parallel with the ETKAS on our allocation system. Decision tables were easy to implement and successful in verifying correctness, completeness, and consistency. The outcomes of the 11,000 historical matches in the rule engine and the ETKAS were exactly the same. Running the rule engine simultaneously in parallel and in real time with the ETKAS also produced no differences. Specifying organ allocation rules in decision tables is already a great step forward in enhancing the clarity of the systems. Yet, using these tables as rule engine input for matches optimizes the flexibility, simplicity and clarity of the whole process, from specification to the performed matches, and in addition this new method allows well controlled simulations. © 2011 The Authors. Transplant International © 2011 European Society for Organ Transplantation.

A Pilot Study to Reduce Computed Tomography Utilization for Pediatric Mild Head Injury in the Emergency Department Using a Clinical Decision Support Tool and a Structured Parent Discussion Tool.

PubMed

Engineer, Rakesh S; Podolsky, Seth R; Fertel, Baruch S; Grover, Purva; Jimenez, Heather; Simon, Erin L; Smalley, Courtney M

2018-05-15

The American College of Emergency Physicians embarked on the "Choosing Wisely" campaign to avoid computed tomographic (CT) scans in patients with minor head injury who are at low risk based on validated decision rules. We hypothesized that a Pediatric Mild Head Injury Care Path could be developed and implemented to reduce inappropriate CT utilization with support of a clinical decision support tool (CDST) and a structured parent discussion tool. A quality improvement project was initiated for 9 weeks to reduce inappropriate CT utilization through 5 interventions: (1) engagement of leadership, (2) provider education, (3) incorporation of a parent discussion tool to guide discussion during the emergency department (ED) visit between the parent and the provider, (4) CDST embedded in the electronic medical record, and (5) importation of data into the note to drive compliance. Patients prospectively were enrolled when providers at a pediatric and a freestanding ED entered data into the CDST for decision making. Rate of care path utilization and head CT reduction was determined for all patients with minor head injury based on International Classification of Diseases, Ninth Revision codes. Targets for care path utilization and head CT reduction were established a priori. Results were compared with baseline data collected from 2013. The CDST was used in 176 (77.5%) of 227 eligible patients. Twelve patients were excluded based on a priori criteria. Adherence to recommendations occurred in 162 (99%) of 164 patients. Head CT utilization was reduced from 62.7% to 22% (odds ratio, 0.17; 95% confidence interval, 0.12-0.24) where CDST was used by the provider. There were no missed traumatic brain injuries in our study group. A Pediatric Mild Head Injury Care Path can be implemented in a pediatric and freestanding ED, resulting in reduced head CT utilization and high levels of adherence to CDST recommendations.

Knowledge-based and model-based hybrid methodology for comprehensive waste minimization in electroplating plants

NASA Astrophysics Data System (ADS)

Luo, Keqin

1999-11-01

The electroplating industry of over 10,000 planting plants nationwide is one of the major waste generators in the industry. Large quantities of wastewater, spent solvents, spent process solutions, and sludge are the major wastes generated daily in plants, which costs the industry tremendously for waste treatment and disposal and hinders the further development of the industry. It becomes, therefore, an urgent need for the industry to identify technically most effective and economically most attractive methodologies and technologies to minimize the waste, while the production competitiveness can be still maintained. This dissertation aims at developing a novel WM methodology using artificial intelligence, fuzzy logic, and fundamental knowledge in chemical engineering, and an intelligent decision support tool. The WM methodology consists of two parts: the heuristic knowledge-based qualitative WM decision analysis and support methodology and fundamental knowledge-based quantitative process analysis methodology for waste reduction. In the former, a large number of WM strategies are represented as fuzzy rules. This becomes the main part of the knowledge base in the decision support tool, WMEP-Advisor. In the latter, various first-principles-based process dynamic models are developed. These models can characterize all three major types of operations in an electroplating plant, i.e., cleaning, rinsing, and plating. This development allows us to perform a thorough process analysis on bath efficiency, chemical consumption, wastewater generation, sludge generation, etc. Additional models are developed for quantifying drag-out and evaporation that are critical for waste reduction. The models are validated through numerous industrial experiments in a typical plating line of an industrial partner. The unique contribution of this research is that it is the first time for the electroplating industry to (i) use systematically available WM strategies, (ii) know quantitatively and accurately what is going on in each tank, and (iii) identify all WM opportunities through process improvement. This work has formed a solid foundation for the further development of powerful WM technologies for comprehensive WM in the following decade.

Evaluation of SARs for the prediction of eye irritation/corrosion potential: structural inclusion rules in the BfR decision support system.

PubMed

Tsakovska, I; Saliner, A Gallegos; Netzeva, T; Pavan, M; Worth, A P

2007-01-01

The proposed REACH regulation within the European Union (EU) aims to minimise the number of laboratory animals used for human hazard and risk assessment while ensuring adequate protection of human health and the environment. One way to achieve this goal is to develop non-testing methods, such as (quantitative) structure-activity relationships ([Q]SARs), suitable for identifying toxicological hazard from chemical structure and physicochemical properties alone. A database containing data submitted within the EU New Chemicals Notification procedure was compiled by the German Bundesinstitut für Risikobewertung (BfR). On the basis of these data, the BfR built a decision support system (DSS) for the prediction of several toxicological endpoints. For the prediction of eye irritation and corrosion potential, the DSS contains 31 physicochemical exclusion rules evaluated previously by the European Chemicals Bureau (ECB), and 27 inclusion rules that define structural alerts potentially responsible for eye irritation and/or corrosion. This work summarises the results of a study carried out by the ECB to assess the performance of the BfR structural rulebase. The assessment included: (a) evaluation of the structural alerts by using the training set of 1341 substances with experimental data for eye irritation and corrosion; and (b) external validation by using an independent test set of 199 chemicals. Recommendations are made for the further development of the structural rules in order to increase the overall predictivity of the DSS.

Process service quality evaluation based on Dempster-Shafer theory and support vector machine.

PubMed

Pei, Feng-Que; Li, Dong-Bo; Tong, Yi-Fei; He, Fei

2017-01-01

Human involvement influences traditional service quality evaluations, which triggers an evaluation's low accuracy, poor reliability and less impressive predictability. This paper proposes a method by employing a support vector machine (SVM) and Dempster-Shafer evidence theory to evaluate the service quality of a production process by handling a high number of input features with a low sampling data set, which is called SVMs-DS. Features that can affect production quality are extracted by a large number of sensors. Preprocessing steps such as feature simplification and normalization are reduced. Based on three individual SVM models, the basic probability assignments (BPAs) are constructed, which can help the evaluation in a qualitative and quantitative way. The process service quality evaluation results are validated by the Dempster rules; the decision threshold to resolve conflicting results is generated from three SVM models. A case study is presented to demonstrate the effectiveness of the SVMs-DS method.

DTFP-Growth: Dynamic Threshold-Based FP-Growth Rule Mining Algorithm Through Integrating Gene Expression, Methylation, and Protein-Protein Interaction Profiles.

PubMed

Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan; Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan; Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan

2018-04-01

Association rule mining is an important technique for identifying interesting relationships between gene pairs in a biological data set. Earlier methods basically work for a single biological data set, and, in maximum cases, a single minimum support cutoff can be applied globally, i.e., across all genesets/itemsets. To overcome this limitation, in this paper, we propose dynamic threshold-based FP-growth rule mining algorithm that integrates gene expression, methylation and protein-protein interaction profiles based on weighted shortest distance to find the novel associations among different pairs of genes in multi-view data sets. For this purpose, we introduce three new thresholds, namely, Distance-based Variable/Dynamic Supports (DVS), Distance-based Variable Confidences (DVC), and Distance-based Variable Lifts (DVL) for each rule by integrating co-expression, co-methylation, and protein-protein interactions existed in the multi-omics data set. We develop the proposed algorithm utilizing these three novel multiple threshold measures. In the proposed algorithm, the values of , , and are computed for each rule separately, and subsequently it is verified whether the support, confidence, and lift of each evolved rule are greater than or equal to the corresponding individual , , and values, respectively, or not. If all these three conditions for a rule are found to be true, the rule is treated as a resultant rule. One of the major advantages of the proposed method compared with other related state-of-the-art methods is that it considers both the quantitative and interactive significance among all pairwise genes belonging to each rule. Moreover, the proposed method generates fewer rules, takes less running time, and provides greater biological significance for the resultant top-ranking rules compared to previous methods.

The application of remote sensing image sea ice monitoring method in Bohai Bay based on C4.5 decision tree algorithm

NASA Astrophysics Data System (ADS)

Ye, Wei; Song, Wei

2018-02-01

In The Paper, the remote sensing monitoring of sea ice problem was turned into a classification problem in data mining. Based on the statistic of the related band data of HJ1B remote sensing images, the main bands of HJ1B images related with the reflectance of seawater and sea ice were found. On the basis, the decision tree rules for sea ice monitoring were constructed by the related bands found above, and then the rules were applied to Liaodong Bay area seriously covered by sea ice for sea ice monitoring. The result proved that the method is effective.

Biclustering Learning of Trading Rules.

PubMed

Huang, Qinghua; Wang, Ting; Tao, Dacheng; Li, Xuelong

2015-10-01

Technical analysis with numerous indicators and patterns has been regarded as important evidence for making trading decisions in financial markets. However, it is extremely difficult for investors to find useful trading rules based on numerous technical indicators. This paper innovatively proposes the use of biclustering mining to discover effective technical trading patterns that contain a combination of indicators from historical financial data series. This is the first attempt to use biclustering algorithm on trading data. The mined patterns are regarded as trading rules and can be classified as three trading actions (i.e., the buy, the sell, and no-action signals) with respect to the maximum support. A modified K nearest neighborhood ( K -NN) method is applied to classification of trading days in the testing period. The proposed method [called biclustering algorithm and the K nearest neighbor (BIC- K -NN)] was implemented on four historical datasets and the average performance was compared with the conventional buy-and-hold strategy and three previously reported intelligent trading systems. Experimental results demonstrate that the proposed trading system outperforms its counterparts and will be useful for investment in various financial markets.

Decentralisation of Health Services in Fiji: A Decision Space Analysis.

PubMed

Mohammed, Jalal; North, Nicola; Ashton, Toni

2015-11-15

Decentralisation aims to bring services closer to the community and has been advocated in the health sector to improve quality, access and equity, and to empower local agencies, increase innovation and efficiency and bring healthcare and decision-making as close as possible to where people live and work. Fiji has attempted two approaches to decentralisation. The current approach reflects a model of deconcentration of outpatient services from the tertiary level hospital to the peripheral health centres in the Suva subdivision. Using a modified decision space approach developed by Bossert, this study measures decision space created in five broad categories (finance, service organisation, human resources, access rules, and governance rules) within the decentralised services. Fiji's centrally managed historical-based allocation of financial resources and management of human resources resulted in no decision space for decentralised agents. Narrow decision space was created in the service organisation category where, with limited decision space created over access rules, Fiji has seen greater usage of its decentralised health centres. There remains limited decision space in governance. The current wave of decentralisation reveals that, whilst the workload has shifted from the tertiary hospital to the peripheral health centres, it has been accompanied by limited transfer of administrative authority, suggesting that Fiji's deconcentration reflects the transfer of workload only with decision-making in the five functional areas remaining largely centralised. As such, the benefits of decentralisation for users and providers are likely to be limited. © 2016 by Kerman University of Medical Sciences.

38 CFR 20.1303 - Rule 1303. Nonprecedential nature of Board decisions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 38 Pensions, Bonuses, and Veterans' Relief 2 2010-07-01 2010-07-01 false Rule 1303. Nonprecedential nature of Board decisions. 20.1303 Section 20.1303 Pensions, Bonuses, and Veterans' Relief....1303 Rule 1303. Nonprecedential nature of Board decisions. Although the Board strives for consistency...

38 CFR 20.1303 - Rule 1303. Nonprecedential nature of Board decisions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 38 Pensions, Bonuses, and Veterans' Relief 2 2011-07-01 2011-07-01 false Rule 1303. Nonprecedential nature of Board decisions. 20.1303 Section 20.1303 Pensions, Bonuses, and Veterans' Relief....1303 Rule 1303. Nonprecedential nature of Board decisions. Although the Board strives for consistency...

38 CFR 20.1303 - Rule 1303. Nonprecedential nature of Board decisions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 38 Pensions, Bonuses, and Veterans' Relief 2 2013-07-01 2013-07-01 false Rule 1303. Nonprecedential nature of Board decisions. 20.1303 Section 20.1303 Pensions, Bonuses, and Veterans' Relief....1303 Rule 1303. Nonprecedential nature of Board decisions. Although the Board strives for consistency...

38 CFR 20.1303 - Rule 1303. Nonprecedential nature of Board decisions.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 38 Pensions, Bonuses, and Veterans' Relief 2 2014-07-01 2014-07-01 false Rule 1303. Nonprecedential nature of Board decisions. 20.1303 Section 20.1303 Pensions, Bonuses, and Veterans' Relief....1303 Rule 1303. Nonprecedential nature of Board decisions. Although the Board strives for consistency...

38 CFR 20.1303 - Rule 1303. Nonprecedential nature of Board decisions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 38 Pensions, Bonuses, and Veterans' Relief 2 2012-07-01 2012-07-01 false Rule 1303. Nonprecedential nature of Board decisions. 20.1303 Section 20.1303 Pensions, Bonuses, and Veterans' Relief....1303 Rule 1303. Nonprecedential nature of Board decisions. Although the Board strives for consistency...

19 CFR 177.10 - Publication of decisions.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 19 Customs Duties 2 2014-04-01 2014-04-01 false Publication of decisions. 177.10 Section 177.10... TREASURY (CONTINUED) ADMINISTRATIVE RULINGS General Ruling Procedure § 177.10 Publication of decisions. (a....8(a)(3). (b) [Reserved] (c) Changes of practice. Before the publication of a ruling which has the...

Development and use of active clinical decision support for preemptive pharmacogenomics

PubMed Central

Bell, Gillian C; Crews, Kristine R; Wilkinson, Mark R; Haidar, Cyrine E; Hicks, J Kevin; Baker, Donald K; Kornegay, Nancy M; Yang, Wenjian; Cross, Shane J; Howard, Scott C; Freimuth, Robert R; Evans, William E; Broeckel, Ulrich; Relling, Mary V; Hoffman, James M

2014-01-01

Background Active clinical decision support (CDS) delivered through an electronic health record (EHR) facilitates gene-based drug prescribing and other applications of genomics to patient care. Objective We describe the development, implementation, and evaluation of active CDS for multiple pharmacogenetic test results reported preemptively. Materials and methods Clinical pharmacogenetic test results accompanied by clinical interpretations are placed into the patient's EHR, typically before a relevant drug is prescribed. Problem list entries created for high-risk phenotypes provide an unambiguous trigger for delivery of post-test alerts to clinicians when high-risk drugs are prescribed. In addition, pre-test alerts are issued if a very-high risk medication is prescribed (eg, a thiopurine), prior to the appropriate pharmacogenetic test result being entered into the EHR. Our CDS can be readily modified to incorporate new genes or high-risk drugs as they emerge. Results Through November 2012, 35 customized pharmacogenetic rules have been implemented, including rules for TPMT with azathioprine, thioguanine, and mercaptopurine, and for CYP2D6 with codeine, tramadol, amitriptyline, fluoxetine, and paroxetine. Between May 2011 and November 2012, the pre-test alerts were electronically issued 1106 times (76 for thiopurines and 1030 for drugs metabolized by CYP2D6), and the post-test alerts were issued 1552 times (1521 for TPMT and 31 for CYP2D6). Analysis of alert outcomes revealed that the interruptive CDS appropriately guided prescribing in 95% of patients for whom they were issued. Conclusions Our experience illustrates the feasibility of developing computational systems that provide clinicians with actionable alerts for gene-based drug prescribing at the point of care. PMID:23978487

Cost–effectiveness thresholds: pros and cons

PubMed Central

Lauer, Jeremy A; De Joncheere, Kees; Edejer, Tessa; Hutubessy, Raymond; Kieny, Marie-Paule; Hill, Suzanne R

2016-01-01

Abstract Cost–effectiveness analysis is used to compare the costs and outcomes of alternative policy options. Each resulting cost–effectiveness ratio represents the magnitude of additional health gained per additional unit of resources spent. Cost–effectiveness thresholds allow cost–effectiveness ratios that represent good or very good value for money to be identified. In 2001, the World Health Organization’s Commission on Macroeconomics in Health suggested cost–effectiveness thresholds based on multiples of a country’s per-capita gross domestic product (GDP). In some contexts, in choosing which health interventions to fund and which not to fund, these thresholds have been used as decision rules. However, experience with the use of such GDP-based thresholds in decision-making processes at country level shows them to lack country specificity and this – in addition to uncertainty in the modelled cost–effectiveness ratios – can lead to the wrong decision on how to spend health-care resources. Cost–effectiveness information should be used alongside other considerations – e.g. budget impact and feasibility considerations – in a transparent decision-making process, rather than in isolation based on a single threshold value. Although cost–effectiveness ratios are undoubtedly informative in assessing value for money, countries should be encouraged to develop a context-specific process for decision-making that is supported by legislation, has stakeholder buy-in, for example the involvement of civil society organizations and patient groups, and is transparent, consistent and fair. PMID:27994285

Lifting Off of the Digital Plateau with Military Decision Support Systems

DTIC Science & Technology

2013-05-23

concerns were echoed in Germany, where Colonel (later General) Jules von Verdy stripped away the complex rules and tables, and relied on the military...Review (March-April 2001): 38-45. 55 Swift, Eben. Orders. Fort Leavenworth: Staff College Press, 1905. Verdy, Jules von. Free Kreigspiel. Edited by

Sideline Coverage

PubMed Central

Gould, Sara J.; Cardone, Dennis A.; Munyak, John; Underwood, Philipp J.; Gould, Stephen A.

2014-01-01

Context: Sidelines coverage presents unique challenges in the evaluation of injured athletes. Health care providers may be confronted with the question of when to obtain radiographs following an injury. Given that most sidelines coverage occurs outside the elite level, radiographs are not readily available at the time of injury, and the decision of when to send a player for radiographs must be made based on physical examination. Clinical tools have been developed to aid in identifying injuries that are likely to result in radiographically important fractures or dislocations. Evidence Acquisition: A search for the keywords x-ray and decision rule along with the anatomic locations shoulder, elbow, wrist, knee, and ankle was performed using the PubMed database. No limits were set regarding year of publication. We selected meta-analyses, randomized controlled trials, and survey results. Our selection focused on the largest, most well-studied published reports. We also attempted to include studies that reported the application of the rules to the field of sports medicine. Study Design: Retrospective literature review. Level of Evidence: Level 4. Results: The Ottawa Foot and Ankle Rules have been validated and implemented and are appropriate for use in both pediatric and adult populations. The Ottawa Knee Rules have been widely studied, validated, and accepted for evaluation of knee injuries. There are promising studies of decision rules for clinically important fractures of the wrist, but these studies have not been validated. The elbow has been evaluated with good outcomes via the elbow extension test, which has been validated in both single and multicenter studies. Currently, there are no reliable clinical decision tools for traumatic sports injuries to the shoulder to aid in the decision of when to obtain radiographs. Conclusion: Clinical decision tools have been developed to aid in the diagnosis and management of injuries commonly sustained during sporting events. Tools that have been appropriately validated in populations outside the initial study population can assist sports medicine physicians in the decision of when to get radiographs from the sidelines. PMID:24790698

Development of a GIService based on spatial data mining for location choice of convenience stores in Taipei City

NASA Astrophysics Data System (ADS)

Jung, Chinte; Sun, Chih-Hong

2006-10-01

Motivated by the increasing accessibility of technology, more and more spatial data are being made digitally available. How to extract the valuable knowledge from these large (spatial) databases is becoming increasingly important to businesses, as well. It is essential to be able to analyze and utilize these large datasets, convert them into useful knowledge, and transmit them through GIS-enabled instruments and the Internet, conveying the key information to business decision-makers effectively and benefiting business entities. In this research, we combine the techniques of GIS, spatial decision support system (SDSS), spatial data mining (SDM), and ArcGIS Server to achieve the following goals: (1) integrate databases from spatial and non-spatial datasets about the locations of businesses in Taipei, Taiwan; (2) use the association rules, one of the SDM methods, to extract the knowledge from the integrated databases; and (3) develop a Web-based SDSS GIService as a location-selection tool for business by the product of ArcGIS Server.

Sexual and reproductive health care for adolescents: legal rights and policy challenges.

PubMed

English, Abigail

2007-12-01

Laws developed over the past half century have significantly improved adolescents' access to essential sexual and reproductive health care. These laws allow many adolescent minors to give their own consent, protect confidentiality, and provide financial support for the care. The consent requirements for adolescents to receive health care are contained primarily in state court decisions and in statutes known as "state minor consent laws," which are based on either the minor's status or the services sought. Confidentiality protections for adolescents' health information are contained in these minor consent laws, in the federal medical privacy regulations known as the "HIPAA Privacy Rule," and in state medical privacy laws. Other significant laws include statutes providing for the emancipation of minors, court decisions delineating the mature minor doctrine, regulations protecting adolescents' access to confidential family planning services in publicly funded programs, and court decisions interpreting the constitutional right of privacy. Special considerations apply to consent and confidentiality questions pertaining to family planning, contraception, and pregnancy-related care for minors. In addition to the explicit provisions of state minor consent laws, many of the most important considerations are articulated in court decisions based on the constitutional right of privacy and the confidentiality requirements that are part of the federal Title X Family Planning Program and Medicaid.

A prospective observational study to assess the diagnostic accuracy of clinical decision rules for children presenting to emergency departments after head injuries (protocol): the Australasian Paediatric Head Injury Rules Study (APHIRST).

PubMed

Babl, Franz E; Lyttle, Mark D; Bressan, Silvia; Borland, Meredith; Phillips, Natalie; Kochar, Amit; Dalziel, Stuart R; Dalton, Sarah; Cheek, John A; Furyk, Jeremy; Gilhotra, Yuri; Neutze, Jocelyn; Ward, Brenton; Donath, Susan; Jachno, Kim; Crowe, Louise; Williams, Amanda; Oakley, Ed

2014-06-13

Head injuries in children are responsible for a large number of emergency department visits. Failure to identify a clinically significant intracranial injury in a timely fashion may result in long term neurodisability and death. Whilst cranial computed tomography (CT) provides rapid and definitive identification of intracranial injuries, it is resource intensive and associated with radiation induced cancer. Evidence based head injury clinical decision rules have been derived to aid physicians in identifying patients at risk of having a clinically significant intracranial injury. Three rules have been identified as being of high quality and accuracy: the Canadian Assessment of Tomography for Childhood Head Injury (CATCH) from Canada, the Children's Head Injury Algorithm for the Prediction of Important Clinical Events (CHALICE) from the UK, and the prediction rule for the identification of children at very low risk of clinically important traumatic brain injury developed by the Pediatric Emergency Care Applied Research Network (PECARN) from the USA. This study aims to prospectively validate and compare the performance accuracy of these three clinical decision rules when applied outside the derivation setting. This study is a prospective observational study of children aged 0 to less than 18 years presenting to 10 emergency departments within the Paediatric Research in Emergency Departments International Collaborative (PREDICT) research network in Australia and New Zealand after head injuries of any severity. Predictor variables identified in CATCH, CHALICE and PECARN clinical decision rules will be collected. Patients will be managed as per the treating clinicians at the participating hospitals. All patients not undergoing cranial CT will receive a follow up call 14 to 90 days after the injury. Outcome data collected will include results of cranial CTs (if performed) and details of admission, intubation, neurosurgery and death. The performance accuracy of each of the rules will be assessed using rule specific outcomes and inclusion and exclusion criteria. This study will allow the simultaneous comparative application and validation of three major paediatric head injury clinical decision rules outside their derivation setting. The study is registered with the Australian New Zealand Clinical Trials Registry (ANZCTR)- ACTRN12614000463673 (registered 2 May 2014).

Decision support system for the operating room rescheduling problem.

PubMed

van Essen, J Theresia; Hurink, Johann L; Hartholt, Woutske; van den Akker, Bernd J

2012-12-01

Due to surgery duration variability and arrivals of emergency surgeries, the planned Operating Room (OR) schedule is disrupted throughout the day which may lead to a change in the start time of the elective surgeries. These changes may result in undesirable situations for patients, wards or other involved departments, and therefore, the OR schedule has to be adjusted. In this paper, we develop a decision support system (DSS) which assists the OR manager in this decision by providing the three best adjusted OR schedules. The system considers the preferences of all involved stakeholders and only evaluates the OR schedules that satisfy the imposed resource constraints. The decision rules used for this system are based on a thorough analysis of the OR rescheduling problem. We model this problem as an Integer Linear Program (ILP) which objective is to minimize the deviation from the preferences of the considered stakeholders. By applying this ILP to instances from practice, we determined that the given preferences mainly lead to (i) shifting a surgery and (ii) scheduling a break between two surgeries. By using these changes in the DSS, the performed simulation study shows that less surgeries are canceled and patients and wards are more satisfied, but also that the perceived workload of several departments increases to compensate this. The system can also be used to judge the acceptability of a proposed initial OR schedule.

76 FR 51442 - Self-Regulatory Organizations; NYSE Arca, Inc.; Notice of Filing of Proposed Rule Change To List...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-18

...-Adviser has designed the following quantitative stock selection rules to make allocation decisions and to..., the Sub-Adviser's investment process is quantitative. Based on extensive historical research, the Sub... open-end fund's portfolio composition must be subject to procedures designed to prevent the use and...

Newsvendor problem under complete uncertainty: a case of innovative products.

PubMed

Gaspars-Wieloch, Helena

2017-01-01

The paper presents a new scenario-based decision rule for the classical version of the newsvendor problem (NP) under complete uncertainty (i.e. uncertainty with unknown probabilities). So far, NP has been analyzed under uncertainty with known probabilities or under uncertainty with partial information (probabilities known incompletely). The novel approach is designed for the sale of new, innovative products, where it is quite complicated to define probabilities or even probability-like quantities, because there are no data available for forecasting the upcoming demand via statistical analysis. The new procedure described in the contribution is based on a hybrid of Hurwicz and Bayes decision rules. It takes into account the decision maker's attitude towards risk (measured by coefficients of optimism and pessimism) and the dispersion (asymmetry, range, frequency of extremes values) of payoffs connected with particular order quantities. It does not require any information about the probability distribution.

Linearly Adjustable International Portfolios

NASA Astrophysics Data System (ADS)

Fonseca, R. J.; Kuhn, D.; Rustem, B.

2010-09-01

We present an approach to multi-stage international portfolio optimization based on the imposition of a linear structure on the recourse decisions. Multiperiod decision problems are traditionally formulated as stochastic programs. Scenario tree based solutions however can become intractable as the number of stages increases. By restricting the space of decision policies to linear rules, we obtain a conservative tractable approximation to the original problem. Local asset prices and foreign exchange rates are modelled separately, which allows for a direct measure of their impact on the final portfolio value.

Requiem for the max rule?

PubMed

Ma, Wei Ji; Shen, Shan; Dziugaite, Gintare; van den Berg, Ronald

2015-11-01

In tasks such as visual search and change detection, a key question is how observers integrate noisy measurements from multiple locations to make a decision. Decision rules proposed to model this process have fallen into two categories: Bayes-optimal (ideal observer) rules and ad-hoc rules. Among the latter, the maximum-of-outputs (max) rule has been the most prominent. Reviewing recent work and performing new model comparisons across a range of paradigms, we find that in all cases except for one, the optimal rule describes human data as well as or better than every max rule either previously proposed or newly introduced here. This casts doubt on the utility of the max rule for understanding perceptual decision-making. Copyright © 2015 Elsevier Ltd. All rights reserved.

Adaptive decision rules for the acquisition of nature reserves.

PubMed

Turner, Will R; Wilcove, David S

2006-04-01

Although reserve-design algorithms have shown promise for increasing the efficiency of conservation planning, recent work casts doubt on the usefulness of some of these approaches in practice. Using three data sets that vary widely in size and complexity, we compared various decision rules for acquiring reserve networks over multiyear periods. We explored three factors that are often important in real-world conservation efforts: uncertain availability of sites for acquisition, degradation of sites, and overall budget constraints. We evaluated the relative strengths and weaknesses of existing optimal and heuristic decision rules and developed a new set of adaptive decision rules that combine the strengths of existing optimal and heuristic approaches. All three of the new adaptive rules performed better than the existing rules we tested under virtually all scenarios of site availability, site degradation, and budget constraints. Moreover, the adaptive rules required no additional data beyond what was readily available and were relatively easy to compute.

Decision Tree based Prediction and Rule Induction for Groundwater Trichloroethene (TCE) Pollution Vulnerability

NASA Astrophysics Data System (ADS)

Park, J.; Yoo, K.

2013-12-01

For groundwater resource conservation, it is important to accurately assess groundwater pollution sensitivity or vulnerability. In this work, we attempted to use data mining approach to assess groundwater pollution vulnerability in a TCE (trichloroethylene) contaminated Korean industrial site. The conventional DRASTIC method failed to describe TCE sensitivity data with a poor correlation with hydrogeological properties. Among the different data mining methods such as Artificial Neural Network (ANN), Multiple Logistic Regression (MLR), Case Base Reasoning (CBR), and Decision Tree (DT), the accuracy and consistency of Decision Tree (DT) was the best. According to the following tree analyses with the optimal DT model, the failure of the conventional DRASTIC method in fitting with TCE sensitivity data may be due to the use of inaccurate weight values of hydrogeological parameters for the study site. These findings provide a proof of concept that DT based data mining approach can be used in predicting and rule induction of groundwater TCE sensitivity without pre-existing information on weights of hydrogeological properties.

How to translate therapeutic recommendations in clinical practice guidelines into rules for critiquing physician prescriptions? Methods and application to five guidelines

PubMed Central

2010-01-01

Background Clinical practice guidelines give recommendations about what to do in various medical situations, including therapeutical recommendations for drug prescription. An effective way to computerize these recommendations is to design critiquing decision support systems, i.e. systems that criticize the physician's prescription when it does not conform to the guidelines. These systems are commonly based on a list of "if conditions then criticism" rules. However, writing these rules from the guidelines is not a trivial task. The objective of this article is to propose methods that (1) simplify the implementation of guidelines' therapeutical recommendations in critiquing systems by automatically translating structured therapeutical recommendations into a list of "if conditions then criticize" rules, and (2) can generate an appropriate textual label to explain to the physician why his/her prescription is not recommended. Methods We worked on the therapeutic recommendations in five clinical practice guidelines concerning chronic diseases related to the management of cardiovascular risk. We evaluated the system using a test base of more than 2000 cases. Results Algorithms for automatically translating therapeutical recommendations into "if conditions then criticize" rules are presented. Eight generic recommendations are also proposed; they are guideline-independent, and can be used as default behaviour for handling various situations that are usually implicit in the guidelines, such as decreasing the dose of a poorly tolerated drug. Finally, we provide models and methods for generating a human-readable textual critique. The system was successfully evaluated on the test base. Conclusion We show that it is possible to criticize physicians' prescriptions starting from a structured clinical guideline, and to provide clear explanations. We are now planning a randomized clinical trial to evaluate the impact of the system on practices. PMID:20509903

TOXPERT: An Expert System for Risk Assessment

PubMed Central

Soto, R. J.; Osimitz, T. G.; Oleson, A.

1988-01-01

TOXPERT is an artificial intelligence based system used to model product safety, toxicology (TOX) and regulatory (REG) decision processes. An expert system shell uses backward chaining rule control to link “marketing approval” goals to the type of product, REG agency, exposure conditions and TOX. Marketing risks are primarily a function of the TOX, hazards and exposure potential. The method employed differentiates between REG requirements in goal seeking control for various types of products. This is accomplished by controlling rule execution by defining frames for each REG agency. In addition, TOXPERT produces classifications of TOX ratings and suggested product labeling. This production rule system uses principles of TOX, REGs, corporate guidelines and internal “rules of thumb.” TOXPERT acts as an advisor for this narrow domain. Advantages are that it can make routine decisions freeing professional's time for more complex problem solving, provide backup and training.

A systematic review of clinical decision support systems for antimicrobial management: are we failing to investigate these interventions appropriately?

PubMed

Rawson, T M; Moore, L S P; Hernandez, B; Charani, E; Castro-Sanchez, E; Herrero, P; Hayhoe, B; Hope, W; Georgiou, P; Holmes, A H

2017-08-01

Clinical decision support systems (CDSS) for antimicrobial management can support clinicians to optimize antimicrobial therapy. We reviewed all original literature (qualitative and quantitative) to understand the current scope of CDSS for antimicrobial management and analyse existing methods used to evaluate and report such systems. PRISMA guidelines were followed. Medline, EMBASE, HMIC Health and Management and Global Health databases were searched from 1 January 1980 to 31 October 2015. All primary research studies describing CDSS for antimicrobial management in adults in primary or secondary care were included. For qualitative studies, thematic synthesis was performed. Quality was assessed using Integrated quality Criteria for the Review Of Multiple Study designs (ICROMS) criteria. CDSS reporting was assessed against a reporting framework for behaviour change intervention implementation. Fifty-eight original articles were included describing 38 independent CDSS. The majority of systems target antimicrobial prescribing (29/38;76%), are platforms integrated with electronic medical records (28/38;74%), and have a rules-based infrastructure providing decision support (29/38;76%). On evaluation against the intervention reporting framework, CDSS studies fail to report consideration of the non-expert, end-user workflow. They have narrow focus, such as antimicrobial selection, and use proxy outcome measures. Engagement with CDSS by clinicians was poor. Greater consideration of the factors that drive non-expert decision making must be considered when designing CDSS interventions. Future work must aim to expand CDSS beyond simply selecting appropriate antimicrobials with clear and systematic reporting frameworks for CDSS interventions developed to address current gaps identified in the reporting of evidence. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Derivation of a clinical decision rule to guide the interhospital transfer of patients with blunt traumatic brain injury.

PubMed

Newgard, C D; Hedges, J R; Stone, J V; Lenfesty, B; Diggs, B; Arthur, M; Mullins, R J

2005-12-01

To derive a clinical decision rule for people with traumatic brain injury (TBI) that enables early identification of patients requiring specialised trauma care. We collected data from 1999 through 2003 on a retrospective cohort of consecutive people aged 18-65 years with a serious head injury (AIS > or =3), transported directly from the scene of injury, and evaluated in the ED. Information on 22 demographical, physiological, radiographic, and lab variables was collected. Resource based "high therapeutic intensity" measures occurring within 72 hours of ED arrival (the outcome measure) were identified a priori and included: neurosurgical intervention, exploratory laparotomy, intensive care interventions, or death. We used classification and regression tree analysis to derive and cross validate the decision rule. 504 consecutive trauma patients were identified as having a serious head injury: 246 (49%) required at least one of the HTI measures. Five ED variables (GCS, respiratory rate, age, temperature, and pulse rate) identified subjects requiring at least one of the HTI measures with 94% sensitivity (95% CI 91 to 97%) and 63% specificity (95% CI 57 to 69%) in the derivation sample, and 90% sensitivity and 55% specificity using cross validation. This decision rule identified among a cohort of head injured patients evaluated in the ED the majority of those who urgently required specialised trauma care. The rule will require prospective validation in injured people presenting to non-tertiary care hospitals before implementation can be recommended.

Irrational decision-making in an amoeboid organism: transitivity and context-dependent preferences.

PubMed

Latty, Tanya; Beekman, Madeleine

2011-01-22

Most models of animal foraging and consumer choice assume that individuals make choices based on the absolute value of items and are therefore 'economically rational'. However, frequent violations of rationality by animals, including humans, suggest that animals use comparative valuation rules. Are comparative valuation strategies a consequence of the way brains process information, or are they an intrinsic feature of biological decision-making? Here, we examine the principles of rationality in an organism with radically different information-processing mechanisms: the brainless, unicellular, slime mould Physarum polycephalum. We offered P. polycephalum amoebas a choice between food options that varied in food quality and light exposure (P. polycephalum is photophobic). The use of an absolute valuation rule will lead to two properties: transitivity and independence of irrelevant alternatives (IIA). Transitivity is satisfied if preferences have a consistent, linear ordering, while IIA states that a decision maker's preference for an item should not change if the choice set is expanded. A violation of either of these principles suggests the use of comparative rather than absolute valuation rules. Physarum polycephalum satisfied transitivity by having linear preference rankings. However, P. polycephalum's preference for a focal alternative increased when a third, inferior quality option was added to the choice set, thus violating IIA and suggesting the use of a comparative valuation process. The discovery of comparative valuation rules in a unicellular organism suggests that comparative valuation rules are ubiquitous, if not universal, among biological decision makers.

Rural performance based planning guidebook.

DOT National Transportation Integrated Search

2017-02-01

Performance-based transportation planning has existed for many years. Recently, it is becoming : more accepted and practiced as a result of federal rules. It is fast becoming the cornerstone for : transportation decision making throughout the country...

Elements of decisional dynamics: An agent-based approach applied to artificial financial market

NASA Astrophysics Data System (ADS)

Lucas, Iris; Cotsaftis, Michel; Bertelle, Cyrille

2018-02-01

This paper introduces an original mathematical description for describing agents' decision-making process in the case of problems affected by both individual and collective behaviors in systems characterized by nonlinear, path dependent, and self-organizing interactions. An application to artificial financial markets is proposed by designing a multi-agent system based on the proposed formalization. In this application, agents' decision-making process is based on fuzzy logic rules and the price dynamics is purely deterministic according to the basic matching rules of a central order book. Finally, while putting most parameters under evolutionary control, the computational agent-based system is able to replicate several stylized facts of financial time series (distributions of stock returns showing a heavy tail with positive excess kurtosis, absence of autocorrelations in stock returns, and volatility clustering phenomenon).

Elements of decisional dynamics: An agent-based approach applied to artificial financial market.

PubMed

Lucas, Iris; Cotsaftis, Michel; Bertelle, Cyrille

2018-02-01

This paper introduces an original mathematical description for describing agents' decision-making process in the case of problems affected by both individual and collective behaviors in systems characterized by nonlinear, path dependent, and self-organizing interactions. An application to artificial financial markets is proposed by designing a multi-agent system based on the proposed formalization. In this application, agents' decision-making process is based on fuzzy logic rules and the price dynamics is purely deterministic according to the basic matching rules of a central order book. Finally, while putting most parameters under evolutionary control, the computational agent-based system is able to replicate several stylized facts of financial time series (distributions of stock returns showing a heavy tail with positive excess kurtosis, absence of autocorrelations in stock returns, and volatility clustering phenomenon).

Healthcare provider perceptions of clinical prediction rules

PubMed Central

Richardson, Safiya; Khan, Sundas; McCullagh, Lauren; Kline, Myriam; Mann, Devin; McGinn, Thomas

2015-01-01

Objectives To examine internal medicine and emergency medicine healthcare provider perceptions of usefulness of specific clinical prediction rules. Setting The study took place in two academic medical centres. A web-based survey was distributed and completed by participants between 1 January and 31 May 2013. Participants Medical doctors, doctors of osteopathy or nurse practitioners employed in the internal medicine or emergency medicine departments at either institution. Primary and secondary outcome measures The primary outcome was to identify the clinical prediction rules perceived as most useful by healthcare providers specialising in internal medicine and emergency medicine. Secondary outcomes included comparing usefulness scores of specific clinical prediction rules based on provider specialty, and evaluating associations between usefulness scores and perceived characteristics of these clinical prediction rules. Results Of the 401 healthcare providers asked to participate, a total of 263 (66%), completed the survey. The CHADS2 score was chosen by most internal medicine providers (72%), and Pulmonary Embolism Rule-Out Criteria (PERC) score by most emergency medicine providers (45%), as one of the top three most useful from a list of 24 clinical prediction rules. Emergency medicine providers rated their top three significantly more positively, compared with internal medicine providers, as having a better fit into their workflow (p=0.004), helping more with decision-making (p=0.037), better fitting into their thought process when diagnosing patients (p=0.001) and overall, on a 10-point scale, more useful (p=0.009). For all providers, the perceived qualities of useful at point of care, helps with decision making, saves time diagnosing, fits into thought process, and should be the standard of clinical care correlated highly (≥0.65) with overall 10-point usefulness scores. Conclusions Healthcare providers describe clear preferences for certain clinical prediction rules, based on medical specialty. PMID:26338684

Using Semantic Components to Represent Dynamics of an Interdisciplinary Healthcare Team in a Multi-Agent Decision Support System.

PubMed

Wilk, Szymon; Kezadri-Hamiaz, Mounira; Rosu, Daniela; Kuziemsky, Craig; Michalowski, Wojtek; Amyot, Daniel; Carrier, Marc

2016-02-01

In healthcare organizations, clinical workflows are executed by interdisciplinary healthcare teams (IHTs) that operate in ways that are difficult to manage. Responding to a need to support such teams, we designed and developed the MET4 multi-agent system that allows IHTs to manage patients according to presentation-specific clinical workflows. In this paper, we describe a significant extension of the MET4 system that allows for supporting rich team dynamics (understood as team formation, management and task-practitioner allocation), including selection and maintenance of the most responsible physician and more complex rules of selecting practitioners for the workflow tasks. In order to develop this extension, we introduced three semantic components: (1) a revised ontology describing concepts and relations pertinent to IHTs, workflows, and managed patients, (2) a set of behavioral rules describing the team dynamics, and (3) an instance base that stores facts corresponding to instances of concepts from the ontology and to relations between these instances. The semantic components are represented in first-order logic and they can be automatically processed using theorem proving and model finding techniques. We employ these techniques to find models that correspond to specific decisions controlling the dynamics of IHT. In the paper, we present the design of extended MET4 with a special focus on the new semantic components. We then describe its proof-of-concept implementation using the WADE multi-agent platform and the Z3 solver (theorem prover/model finder). We illustrate the main ideas discussed in the paper with a clinical scenario of an IHT managing a patient with chronic kidney disease.

An Intelligent Polar Cyberinfrastrucuture to Support Spatiotemporal Decision Making

NASA Astrophysics Data System (ADS)

Song, M.; Li, W.; Zhou, X.

2014-12-01

In the era of big data, polar sciences have already faced an urgent demand of utilizing intelligent approaches to support precise and effective spatiotemporal decision-making. Service-oriented cyberinfrastructure has advantages of seamlessly integrating distributed computing resources, and aggregating a variety of geospatial data derived from Earth observation network. This paper focuses on building a smart service-oriented cyberinfrastructure to support intelligent question answering related to polar datasets. The innovation of this polar cyberinfrastructure includes: (1) a problem-solving environment that parses geospatial question in natural language, builds geoprocessing rules, composites atomic processing services and executes the entire workflow; (2) a self-adaptive spatiotemporal filter that is capable of refining query constraints through semantic analysis; (3) a dynamic visualization strategy to support results animation and statistics in multiple spatial reference systems; and (4) a user-friendly online portal to support collaborative decision-making. By means of this polar cyberinfrastructure, we intend to facilitate integration of distributed and heterogeneous Arctic datasets and comprehensive analysis of multiple environmental elements (e.g. snow, ice, permafrost) to provide a better understanding of the environmental variation in circumpolar regions.

NRC safety research in support of regulation - FY 1994. Volume 9

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

NONE

This report, the tenth in a series of annual reports, was prepared in response to congressional inquiries concerning how nuclear regulatory research is used. It summarizes the accomplishments of the Office of Nuclear Regulatory Research during FY 1994. The goal of the Office of Nuclear Regulatory Research (RES) is to ensure the availability of sound technical bases for timely rulemaking and related decisions in support of NRC regulatory/licensing/inspection activities. RES also has responsibilities related to the resolution of generic safety issues and to the review of licensee submittals regarding individual plant examinations. It is the responsibility of RES to conductmore » the NRC`s rulemaking process, including the issuance of regulatory guides and rules that govern NRC licensed activities.« less

Impact of clinical decision rules on clinical care of traumatic injuries to the foot and ankle, knee, cervical spine, and head.

PubMed

Perry, Jeffrey J; Stiell, Ian G

2006-12-01

Traumatic injuries to the ankle/foot, knee, cervical spine, and head are very commonly seen in emergency and accident departments around the world. There has been much interest in the development of clinical decision rules to help guide the investigations of these patients in a standardised and cost-effective manner. In this article we reviewed the impact of the Ottawa ankle rules, Ottawa knee rules, Canadian C-spine rule and the Canadian CT head rule. The studies conducted have confirmed that the use of well developed clinical decision rules results in less radiography, less time spent in the emergency department and does not decrease patient satisfaction or result in misdiagnosis. Emergency physicians around the world should adopt the use of clinical decision rules for ankle/foot, knee, cervical spine and minor head injuries. With relatively simple implementation strategies, care can be standardized and costs reduced while providing excellent clinical care.

Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory

EPA Science Inventory

Efforts are increasingly being made to classify the world’s wetland resources, an important ecosystem and habitat that is diminishing in abundance. There are multiple remote sensing classification methods, including a suite of nonparametric classifiers such as decision-tree...

Applying Generalizability Theory for Making Quantitative RTI Progress-Monitoring Decisions

ERIC Educational Resources Information Center

Fan, Chung-Hau; Hansmann, Paul R.

2015-01-01

Language in the Individuals With Disabilities Education Improvement Act (IDEIA) allows the use of response-to-intervention (RTI) methodology in the identification of specific learning disabilities. However, there is no consensus on decision rules using curriculum-based measurement of oral reading fluency (CBM-R) for defining responsiveness. The…

17 CFR 171.1 - Scope of rules.

Code of Federal Regulations, 2011 CFR

2011-04-01

... the aggrieved party's appeal; (2) A decision in an arbitration action brought pursuant to section 17(b... associated with a member based solely on that person's failure to pay an arbitration award or a settlement... to pursue the right to appeal an adverse decision to the Appeals Committee of the National Futures...

17 CFR 171.1 - Scope of rules.

Code of Federal Regulations, 2010 CFR

2010-04-01

... the aggrieved party's appeal; (2) A decision in an arbitration action brought pursuant to section 17(b... associated with a member based solely on that person's failure to pay an arbitration award or a settlement... to pursue the right to appeal an adverse decision to the Appeals Committee of the National Futures...

Explanation-based learning in infancy.

PubMed

Baillargeon, Renée; DeJong, Gerald F

2017-10-01

In explanation-based learning (EBL), domain knowledge is leveraged in order to learn general rules from few examples. An explanation is constructed for initial exemplars and is then generalized into a candidate rule that uses only the relevant features specified in the explanation; if the rule proves accurate for a few additional exemplars, it is adopted. EBL is thus highly efficient because it combines both analytic and empirical evidence. EBL has been proposed as one of the mechanisms that help infants acquire and revise their physical rules. To evaluate this proposal, 11- and 12-month-olds (n = 260) were taught to replace their current support rule (that an object is stable when half or more of its bottom surface is supported) with a more sophisticated rule (that an object is stable when half or more of the entire object is supported). Infants saw teaching events in which asymmetrical objects were placed on a base, followed by static test displays involving a novel asymmetrical object and a novel base. When the teaching events were designed to facilitate EBL, infants learned the new rule with as few as two (12-month-olds) or three (11-month-olds) exemplars. When the teaching events were designed to impede EBL, however, infants failed to learn the rule. Together, these results demonstrate that even infants, with their limited knowledge about the world, benefit from the knowledge-based approach of EBL.

Heterogeneous data fusion and intelligent techniques embedded in a mobile application for real-time chronic disease management.

PubMed

Bellos, Christos; Papadopoulos, Athanassios; Rosso, Roberto; Fotiadis, Dimitrios I

2011-01-01

CHRONIOUS system is an integrated platform aiming at the management of chronic disease patients. One of the most important components of the system is a Decision Support System (DSS) that has been developed in a Smart Device (SD). This component decides on patient's current health status by combining several data, which are acquired either by wearable sensors or manually inputted by the patient or retrieved from the specific database. In case no abnormal situation has been tracked, the DSS takes no action and remains deactivated until next abnormal situation pack of data are being acquired or next scheduled data being transmitted. The DSS that has been implemented is an integrated classification system with two parallel classifiers, combining an expert system (rule-based system) and a supervised classifier, such as Support Vector Machines (SVM), Random Forests, artificial Neural Networks (aNN like the Multi-Layer Perceptron), Decision Trees and Naïve Bayes. The above categorized system is useful for providing critical information about the health status of the patient.

How Decision Support Systems Can Benefit from a Theory of Change Approach.

PubMed

Allen, Will; Cruz, Jennyffer; Warburton, Bruce

2017-06-01

Decision support systems are now mostly computer and internet-based information systems designed to support land managers with complex decision-making. However, there is concern that many environmental and agricultural decision support systems remain underutilized and ineffective. Recent efforts to improve decision support systems use have focused on enhancing stakeholder participation in their development, but a mismatch between stakeholders' expectations and the reality of decision support systems outputs continues to limit uptake. Additional challenges remain in problem-framing and evaluation. We propose using an outcomes-based approach called theory of change in conjunction with decision support systems development to support both wider problem-framing and outcomes-based monitoring and evaluation. The theory of change helps framing by placing the decision support systems within a wider context. It highlights how decision support systems use can "contribute" to long-term outcomes, and helps align decision support systems outputs with these larger goals. We illustrate the benefits of linking decision support systems development and application with a theory of change approach using an example of pest rabbit management in Australia. We develop a theory of change that outlines the activities required to achieve the outcomes desired from an effective rabbit management program, and two decision support systems that contribute to specific aspects of decision making in this wider problem context. Using a theory of change in this way should increase acceptance of the role of decision support systems by end-users, clarify their limitations and, importantly, increase effectiveness of rabbit management. The use of a theory of change should benefit those seeking to improve decision support systems design, use and, evaluation.

How Decision Support Systems Can Benefit from a Theory of Change Approach

NASA Astrophysics Data System (ADS)

Allen, Will; Cruz, Jennyffer; Warburton, Bruce

2017-06-01

Decision support systems are now mostly computer and internet-based information systems designed to support land managers with complex decision-making. However, there is concern that many environmental and agricultural decision support systems remain underutilized and ineffective. Recent efforts to improve decision support systems use have focused on enhancing stakeholder participation in their development, but a mismatch between stakeholders' expectations and the reality of decision support systems outputs continues to limit uptake. Additional challenges remain in problem-framing and evaluation. We propose using an outcomes-based approach called theory of change in conjunction with decision support systems development to support both wider problem-framing and outcomes-based monitoring and evaluation. The theory of change helps framing by placing the decision support systems within a wider context. It highlights how decision support systems use can "contribute" to long-term outcomes, and helps align decision support systems outputs with these larger goals. We illustrate the benefits of linking decision support systems development and application with a theory of change approach using an example of pest rabbit management in Australia. We develop a theory of change that outlines the activities required to achieve the outcomes desired from an effective rabbit management program, and two decision support systems that contribute to specific aspects of decision making in this wider problem context. Using a theory of change in this way should increase acceptance of the role of decision support systems by end-users, clarify their limitations and, importantly, increase effectiveness of rabbit management. The use of a theory of change should benefit those seeking to improve decision support systems design, use and, evaluation.

Machine Reading as a Cognitive Science Research Instrument

DTIC Science & Technology

2007-01-01

perspectives on decision making emerge: deontological and consequentialist. Deontological decisions are made based one’s duties or rules and the...there is a vaccine which would cure the disease, but also it will kill 1% of the recipients of the vaccine, a deontological decision maker would...Tanner, C., Medin D. L., Iliev R. (Forthcoming) "Influence of Deontological vs. Consequentialist Orientations on Act Choices and Framing Effects

Application of Fuzzy Logic in Oral Cancer Risk Assessment

PubMed Central

SCROBOTĂ, Ioana; BĂCIUȚ, Grigore; FILIP, Adriana Gabriela; TODOR, Bianca; BLAGA, Florin; BĂCIUȚ, Mihaela Felicia

2017-01-01

Background: The mapping of the malignization mechanism is still incomplete, but oxidative stress is strongly correlated to carcinogenesis. In our research, using fuzzy logic, we aimed to estimate the oxidative stress related-cancerization risk of the oral potentially malignant disorders. Methods: Serum from 16 patients diagnosed (clinical and histopathological) with oral potentially malignant disorders (Dept. of Cranio-Maxillofacial Surgery and Radiology, ”Iuliu Hațieganu” University of Medicine and Pharmacy, Cluj Napoca, Romania) was processed fluorometric for malondialdehyde and proton donors assays (Dept. of Physiology,”Iuliu Hațieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania). The values were used as inputs, they were associated linguistic terms using MIN-MAX method and 25 IF-THEN inference rules were generated to estimate the output value, the cancerization risk appreciated on a scale from 1 to 10 - IF malondialdehyde is very high and donors protons are very low THEN the cancer risk is reaching the maximum value (Dept. of Industrial Engineering, Faculty of Managerial and Technological Engineering, University of Oradea, Oradea, Romania) (2012–2014). Results: We estimated the cancerization risk of the oral potentially malignant disorders by implementing the multi-criteria decision support system based on serum malondialdehyde and proton donors’ values. The risk was estimated as a concrete numerical value on a scale from 1 to 10 depending on the input numerical/linguistic value. Conclusion: The multi-criteria decision support system proposed by us, integrated into a more complex computerized decision support system, could be used as an important aid in oral cancer screening and establish future medical decision in oral potentially malignant disorders. PMID:28560191

Application of Fuzzy Logic in Oral Cancer Risk Assessment.

PubMed

Scrobotă, Ioana; Băciuț, Grigore; Filip, Adriana Gabriela; Todor, Bianca; Blaga, Florin; Băciuț, Mihaela Felicia

2017-05-01

The mapping of the malignization mechanism is still incomplete, but oxidative stress is strongly correlated to carcinogenesis. In our research, using fuzzy logic, we aimed to estimate the oxidative stress related-cancerization risk of the oral potentially malignant disorders. Serum from 16 patients diagnosed (clinical and histopathological) with oral potentially malignant disorders (Dept. of Cranio-Maxillofacial Surgery and Radiology, "Iuliu Hațieganu" University of Medicine and Pharmacy, Cluj Napoca, Romania) was processed fluorometric for malondialdehyde and proton donors assays (Dept. of Physiology,"Iuliu Hațieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania). The values were used as inputs, they were associated linguistic terms using MIN-MAX method and 25 IF-THEN inference rules were generated to estimate the output value, the cancerization risk appreciated on a scale from 1 to 10 - IF malondialdehyde is very high and donors protons are very low THEN the cancer risk is reaching the maximum value (Dept. of Industrial Engineering, Faculty of Managerial and Technological Engineering, University of Oradea, Oradea, Romania) (2012-2014). We estimated the cancerization risk of the oral potentially malignant disorders by implementing the multi-criteria decision support system based on serum malondialdehyde and proton donors' values. The risk was estimated as a concrete numerical value on a scale from 1 to 10 depending on the input numerical/linguistic value. The multi-criteria decision support system proposed by us, integrated into a more complex computerized decision support system, could be used as an important aid in oral cancer screening and establish future medical decision in oral potentially malignant disorders.

Realistic decision-making processes in a vaccination game

NASA Astrophysics Data System (ADS)

Iwamura, Yoshiro; Tanimoto, Jun

2018-03-01

Previous studies of vaccination games have nearly always assumed a pairwise comparison between a focal and neighboring player for the strategy updating rule, which comes from numerous compiled studies on spatial versions of 2-player and 2-strategy (2 × 2) games such as the spatial prisoner's dilemma (SPD). We propose, in this study, new update rules because the human decision-making process of whether to commit to a vaccination is obviously influenced by a "sense of crisis" or "fear" urging him/her toward vaccination, otherwise they will likely be infected. The rule assumes that an agent evaluates whether getting a vaccination or trying to free ride should be attempted based on observations of whether neighboring non-vaccinators were able to successfully free ride during the previous time-step. Compared to the conventional updating rule (standard pairwise comparison assuming a Fermi function), the new rules generally realize higher vaccination coverage and smaller final epidemic sizes. One rule in particular shows very good performance with significantly smaller epidemic sizes despite comparable levels of vaccination coverage. This is because the specific update rule helps vaccinators spread widely in the domain, which effectively hampers the spread of epidemics.

Multi-criteria decision making--an approach to setting priorities in health care.

PubMed

Nobre, F F; Trotta, L T; Gomes, L F

1999-12-15

The objective of this paper is to present a multi-criteria decision making (MCDM) approach to support public health decision making that takes into consideration the fuzziness of the decision goals and the behavioural aspect of the decision maker. The approach is used to analyse the process of health technology procurement in a University Hospital in Rio de Janeiro, Brazil. The method, known as TODIM, relies on evaluating alternatives with a set of decision criteria assessed using an ordinal scale. Fuzziness in generating criteria scores and weights or conflicts caused by dealing with different viewpoints of a group of decision makers (DMs) are solved using fuzzy set aggregation rules. The results suggested that MCDM models, incorporating fuzzy set approaches, should form a set of tools for public health decision making analysis, particularly when there are polarized opinions and conflicting objectives from the DM group. Copyright 1999 John Wiley & Sons, Ltd.

Decision Rules Used in Academic Program Closure: Where the Rubber Meets the Road.

ERIC Educational Resources Information Center

Eckel, Peter D.

This study examines, from an organizational perspective, decision rules guiding program discontinuance, testing the framework of decision rule rationality versus action rationality. A multi-site case study method was used; interviews were conducted with 11-16 individuals at each of four research I or II universities that had discontinued at least…

Logical-rule models of classification response times: a synthesis of mental-architecture, random-walk, and decision-bound approaches.

PubMed

Fific, Mario; Little, Daniel R; Nosofsky, Robert M

2010-04-01

We formalize and provide tests of a set of logical-rule models for predicting perceptual classification response times (RTs) and choice probabilities. The models are developed by synthesizing mental-architecture, random-walk, and decision-bound approaches. According to the models, people make independent decisions about the locations of stimuli along a set of component dimensions. Those independent decisions are then combined via logical rules to determine the overall categorization response. The time course of the independent decisions is modeled via random-walk processes operating along individual dimensions. Alternative mental architectures are used as mechanisms for combining the independent decisions to implement the logical rules. We derive fundamental qualitative contrasts for distinguishing among the predictions of the rule models and major alternative models of classification RT. We also use the models to predict detailed RT-distribution data associated with individual stimuli in tasks of speeded perceptual classification. PsycINFO Database Record (c) 2010 APA, all rights reserved.

Probabilistic Reward- and Punishment-based Learning in Opioid Addiction: Experimental and Computational Data

PubMed Central

Myers, Catherine E.; Sheynin, Jony; Baldson, Tarryn; Luzardo, Andre; Beck, Kevin D.; Hogarth, Lee; Haber, Paul; Moustafa, Ahmed A.

2016-01-01

Addiction is the continuation of a habit in spite of negative consequences. A vast literature gives evidence that this poor decision-making behavior in individuals addicted to drugs also generalizes to laboratory decision making tasks, suggesting that the impairment in decision-making is not limited to decisions about taking drugs. In the current experiment, opioid-addicted individuals and matched controls with no history of illicit drug use were administered a probabilistic classification task that embeds both reward-based and punishment-based learning trials, and a computational model of decision making was applied to understand the mechanisms describing individuals’ performance on the task. Although behavioral results showed thatopioid-addicted individuals performed as well as controls on both reward- and punishment-based learning, the modeling results suggested subtle differences in how decisions were made between the two groups. Specifically, the opioid-addicted group showed decreased tendency to repeat prior responses, meaning that they were more likely to “chase reward” when expectancies were violated, whereas controls were more likely to stick with a previously-successful response rule, despite occasional expectancy violations. This tendency to chase short-term reward, potentially at the expense of developing rules that maximize reward over the long term, may be a contributing factor to opioid addiction. Further work is indicated to better understand whether this tendency arises as a result of brain changes in the wake of continued opioid use/abuse, or might be a pre-existing factor that may contribute to risk for addiction. PMID:26381438

Designing efficient nitrous oxide sampling strategies in agroecosystems using simulation models

NASA Astrophysics Data System (ADS)

Saha, Debasish; Kemanian, Armen R.; Rau, Benjamin M.; Adler, Paul R.; Montes, Felipe

2017-04-01

Annual cumulative soil nitrous oxide (N2O) emissions calculated from discrete chamber-based flux measurements have unknown uncertainty. We used outputs from simulations obtained with an agroecosystem model to design sampling strategies that yield accurate cumulative N2O flux estimates with a known uncertainty level. Daily soil N2O fluxes were simulated for Ames, IA (corn-soybean rotation), College Station, TX (corn-vetch rotation), Fort Collins, CO (irrigated corn), and Pullman, WA (winter wheat), representing diverse agro-ecoregions of the United States. Fertilization source, rate, and timing were site-specific. These simulated fluxes surrogated daily measurements in the analysis. We ;sampled; the fluxes using a fixed interval (1-32 days) or a rule-based (decision tree-based) sampling method. Two types of decision trees were built: a high-input tree (HI) that included soil inorganic nitrogen (SIN) as a predictor variable, and a low-input tree (LI) that excluded SIN. Other predictor variables were identified with Random Forest. The decision trees were inverted to be used as rules for sampling a representative number of members from each terminal node. The uncertainty of the annual N2O flux estimation increased along with the fixed interval length. A 4- and 8-day fixed sampling interval was required at College Station and Ames, respectively, to yield ±20% accuracy in the flux estimate; a 12-day interval rendered the same accuracy at Fort Collins and Pullman. Both the HI and the LI rule-based methods provided the same accuracy as that of fixed interval method with up to a 60% reduction in sampling events, particularly at locations with greater temporal flux variability. For instance, at Ames, the HI rule-based and the fixed interval methods required 16 and 91 sampling events, respectively, to achieve the same absolute bias of 0.2 kg N ha-1 yr-1 in estimating cumulative N2O flux. These results suggest that using simulation models along with decision trees can reduce the cost and improve the accuracy of the estimations of cumulative N2O fluxes using the discrete chamber-based method.

14 CFR 16.227 - Standard of proof.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 14 Aeronautics and Space 1 2014-01-01 2014-01-01 false Standard of proof. 16.227 Section 16.227 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION PROCEDURAL RULES RULES OF... hearing officer shall issue an initial decision or rule in a party's favor only if the decision or ruling...

75 FR 15499 - National Primary Drinking Water Regulations; Announcement of the Results of EPA's Review of...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-29

...--packed tower aeration PWS--public water system R2S2--Regulatory Review Support Spreadsheet RED...--hexavalent chromium CWS--community water system DBPs--disinfection byproducts DBCP--1,2-dibromo-3... Eligibility Decision IRIS--Integrated Risk Information System LCR--Lead and Copper Rule LH--lutenizing hormone...

40 CFR 52.2570 - Identification of plan.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Resources submitted revised rules NR 154.01 (126m), 154.02, 154.03 and 154.06. Support materials for these.... (A) The following Sections of Chapter 144 of the Wisconsin Statutes, entitled “Water, Sewage, Refuse...) Letter from the State dated July 13, 1984, stating that decisions made pursuant to NR 154.25 would be...

40 CFR 52.2570 - Identification of plan.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Resources submitted revised rules NR 154.01 (126m), 154.02, 154.03 and 154.06. Support materials for these.... (A) The following Sections of Chapter 144 of the Wisconsin Statutes, entitled “Water, Sewage, Refuse...) Letter from the State dated July 13, 1984, stating that decisions made pursuant to NR 154.25 would be...

Building Support for Student Success: A Framework for Achieving the Dream's State Policy Work

ERIC Educational Resources Information Center

Jobs for the Future, 2005

2005-01-01

This document is designed to help accelerate and strengthen state policy initiatives that promote student success; and derive lessons from this work about strategies for influencing the allocation of resources, the transparency of and accountability for results, and the rules that shape how institutions make decisions about priorities and…

Autonomous Flight Safety System

NASA Technical Reports Server (NTRS)

Simpson, James

2010-01-01

The Autonomous Flight Safety System (AFSS) is an independent self-contained subsystem mounted onboard a launch vehicle. AFSS has been developed by and is owned by the US Government. Autonomously makes flight termination/destruct decisions using configurable software-based rules implemented on redundant flight processors using data from redundant GPS/IMU navigation sensors. AFSS implements rules determined by the appropriate Range Safety officials.

An automated approach to the design of decision tree classifiers

NASA Technical Reports Server (NTRS)

Argentiero, P.; Chin, P.; Beaudet, P.

1980-01-01

The classification of large dimensional data sets arising from the merging of remote sensing data with more traditional forms of ancillary data is considered. Decision tree classification, a popular approach to the problem, is characterized by the property that samples are subjected to a sequence of decision rules before they are assigned to a unique class. An automated technique for effective decision tree design which relies only on apriori statistics is presented. This procedure utilizes a set of two dimensional canonical transforms and Bayes table look-up decision rules. An optimal design at each node is derived based on the associated decision table. A procedure for computing the global probability of correct classfication is also provided. An example is given in which class statistics obtained from an actual LANDSAT scene are used as input to the program. The resulting decision tree design has an associated probability of correct classification of .76 compared to the theoretically optimum .79 probability of correct classification associated with a full dimensional Bayes classifier. Recommendations for future research are included.

Simple heuristics in over-the-counter drug choices: a new hint for medical education and practice.

PubMed

Riva, Silvia; Monti, Marco; Antonietti, Alessandro

2011-01-01

Over-the-counter (OTC) drugs are widely available and often purchased by consumers without advice from a health care provider. Many people rely on self-management of medications to treat common medical conditions. Although OTC medications are regulated by the National and the International Health and Drug Administration, many people are unaware of proper dosing, side effects, adverse drug reactions, and possible medication interactions. This study examined how subjects make their decisions to select an OTC drug, evaluating the role of cognitive heuristics which are simple and adaptive rules that help the decision-making process of people in everyday contexts. By analyzing 70 subjects' information-search and decision-making behavior when selecting OTC drugs, we examined the heuristics they applied in order to assess whether simple decision-making processes were also accurate and relevant. Subjects were tested with a sequence of two experimental tests based on a computerized Java system devised to analyze participants' choices in a virtual environment. We found that subjects' information-search behavior reflected the use of fast and frugal heuristics. In addition, although the heuristics which correctly predicted subjects' decisions implied significantly fewer cues on average than the subjects did in the information-search task, they were accurate in describing order of information search. A simple combination of a fast and frugal tree and a tallying rule predicted more than 78% of subjects' decisions. The current emphasis in health care is to shift some responsibility onto the consumer through expansion of self medication. To know which cognitive mechanisms are behind the choice of OTC drugs is becoming a relevant purpose of current medical education. These findings have implications both for the validity of simple heuristics describing information searches in the field of OTC drug choices and for current medical education, which has to prepare competent health specialists to orientate and support the choices of their patients.

Simple heuristics in over-the-counter drug choices: a new hint for medical education and practice

PubMed Central

Riva, Silvia; Monti, Marco; Antonietti, Alessandro

2011-01-01

Introduction Over-the-counter (OTC) drugs are widely available and often purchased by consumers without advice from a health care provider. Many people rely on self-management of medications to treat common medical conditions. Although OTC medications are regulated by the National and the International Health and Drug Administration, many people are unaware of proper dosing, side effects, adverse drug reactions, and possible medication interactions. Purpose This study examined how subjects make their decisions to select an OTC drug, evaluating the role of cognitive heuristics which are simple and adaptive rules that help the decision-making process of people in everyday contexts. Subjects and methods By analyzing 70 subjects’ information-search and decision-making behavior when selecting OTC drugs, we examined the heuristics they applied in order to assess whether simple decision-making processes were also accurate and relevant. Subjects were tested with a sequence of two experimental tests based on a computerized Java system devised to analyze participants’ choices in a virtual environment. Results We found that subjects’ information-search behavior reflected the use of fast and frugal heuristics. In addition, although the heuristics which correctly predicted subjects’ decisions implied significantly fewer cues on average than the subjects did in the information-search task, they were accurate in describing order of information search. A simple combination of a fast and frugal tree and a tallying rule predicted more than 78% of subjects’ decisions. Conclusion The current emphasis in health care is to shift some responsibility onto the consumer through expansion of self medication. To know which cognitive mechanisms are behind the choice of OTC drugs is becoming a relevant purpose of current medical education. These findings have implications both for the validity of simple heuristics describing information searches in the field of OTC drug choices and for current medical education, which has to prepare competent health specialists to orientate and support the choices of their patients. PMID:23745077

How should we build a generic open-source water management simulator?

NASA Astrophysics Data System (ADS)

Khadem, M.; Meier, P.; Rheinheimer, D. E.; Padula, S.; Matrosov, E.; Selby, P. D.; Knox, S.; Harou, J. J.

2014-12-01

Increasing water needs for agriculture, industry and cities mean effective and flexible water resource system management tools will remain in high demand. Currently many regions or countries use simulators that have been adapted over time to their unique system properties and water management rules and realities. Most regions operate with a preferred short-list of water management and planning decision support systems. Is there scope for a simulator, shared within the water management community, that could be adapted to different contexts, integrate community contributions, and connect to generic data and model management software? What role could open-source play in such a project? How could a genericuser-interface and data/model management software sustainably be attached to this model or suite of models? Finally, how could such a system effectively leverage existing model formulations, modeling technologies and software? These questions are addressed by the initial work presented here. We introduce a generic water resource simulation formulation that enables and integrates both rule-based and optimization driven technologies. We suggest how it could be linked to other sub-models allowing for detailed agent-based simulation of water management behaviours. An early formulation is applied as an example to the Thames water resource system in the UK. The model uses centralised optimisation to calculate allocations but allows for rule-based operations as well in an effort to represent observed behaviours and rules with fidelity. The model is linked through import/export commands to a generic network model platform named Hydra. Benefits and limitations of the approach are discussed and planned work and potential use cases are outlined.

Awareness and use of the Ottawa ankle and knee rules in 5 countries: can publication alone be enough to change practice?

PubMed

Graham, I D; Stiell, I G; Laupacis, A; McAuley, L; Howell, M; Clancy, M; Durieux, P; Simon, N; Emparanza, J I; Aginaga, J R; O'connor, A; Wells, G

2001-03-01

We evaluate the international diffusion of the Ottawa Ankle and Knee Rules and determine emergency physicians' attitudes toward clinical decision rules in general. We conducted a cross-sectional, self-administered mail survey of random samples of 500 members each of the American College of Emergency Physicians, Canadian Association of Emergency Physicians, British Association for Accident and Emergency Medicine, Spanish Society for Emergency Medicine, and all members (n=1,350) of the French Speaking Society of Emergency Physicians, France. Main outcome measures were awareness of the Ottawa Ankle and Knee Rules, reported use of these rules, and attitudes toward clinical decision rules in general. A total of 1,769 (57%) emergency physicians responded, with country-specific response rates between 49% (United States and France) and 79% (Canada). More than 69% of physicians in all countries, except Spain, were aware of the Ottawa Ankle Rules. Use of the Ottawa Ankle Rules differed by country with more than 70% of all responding Canadian and United Kingdom physicians reporting frequent use of the rules compared with fewer than one third of US, French, and Spanish physicians. The Ottawa Knee Rule was less well known and less used by physicians in all countries. Most physicians in all countries viewed decision rules as intended to improve the quality of health care (>78%), a convenient source of advice (>67%), and good educational tools (>61%). Of all physicians, those from the United States held the least positive attitudes toward decision rules. This constitutes the largest international survey of emergency physicians' attitudes toward and use of clinical decision rules. Striking differences were apparent among countries with regard to knowledge and use of decision rules. Despite similar awareness in the United States, Canada, and the United Kingdom, US physicians appeared much less likely to use the Ottawa Ankle Rules. Future research should investigate factors leading to differences in rates of diffusion among countries and address strategies to enhance dissemination and implementation of such rules in the emergency department.

STAKEHOLDER INVOLVEMENT IN THE HEALTH TECHNOLOGY ASSESSMENT PROCESS IN LATIN AMERICA.

PubMed

Pichon-Riviere, Andres; Soto, Natalie; Augustovski, Federico; Sampietro-Colom, Laura

2018-06-11

Latin American countries are taking important steps to expand and strengthen universal health coverage, and health technology assessment (HTA) has an increasingly prominent role in this process. Participation of all relevant stakeholders has become a priority in this effort. Key issues in this area were discussed during the 2017 Latin American Health Technology Assessment International (HTAi) Policy Forum. The Forum included forty-one participants from Latin American HTA agencies; public, social security, and private insurance sectors; and the pharmaceutical and medical device industry. A background paper and presentations by invited experts and Forum members supported discussions. This study presents a summary of these discussions. Stakeholder involvement in HTA remains inconsistently implemented in the region and few countries have established formal processes. Participants agreed that stakeholder involvement is key to improve the HTA process, but the form and timing of such improvements must be adapted to local contexts. The legitimization of both HTA and decision-making processes was identified as one of the main reasons to promote stakeholder involvement; but to be successful, the entire system of assessment and decision making must be properly staffed and organized, and certain basic conditions must be met, including transparency in the HTA process and a clear link between HTA and decision making. Participants suggested a need for establishing clear rules of participation in HTA that would protect HTA producers and decision makers from potentially distorting external influences. Such rules and mechanisms could help foster trust and credibility among stakeholders, supporting actual involvement in HTA processes.

Research on web-based decision support system for sports competitions

NASA Astrophysics Data System (ADS)

Huo, Hanqiang

2010-07-01

This paper describes the system architecture and implementation technology of the decision support system for sports competitions, discusses the design of decision-making modules, management modules and security of the system, and proposes the development idea of building a web-based decision support system for sports competitions.

C-learning: A new classification framework to estimate optimal dynamic treatment regimes.

PubMed

Zhang, Baqun; Zhang, Min

2017-12-11

A dynamic treatment regime is a sequence of decision rules, each corresponding to a decision point, that determine that next treatment based on each individual's own available characteristics and treatment history up to that point. We show that identifying the optimal dynamic treatment regime can be recast as a sequential optimization problem and propose a direct sequential optimization method to estimate the optimal treatment regimes. In particular, at each decision point, the optimization is equivalent to sequentially minimizing a weighted expected misclassification error. Based on this classification perspective, we propose a powerful and flexible C-learning algorithm to learn the optimal dynamic treatment regimes backward sequentially from the last stage until the first stage. C-learning is a direct optimization method that directly targets optimizing decision rules by exploiting powerful optimization/classification techniques and it allows incorporation of patient's characteristics and treatment history to improve performance, hence enjoying advantages of both the traditional outcome regression-based methods (Q- and A-learning) and the more recent direct optimization methods. The superior performance and flexibility of the proposed methods are illustrated through extensive simulation studies. © 2017, The International Biometric Society.

Extracting fuzzy rules under uncertainty and measuring definability using rough sets

NASA Technical Reports Server (NTRS)

Culas, Donald E.

1991-01-01

Although computers have come a long way since their invention, they are basically able to handle only crisp values at the hardware level. Unfortunately, the world we live in consists of problems which fail to fall into this category, i.e., uncertainty is all too common. A problem is looked at which involves uncertainty. To be specific, attributes are dealt with which are fuzzy sets. Under this condition, knowledge is acquired by looking at examples. In each example, a condition as well as a decision is made available. Based on the examples given, two sets of rules are extracted, certain and possible. Furthermore, measures are constructed of how much these rules are believed in, and finally, the decisions are defined as a function of the terms used in the conditions.

Faults Discovery By Using Mined Data

NASA Technical Reports Server (NTRS)

Lee, Charles

2005-01-01

Fault discovery in the complex systems consist of model based reasoning, fault tree analysis, rule based inference methods, and other approaches. Model based reasoning builds models for the systems either by mathematic formulations or by experiment model. Fault Tree Analysis shows the possible causes of a system malfunction by enumerating the suspect components and their respective failure modes that may have induced the problem. The rule based inference build the model based on the expert knowledge. Those models and methods have one thing in common; they have presumed some prior-conditions. Complex systems often use fault trees to analyze the faults. Fault diagnosis, when error occurs, is performed by engineers and analysts performing extensive examination of all data gathered during the mission. International Space Station (ISS) control center operates on the data feedback from the system and decisions are made based on threshold values by using fault trees. Since those decision-making tasks are safety critical and must be done promptly, the engineers who manually analyze the data are facing time challenge. To automate this process, this paper present an approach that uses decision trees to discover fault from data in real-time and capture the contents of fault trees as the initial state of the trees.

Advanced Weather Awareness and Reporting Enhancements

NASA Technical Reports Server (NTRS)

Busquets, Anthony M. (Technical Monitor); Ruokangas, Corinne Clinton; Kelly, Wallace E., III

2005-01-01

AWARE (Aviation Weather Awareness and Reporting Enhancements) was a NASA Cooperative Research and Development program conducted jointly by Rockwell Scientific, Rockwell Collins, and NASA. The effort culminated in an enhanced weather briefing and reporting tool prototype designed to integrate graphical and text-based aviation weather data to provide clear situational awareness in the context of a specific pilot, flight and equipment profile. The initial implementation of AWARE was as a web-based preflight planning tool, specifically for general aviation pilots, who do not have access to support such as the dispatchers available for commercial airlines. Initial usability tests showed that for VFR (Visual Flight Rules) pilots, AWARE provided faster and more effective weather evaluation. In a subsequent formal usability test for IFR (Instrument Flight Rules) pilots, all users finished the AWARE tests faster than the parallel DUAT tests, and all subjects graded AWARE higher for effectiveness, efficiency, and usability. The decision analysis basis of AWARE differentiates it from other aviation safety programs, providing analysis of context-sensitive data in a personalized graphical format to aid pilots/dispatchers in their complex flight requirements.

Multiple kernel based feature and decision level fusion of iECO individuals for explosive hazard detection in FLIR imagery

NASA Astrophysics Data System (ADS)

Price, Stanton R.; Murray, Bryce; Hu, Lequn; Anderson, Derek T.; Havens, Timothy C.; Luke, Robert H.; Keller, James M.

2016-05-01

A serious threat to civilians and soldiers is buried and above ground explosive hazards. The automatic detection of such threats is highly desired. Many methods exist for explosive hazard detection, e.g., hand-held based sensors, downward and forward looking vehicle mounted platforms, etc. In addition, multiple sensors are used to tackle this extreme problem, such as radar and infrared (IR) imagery. In this article, we explore the utility of feature and decision level fusion of learned features for forward looking explosive hazard detection in IR imagery. Specifically, we investigate different ways to fuse learned iECO features pre and post multiple kernel (MK) support vector machine (SVM) based classification. Three MK strategies are explored; fixed rule, heuristics and optimization-based. Performance is assessed in the context of receiver operating characteristic (ROC) curves on data from a U.S. Army test site that contains multiple target and clutter types, burial depths and times of day. Specifically, the results reveal two interesting things. First, the different MK strategies appear to indicate that the different iECO individuals are all more-or-less important and there is not a dominant feature. This is reinforcing as our hypothesis was that iECO provides different ways to approach target detection. Last, we observe that while optimization-based MK is mathematically appealing, i.e., it connects the learning of the fusion to the underlying classification problem we are trying to solve, it appears to be highly susceptible to over fitting and simpler, e.g., fixed rule and heuristics approaches help us realize more generalizable iECO solutions.

A conceptual review of decision making in social dilemmas: applying a logic of appropriateness.

PubMed

Weber, J Mark; Kopelman, Shirli; Messick, David M

2004-01-01

Despite decades of experimental social dilemma research, "theoretical integration has proven elusive" (Smithson & Foddy, 1999, p. 14). To advance a theory of decision making in social dilemmas, this article provides a conceptual review of the literature that applies a "logic of appropriateness" (March, 1994) framework. The appropriateness framework suggests that people making decisions ask themselves (explicitly or implicitly), "What does a person like me do in a situation like this? " This question identifies 3 significant factors: recognition and classification of the kind of situation encountered, the identity of the individual making the decision, and the application of rules or heuristics in guiding behavioral choice. In contrast with dominant rational choice models, the appropriateness framework proposed accommodates the inherently social nature of social dilemmas, and the role of rule and heuristic based processing. Implications for the interpretation of past findings and the direction of future research are discussed.

SFINX-a drug-drug interaction database designed for clinical decision support systems.

PubMed

Böttiger, Ylva; Laine, Kari; Andersson, Marine L; Korhonen, Tuomas; Molin, Björn; Ovesjö, Marie-Louise; Tirkkonen, Tuire; Rane, Anders; Gustafsson, Lars L; Eiermann, Birgit

2009-06-01

The aim was to develop a drug-drug interaction database (SFINX) to be integrated into decision support systems or to be used in website solutions for clinical evaluation of interactions. Key elements such as substance properties and names, drug formulations, text structures and references were defined before development of the database. Standard operating procedures for literature searches, text writing rules and a classification system for clinical relevance and documentation level were determined. ATC codes, CAS numbers and country-specific codes for substances were identified and quality assured to ensure safe integration of SFINX into other data systems. Much effort was put into giving short and practical advice regarding clinically relevant drug-drug interactions. SFINX includes over 8,000 interaction pairs and is integrated into Swedish and Finnish computerised decision support systems. Over 31,000 physicians and pharmacists are receiving interaction alerts through SFINX. User feedback is collected for continuous improvement of the content. SFINX is a potentially valuable tool delivering instant information on drug interactions during prescribing and dispensing.

The US Court of Appeals for the D.C. Circuit Ruling to Stay the CSAPR

EPA Pesticide Factsheets

The United States Court of Appeals for the D.C. Circuit issued its ruling to stay the CSAPR pending judicial review. The court's decision is not a decision on the merits of the rule. EPA is ensuring the transition back to the Clean Air Interstate Rule.

Is expected utility theory normative for medical decision making?

PubMed

Cohen, B J

1996-01-01

Expected utility theory is felt by its proponents to be a normative theory of decision making under uncertainty. The theory starts with some simple axioms that are held to be rules that any rational person would follow. It can be shown that if one adheres to these axioms, a numerical quantity, generally referred to as utility, can be assigned to each possible outcome, with the preferred course of action being that which has the highest expected utility. One of these axioms, the independence principle, is controversial, and is frequently violated in experimental situations. Proponents of the theory hold that these violations are irrational. The independence principle is simply an axiom dictating consistency among preferences, in that it dictates that a rational agent should hold a specified preference given another stated preference. When applied to preferences between lotteries, the independence principle can be demonstrated to be a rule that is followed only when preferences are formed in a particular way. The logic of expected utility theory is that this demonstration proves that preferences should be formed in this way. An alternative interpretation is that this demonstrates that the independence principle is not a valid general rule of consistency, but in particular, is a rule that must be followed if one is to consistently apply the decision rule "choose the lottery that has the highest expected utility." This decision rule must be justified on its own terms as a valid rule of rationality by demonstration that violation would lead to decisions that conflict with the decision maker's goals. This rule does not appear to be suitable for medical decisions because often these are one-time decisions in which expectation, a long-run property of a random variable, would not seem to be applicable. This is particularly true for those decisions involving a non-trivial risk of death.

From guideline modeling to guideline execution: defining guideline-based decision-support services.

PubMed Central

Tu, S. W.; Musen, M. A.

2000-01-01

We describe our task-based approach to defining the guideline-based decision-support services that the EON system provides. We categorize uses of guidelines in patient-specific decision support into a set of generic tasks--making of decisions, specification of work to be performed, interpretation of data, setting of goals, and issuance of alert and reminders--that can be solved using various techniques. Our model includes constructs required for representing the knowledge used by these techniques. These constructs form a toolkit from which developers can select modeling solutions for guideline task. Based on the tasks and the guideline model, we define a guideline-execution architecture and a model of interactions between a decision-support server and clients that invoke services provided by the server. These services use generic interfaces derived from guideline tasks and their associated modeling constructs. We describe two implementations of these decision-support services and discuss how this work can be generalized. We argue that a well-defined specification of guideline-based decision-support services will facilitate sharing of tools that implement computable clinical guidelines. PMID:11080007

Motor command inhibition and the representation of response mode during motor imagery.

PubMed

Scheil, Juliane; Liefooghe, Baptist

2018-05-01

Research on motor imagery proposes that overt actions during motor imagery can be avoided by proactively signaling subthreshold motor commands to the effectors and by invoking motor-command inhibition. A recent study by Rieger, Dahm, and Koch (2017) found evidence in support of motor command inhibition, which indicates that MI cannot be completed on the sole basis of subthreshold motor commands. However, during motor imagery, participants know in advance when a covert response is to be made and it is thus surprising such additional motor-command inhibition is needed. Accordingly, the present study tested whether the demand to perform an action covertly can be proactively integrated by investigating the formation of task-specific action rules during motor imagery. These task-specific action rules relate the decision rules of a task to the mode in which these rules need to be applied (e.g., if smaller than 5, press the left key covertly). To this end, an experiment was designed in which participants had to switch between two numerical judgement tasks and two response modes: covert responding and overt responding. First, we observed markers of motor command inhibition and replicated the findings of Rieger and colleagues. Second, we observed evidence suggesting that task-specific action rules are created for the overt response mode (e.g., if smaller than 5, press the left key). In contrast, for the covert response mode, no task-specific action rules are formed and decision rules do not include mode-specific information (e.g., if smaller than 5, left). Copyright © 2018 Elsevier B.V. All rights reserved.

Surrogate decision making and intellectual virtue.

PubMed

Bock, Gregory L

2014-01-01

Patients can be harmed by a religiously motivated surrogate decision maker whose decisions are contrary to the standard of care; therefore, surrogate decision making should be held to a high standard. Stewart Eskew and Christopher Meyers proposed a two-part rule for deciding which religiously based decisions to honor: (1) a secular reason condition and (2) a rationality condition. The second condition is based on a coherence theory of rationality, which they claim is accessible, generous, and culturally sensitive. In this article, I will propose strengthening the rationality condition by grounding it in a theory of intellectual virtue, which is both rigorous and culturally sensitive. Copyright 2014 The Journal of Clinical Ethics. All rights reserved.

The conscious mind and its emergent properties; an analysis based on decision theory.

PubMed

Morris, James A

2011-08-01

The process of conscious and unconscious decision making is analyzed using decision theory. An essential part of an optimum decision strategy is the assessment of values and costs associated with correct and incorrect decisions. In the case of unconscious decisions this involves an automatic process akin to computation using numerical values. But for conscious decisions the conscious mind must experience the outcome of the decision as pleasure or pain. It is suggested that the rules of behavior are programmed in our genes but modified by experience of the society in which we are reared. Our unconscious then uses the rules to reward or punish our conscious mind for the decisions it makes. This is relevant to concepts of altruism and religion in society. It is consistent with the observation that we prefer beauty to utility. The decision theory equations also explain the paradox that a single index of happiness can be applied in society. The symptoms of mental illness can be due to appropriate or inappropriate action by the unconscious. The former indicates a psychological conflict between conscious and unconscious decision making. Inappropriate action indicates that a pathological process has switched on genetic networks that should be switched off. Copyright © 2011 Elsevier Ltd. All rights reserved.

A clinical decision rule and D-dimer testing to rule out upper extremity deep vein thrombosis in high-risk patients.

PubMed

van Es, Nick; Bleker, Suzanne M; Di Nisio, Marcello; Kleinjan, Ankie; Beyer-Westendorf, Jan; Camporese, Giuseppe; Kamphuisen, Pieter W; Büller, Harry R; Bossuyt, Patrick M

2016-12-01

In a management study, a diagnostic algorithm consisting of a clinical decision rule, D-dimer, and ultrasonography was shown to safely exclude upper extremity deep vein thrombosis (UEDVT). Efficiency may be lower in high-risk subgroups: those with a central venous catheter or pacemaker, inpatients, cancer, and elderly patients. Data of 406 patients with suspected UEDVT enrolled in a prospective management study were used for the present analysis. The aim was to evaluate the efficiency of the algorithm in subgroups, defined as the proportion of patients in whom imaging could be safely withheld based on the combination of a decision rule result indicating "UEDVT unlikely" and a normal D-dimer result. The strategy excluded UEDVT in 87 of 406 patients (21%); ultrasonography was withheld in these patients and none developed UEDVT during 3months of follow-up. In contrast, ultrasonography could be withheld in only 4 of 92 patients with a catheter or pacemaker (4.3%; 95% CI: 1.7% to 11%) and in 4 of 83 inpatients (4.8%; 95% CI: 1.9% to 12%). The efficiency was 11% in patients with cancer and 13% in those older than 75years. Although the combination of a decision rule and D-dimer testing is safe in excluding UEDVT in the overall population of patients with suspected UEDVT, its efficiency appears limited in some subgroups, in particular those with a central venous catheter or pacemaker, and inpatients. Copyright © 2016 Elsevier Ltd. All rights reserved.

76 FR 34815 - Wyoming Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-14

...We are issuing a final decision on an amendment to the Wyoming regulatory program (the ``Wyoming program'') under the Surface Mining Control and Reclamation Act of 1977 (``SMCRA'' or ``the Act''). Our decision approves in part, disapproves in part and defers in part the amendment. Wyoming proposed to amend Chapters 1, 2, 4, 5, and Appendix A of the Land Quality Division (LQD) Coal Rules and Regulations to address required program amendments and other deficiencies identified by OSMRE, and to improve and clarify rules relating to requirements for vegetation measurements and performance standards. Specifically, the proposed changes clarify baseline vegetation requirements and revegetation reclamation plan requirements, clarify revegetation success standards and codify normal husbandry practices, reorganize and clarify species diversity and shrub density requirements, and revise and add definitions supporting those proposed changes. Wyoming also proposed changes to its rules in Chapters 2, 4, and 5 regarding cultural and historic resources, prime farmland, siltation structures and impoundments, and operator information. Wyoming revised its program to be consistent with the corresponding Federal regulations and SMCRA, clarify ambiguities, and improve operational efficiency.

Family and peer support matter for precoital and coital behaviors among adolescents in Lima

PubMed Central

Bayer, Angela M.; Cabrera, Lilia Z.; Gilman, Robert H.; Hindin, Michelle J.; Tsui, Amy O.

2015-01-01

We analyzed the association between sub-scales developed with adolescents and the outcomes of precoital behaviors and vaginal sex in Lima, Peru. Adolescent participants in key informant sessions operationalized concepts identified during qualitative concept mapping into several sub-scales. Face and content validity testing and pilot application with respondent debriefing were used to refine the sub-scales. Three hundred 15–17 year olds were surveyed about the sub-scales, socio-demographics and sexual behaviors. Exploratory factor analysis confirmed six sub-scales, self-image, goals and decision-making, family education, parental rules/control, school support and peer support, which we regressed on the outcomes. Twice as many males as females reported more than three precoital behaviors and vaginal sex. Higher peer support reduced the likelihood of vaginal sex and precoital behaviors and higher family education reduced precoital behaviors. Results affirm the importance of including adolescents in the entire research process and of sex education with family- and peer-based strategies. PMID:25305443

Decision Tree Repository and Rule Set Based Mingjiang River Estuarine Wetlands Classifaction

NASA Astrophysics Data System (ADS)

Zhang, W.; Li, X.; Xiao, W.

2018-05-01

The increasing urbanization and industrialization have led to wetland losses in estuarine area of Mingjiang River over past three decades. There has been increasing attention given to produce wetland inventories using remote sensing and GIS technology. Due to inconsistency training site and training sample, traditionally pixel-based image classification methods can't achieve a comparable result within different organizations. Meanwhile, object-oriented image classification technique shows grate potential to solve this problem and Landsat moderate resolution remote sensing images are widely used to fulfill this requirement. Firstly, the standardized atmospheric correct, spectrally high fidelity texture feature enhancement was conducted before implementing the object-oriented wetland classification method in eCognition. Secondly, we performed the multi-scale segmentation procedure, taking the scale, hue, shape, compactness and smoothness of the image into account to get the appropriate parameters, using the top and down region merge algorithm from single pixel level, the optimal texture segmentation scale for different types of features is confirmed. Then, the segmented object is used as the classification unit to calculate the spectral information such as Mean value, Maximum value, Minimum value, Brightness value and the Normalized value. The Area, length, Tightness and the Shape rule of the image object Spatial features and texture features such as Mean, Variance and Entropy of image objects are used as classification features of training samples. Based on the reference images and the sampling points of on-the-spot investigation, typical training samples are selected uniformly and randomly for each type of ground objects. The spectral, texture and spatial characteristics of each type of feature in each feature layer corresponding to the range of values are used to create the decision tree repository. Finally, with the help of high resolution reference images, the random sampling method is used to conduct the field investigation, achieve an overall accuracy of 90.31 %, and the Kappa coefficient is 0.88. The classification method based on decision tree threshold values and rule set developed by the repository, outperforms the results obtained from the traditional methodology. Our decision tree repository and rule set based object-oriented classification technique was an effective method for producing comparable and consistency wetlands data set.

Implementation of workflow engine technology to deliver basic clinical decision support functionality.

PubMed

Huser, Vojtech; Rasmussen, Luke V; Oberg, Ryan; Starren, Justin B

2011-04-10

Workflow engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic. We present our implementation of a workflow engine technology that addresses the two above-described challenges in delivering clinical decision support. Our system is based on a cross-industry standard of XML (extensible markup language) process definition language (XPDL). The core components of the system are a workflow editor for modeling clinical scenarios and a workflow engine for execution of those scenarios. We demonstrate, with an open-source and publicly available workflow suite, that clinical decision support logic can be executed on retrospective data. The same flowchart-based representation can also function in a prospective mode where the system can be integrated with an EHR system and respond to real-time clinical events. We limit the scope of our implementation to decision support content generation (which can be EHR system vendor independent). We do not focus on supporting complex decision support content delivery mechanisms due to lack of standardization of EHR systems in this area. We present results of our evaluation of the flowchart-based graphical notation as well as architectural evaluation of our implementation using an established evaluation framework for clinical decision support architecture. We describe an implementation of a free workflow technology software suite (available at http://code.google.com/p/healthflow) and its application in the domain of clinical decision support. Our implementation seamlessly supports clinical logic testing on retrospective data and offers a user-friendly knowledge representation paradigm. With the presented software implementation, we demonstrate that workflow engine technology can provide a decision support platform which evaluates well against an established clinical decision support architecture evaluation framework. Due to cross-industry usage of workflow engine technology, we can expect significant future functionality enhancements that will further improve the technology's capacity to serve as a clinical decision support platform.

46 CFR 201.159 - Decisions; contents and service.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 46 Shipping 8 2012-10-01 2012-10-01 false Decisions; contents and service. 201.159 Section 201.159 Shipping MARITIME ADMINISTRATION, DEPARTMENT OF TRANSPORTATION POLICY, PRACTICE AND PROCEDURE RULES OF PRACTICE AND PROCEDURE Briefs, Requests for Findings, Decisions, Exceptions (Rule 16) § 201.159 Decisions; contents and service. All initial,...

Comprehensible knowledge model creation for cancer treatment decision making.

PubMed

Afzal, Muhammad; Hussain, Maqbool; Ali Khan, Wajahat; Ali, Taqdir; Lee, Sungyoung; Huh, Eui-Nam; Farooq Ahmad, Hafiz; Jamshed, Arif; Iqbal, Hassan; Irfan, Muhammad; Abbas Hydari, Manzar

2017-03-01

A wealth of clinical data exists in clinical documents in the form of electronic health records (EHRs). This data can be used for developing knowledge-based recommendation systems that can assist clinicians in clinical decision making and education. One of the big hurdles in developing such systems is the lack of automated mechanisms for knowledge acquisition to enable and educate clinicians in informed decision making. An automated knowledge acquisition methodology with a comprehensible knowledge model for cancer treatment (CKM-CT) is proposed. With the CKM-CT, clinical data are acquired automatically from documents. Quality of data is ensured by correcting errors and transforming various formats into a standard data format. Data preprocessing involves dimensionality reduction and missing value imputation. Predictive algorithm selection is performed on the basis of the ranking score of the weighted sum model. The knowledge builder prepares knowledge for knowledge-based services: clinical decisions and education support. Data is acquired from 13,788 head and neck cancer (HNC) documents for 3447 patients, including 1526 patients of the oral cavity site. In the data quality task, 160 staging values are corrected. In the preprocessing task, 20 attributes and 106 records are eliminated from the dataset. The Classification and Regression Trees (CRT) algorithm is selected and provides 69.0% classification accuracy in predicting HNC treatment plans, consisting of 11 decision paths that yield 11 decision rules. Our proposed methodology, CKM-CT, is helpful to find hidden knowledge in clinical documents. In CKM-CT, the prediction models are developed to assist and educate clinicians for informed decision making. The proposed methodology is generalizable to apply to data of other domains such as breast cancer with a similar objective to assist clinicians in decision making and education. Copyright © 2017 Elsevier Ltd. All rights reserved.

Judgment and decision making.

PubMed

Mellers, B A; Schwartz, A; Cooke, A D

1998-01-01

For many decades, research in judgment and decision making has examined behavioral violations of rational choice theory. In that framework, rationality is expressed as a single correct decision shared by experimenters and subjects that satisfies internal coherence within a set of preferences and beliefs. Outside of psychology, social scientists are now debating the need to modify rational choice theory with behavioral assumptions. Within psychology, researchers are debating assumptions about errors for many different definitions of rationality. Alternative frameworks are being proposed. These frameworks view decisions as more reasonable and adaptive that previously thought. For example, "rule following." Rule following, which occurs when a rule or norm is applied to a situation, often minimizes effort and provides satisfying solutions that are "good enough," though not necessarily the best. When rules are ambiguous, people look for reasons to guide their decisions. They may also let their emotions take charge. This chapter presents recent research on judgment and decision making from traditional and alternative frameworks.

The utility of decision support, clinical guidelines, and financial incentives as tools to achieve improved clinical performance.

PubMed

Goldfarb, S

1999-03-01

Whether one seeks to reduce inappropriate utilization of resources, improve diagnostic accuracy, increase utilization of effective therapies, or reduce the incidence of complications, the key to change is physician involvement in change. Unfortunately, a simple approach to the problem of inducing change in physician behavior is not available. There is a generally accepted view that expert, best-practice guidelines will improve clinical performance. However, there may be a bias to report positive results and a lack of careful analysis of guideline usage in routine practice in a "postmarketing" study akin to that seen in the pharmaceutical industry. Systems that allow the reliable assessment of quality of outcomes, efficiency of resource utilization, and accurate assessment of the risks associated with the care of given patient populations must be widely available before deciding whether an incentive-based system for providing the full range of medical care is feasible. Decision support focuses on providing information, ideally at the "point of service" and in the context of a particular clinical situation. Rules are self-imposed by physicians and are therefore much more likely to be adopted. As health care becomes corporatized, with increasing numbers of physicians employed by large organizations with the capacity to provide detailed information on the nature and quality of clinical care, it is possible that properly constructed guidelines, appropriate financial incentives, and robust forms of decision support will lead to a physician-led, process improvement approach to more rational and affordable health care.

Acute knee injuries: use of decision rules for selective radiograph ordering.

PubMed

Tandeter, H B; Shvartzman, P; Stevens, Max A

1999-12-01

Family physicians often encounter patients with acute knee trauma. Radiographs of injured knees are commonly ordered, even though fractures are found in only 6 percent of such patients and emergency department physicians can usually discriminate clinically between fracture and nonfracture. Decision rules have been developed to reduce the unnecessary use of radiologic studies in patients with acute knee injury. The Ottawa knee rules and the Pittsburgh decision rules are the latest guidelines for the selective use of radiographs in knee trauma. Application of these rules may lead to a more efficient evaluation of knee injuries and a reduction in health costs without an increase in adverse outcomes.

A Non-technical User-Oriented Display Notation for XACML Conditions

NASA Astrophysics Data System (ADS)

Stepien, Bernard; Felty, Amy; Matwin, Stan

Ideally, access control to resources in complex IT systems ought to be handled by business decision makers who own a given resource (e.g., the pay and benefits section of an organization should decide and manage the access rules to the payroll system). To make this happen, the security and database communities need to develop vendor-independent access management tools, useable by decision makers, rather than technical personnel detached from a given business function. We have developed and implemented such tool, based on XACML. The XACML is an important emerging tool for managing complex access control applications. As a formal notation, based on an XML schema representing the grammar of a given application, XACML is precise and non-ambiguous. But this very property puts it out of reach of non-technical users. We propose a new notation for displaying and editing XACML rules that is independent of XML, and we develop an editor for it. Our notation combines a tree representation of logical expressions with an accessible natural language layer. Our early experience indicates that such rules can be grasped by non-technical users wishing to develop and control rules for accessing their own resources.

Innovative Tools for Water Quality/Quantity Management: New York City's Operations Support Tool

NASA Astrophysics Data System (ADS)

Wang, L.; Schaake, J. C.; Day, G. N.; Porter, J.; Sheer, D. P.; Pyke, G.

2011-12-01

The New York City Department of Environmental Protection (DEP) manages New York City's water supply, which is comprised of over 20 reservoirs and supplies more than 1 billion gallons of water per day to over 9 million customers. Recently, DEP has initiated design of an Operations Support Tool (OST), a state-of-the-art decision support system to provide computational and predictive support for water supply operations and planning. This presentation describes the technical structure of OST, including the underlying water supply and water quality models, data sources and database management, reservoir inflow forecasts, and the functionalities required to meet the needs of a diverse group of end users. OST is a major upgrade of DEP's current water supply - water quality model, developed to evaluate alternatives for controlling turbidity in NYC's Catskill reservoirs. While the current model relies on historical hydrologic and meteorological data, OST can be driven by forecasted future conditions. It will receive a variety of near-real-time data from a number of sources. OST will support two major types of simulations: long-term, for evaluating policy or infrastructure changes over an extended period of time; and short-term "position analysis" (PA) simulations, consisting of multiple short simulations, all starting from the same initial conditions. Typically, the starting conditions for a PA run will represent those for the current day and traces of forecasted hydrology will drive the model for the duration of the simulation period. The result of these simulations will be a distribution of future system states based on system operating rules and the range of input ensemble streamflow predictions. DEP managers will analyze the output distributions and make operation decisions using risk-based metrics such as probability of refill. Currently, in the developmental stages of OST, forecasts are based on antecedent hydrologic conditions and are statistical in nature. The statistical algorithm is a relatively simple and versatile, but lacks short-term skill critical for water quality and spill management. To improve short-term skill, OST will ultimately operate with meteorologically driven hydrologic forecasts provided by the National Weather Service (NWS). OST functionalities will support a wide range of DEP uses, including short term operational projections, outage planning and emergency management, operating rule development, and water supply planning. A core use of OST will be to inform reservoir management strategies to control and mitigate turbidity events while ensuring water supply reliability. OST will also allow DEP to manage its complex reservoir system to meet multiple objectives, including ecological flows, tailwater fisheries and recreational releases, and peak flow mitigation for downstream communities.

Web-services-based spatial decision support system to facilitate nuclear waste siting

NASA Astrophysics Data System (ADS)

Huang, L. Xinglai; Sheng, Grant

2006-10-01

The availability of spatial web services enables data sharing among managers, decision and policy makers and other stakeholders in much simpler ways than before and subsequently has created completely new opportunities in the process of spatial decision making. Though generally designed for a certain problem domain, web-services-based spatial decision support systems (WSDSS) can provide a flexible problem-solving environment to explore the decision problem, understand and refine problem definition, and generate and evaluate multiple alternatives for decision. This paper presents a new framework for the development of a web-services-based spatial decision support system. The WSDSS is comprised of distributed web services that either have their own functions or provide different geospatial data and may reside in different computers and locations. WSDSS includes six key components, namely: database management system, catalog, analysis functions and models, GIS viewers and editors, report generators, and graphical user interfaces. In this study, the architecture of a web-services-based spatial decision support system to facilitate nuclear waste siting is described as an example. The theoretical, conceptual and methodological challenges and issues associated with developing web services-based spatial decision support system are described.

Effectiveness of Implementing Evidence-based Interventions to Reduce C-spine Image Ordering in the Emergency Department: A Systematic Review.

PubMed

Desai, Shashwat; Liu, Chaocheng; Kirkland, Scott W; Krebs, Lynette D; Keto-Lambert, Diana; Rowe, Brian H

2018-06-01

Appropriate use of imaging for adult patients with cervical spine (C-spine) injuries in the emergency department (ED) is a longstanding issue. Guidance for C-spine ordering exists; however, the effectiveness of the decision support implementation in the ED is not well studied. This systematic review examines the implementation and effectiveness of evidence-based interventions aimed at reducing C-spine imaging in adults presenting to the ED with neck trauma. Six electronic databases and the gray literature were searched. Comparative intervention studies were eligible for inclusion. Two independent reviewers screened for study eligibility, study quality, and extracted data. The change in imaging was reported using individual odds ratios (ORs) with 95% confidence intervals (CIs) using random effects. A total of 990 unique citations were screened for relevance of which six before-after studies and one randomized controlled trial were included. None of the studies were assessed as high quality. Interventions consisted primarily of locally developed guidelines or established clinical decision rules such as the NEXUS or the Canadian C-spine rule. Overall, implementation of interventions aimed at reducing C-spine image ordering resulted in a statistically significant reduction in imaging (OR = 0.69, 95% CI = 0.51-0.93); however, heterogeneity was high (I 2  = 82%). Subgroup analysis revealed no differences between studies that specified enrolling alert and stable patients compared to unspecified trauma (p = 0.81) or between studies employing multifaceted versus nonmultifaceted interventions (p = 0.66). While studies generally provided details on implementation strategies (e.g., teaching sessions, pocket cards, posters, computerized decision support) the effectiveness of these implementation strategies were frequently not reported. There is moderate evidence regarding the effectiveness of interventions to reduce C-spine image ordering in adult patients seen in the ED with neck trauma. Given the national and international focus on improving appropriateness and reducing unnecessary C-spine imaging through campaigns such as Choosing Wisely, additional interventional research in this field is warranted. © 2017 by the Society for Academic Emergency Medicine.

Collective decision making and social interaction rules in mixed-species flocks of songbirds

PubMed Central

Farine, Damien R.; Aplin, Lucy M.; Garroway, Colin J.; Mann, Richard P.; Sheldon, Ben C.

2014-01-01

Associations in mixed-species foraging groups are common in animals, yet have rarely been explored in the context of collective behaviour. Despite many investigations into the social and ecological conditions under which individuals should form groups, we still know little about the specific behavioural rules that individuals adopt in these contexts, or whether these can be generalized to heterospecifics. Here, we studied collective behaviour in flocks in a community of five species of woodland passerine birds. We adopted an automated data collection protocol, involving visits by RFID-tagged birds to feeding stations equipped with antennae, over two winters, recording 91 576 feeding events by 1904 individuals. We demonstrated highly synchronized feeding behaviour within patches, with birds moving towards areas of the patch with the largest proportion of the flock. Using a model of collective decision making, we then explored the underlying decision rule birds may be using when foraging in mixed-species flocks. The model tested whether birds used a different decision rule for conspecifics and heterospecifics, and whether the rules used by individuals of different species varied. We found that species differed in their response to the distribution of conspecifics and heterospecifics across foraging patches. However, simulating decisions using the different rules, which reproduced our data well, suggested that the outcome of using different decision rules by each species resulted in qualitatively similar overall patterns of movement. It is possible that the decision rules each species uses may be adjusted to variation in mean species abundance in order for individuals to maintain the same overall flock-level response. This is likely to be important for maintaining coordinated behaviour across species, and to result in quick and adaptive flock responses to food resources that are patchily distributed in space and time. PMID:25214653

Utility of Decision Rules for Transcutaneous Bilirubin Measurements

PubMed Central

Burgos, Anthony E.; Flaherman, Valerie; Chung, Esther K.; Simpson, Elizabeth A.; Goyal, Neera K.; Von Kohorn, Isabelle; Dhepyasuwan, Niramol

2016-01-01

BACKGROUND: Transcutaneous bilirubin (TcB) meters are widely used for screening newborns for jaundice, with a total serum bilirubin (TSB) measurement indicated when the TcB value is classified as “positive” by using a decision rule. The goal of our study was to assess the clinical utility of 3 recommended TcB screening decision rules. METHODS: Paired TcB/TSB measurements were collected at 34 newborn nursery sites. At 27 sites (sample 1), newborns were routinely screened with a TcB measurement. For sample 2, sites that typically screen with TSB levels also obtained a TcB measurement for the study. Three decision rules to define a positive TcB measurement were evaluated: ≥75th percentile on the Bhutani nomogram, 70% of the phototherapy level, and within 3 mg/dL of the phototherapy threshold. The primary outcome was a TSB level at/above the phototherapy threshold. The rate of false-negative TcB screens and percentage of blood draws avoided were calculated for each decision rule. RESULTS: For sample 1, data were analyzed on 911 paired TcB-TSB measurements from a total of 8316 TcB measurements. False-negative rates were <10% with all decision rules; none identified all 31 newborns with a TSB level at/above the phototherapy threshold. The percentage of blood draws avoided ranged from 79.4% to 90.7%. In sample 2, each rule correctly identified all 8 newborns with TSB levels at/above the phototherapy threshold. CONCLUSIONS: Although all of the decision rules can be used effectively to screen newborns for jaundice, each will “miss” some infants with a TSB level at/above the phototherapy threshold. PMID:27244792

An Evolutionary Complex Systems Decision-Support Tool for the Management of Operations

NASA Astrophysics Data System (ADS)

Baldwin, J. S.; Allen, P. M.; Ridgway, K.

2011-12-01

This research aimed to add both to the development of complex systems thinking in the subject area of Operations and Production Management and to the limited number of applications of computational models and simulations from the science of complex systems. The latter potentially offer helpful decision-support tools for operations and production managers. A mechanical engineering firm was used as a case study where a combined qualitative and quantitative methodological approach was employed to extract the required data from four senior managers. Company performance measures as well as firm technologies, practices and policies, and their relation and interaction with one another, were elicited. The data were subjected to an evolutionary complex systems model resulting in a series of simulations. The findings included both reassuring and some unexpected results. The simulation based on the CEO's opinions led the most cohesive and synergistic collection of practices describing the firm, closely followed by the Marketing and R&D Managers. The Manufacturing Manager's responses led to the most extreme evolutionary trajectory where the integrity of the entire firm came into question particularly when considering how employees were utilised. By drawing directly from the opinions and views of managers rather than from logical 'if-then' rules and averaged mathematical representations of agents that characterise agent-based and other self-organisational models, this work builds on previous applications by capturing a micro-level description of diversity and a learning effect that has been problematical not only in terms of theory but also in application. This approach can be used as a decision-support tool for operations and other managers providing a forum with which to explore a) the strengths, weaknesses and consequences of different decision-making capacities within the firm; b) the introduction of new manufacturing technologies, practices and policies; and, c) the different evolutionary trajectories that a firm can take.

Developing Novel Reservoir Rule Curves Using Seasonal Inflow Projections

NASA Astrophysics Data System (ADS)

Tseng, Hsin-yi; Tung, Ching-pin

2015-04-01

Due to significant seasonal rainfall variations, reservoirs and their flexible operational rules are indispensable to Taiwan. Furthermore, with the intensifying impacts of climate change on extreme climate, the frequency of droughts in Taiwan has been increasing in recent years. Drought is a creeping phenomenon, the slow onset character of drought makes it difficult to detect at an early stage, and causes delays on making the best decision of allocating water. For these reasons, novel reservoir rule curves using projected seasonal streamflow are proposed in this study, which can potentially reduce the adverse effects of drought. This study dedicated establishing new rule curves which consider both current available storage and anticipated monthly inflows with leading time of two months to reduce the risk of water shortage. The monthly inflows are projected based on the seasonal climate forecasts from Central Weather Bureau (CWB), which a weather generation model is used to produce daily weather data for the hydrological component of the GWLF. To incorporate future monthly inflow projections into rule curves, this study designs a decision flow index which is a linear combination of current available storage and inflow projections with leading time of 2 months. By optimizing linear relationship coefficients of decision flow index, the shape of rule curves and the percent of water supply in each zone, the best rule curves to decrease water shortage risk and impacts can be developed. The Shimen Reservoir in the northern Taiwan is used as a case study to demonstrate the proposed method. Existing rule curves (M5 curves) of Shimen Reservoir are compared with two cases of new rule curves, including hindcast simulations and historic seasonal forecasts. The results show new rule curves can decrease the total water shortage ratio, and in addition, it can also allocate shortage amount to preceding months to avoid extreme shortage events. Even though some uncertainties in historic forecasts would result unnecessary discounts of water supply, it still performs better than M5 curves during droughts.

Big-Data Based Decision-Support Systems to Improve Clinicians' Cognition.

PubMed

Roosan, Don; Samore, Matthew; Jones, Makoto; Livnat, Yarden; Clutter, Justin

2016-01-01

Complex clinical decision-making could be facilitated by using population health data to inform clinicians. In two previous studies, we interviewed 16 infectious disease experts to understand complex clinical reasoning. For this study, we focused on answers from the experts on how clinical reasoning can be supported by population-based Big-Data. We found cognitive strategies such as trajectory tracking, perspective taking, and metacognition has the potential to improve clinicians' cognition to deal with complex problems. These cognitive strategies could be supported by population health data, and all have important implications for the design of Big-Data based decision-support tools that could be embedded in electronic health records. Our findings provide directions for task allocation and design of decision-support applications for health care industry development of Big data based decision-support systems.

Big-Data Based Decision-Support Systems to Improve Clinicians’ Cognition

PubMed Central

Roosan, Don; Samore, Matthew; Jones, Makoto; Livnat, Yarden; Clutter, Justin

2016-01-01

Complex clinical decision-making could be facilitated by using population health data to inform clinicians. In two previous studies, we interviewed 16 infectious disease experts to understand complex clinical reasoning. For this study, we focused on answers from the experts on how clinical reasoning can be supported by population-based Big-Data. We found cognitive strategies such as trajectory tracking, perspective taking, and metacognition has the potential to improve clinicians’ cognition to deal with complex problems. These cognitive strategies could be supported by population health data, and all have important implications for the design of Big-Data based decision-support tools that could be embedded in electronic health records. Our findings provide directions for task allocation and design of decision-support applications for health care industry development of Big data based decision-support systems. PMID:27990498

A two-stage stochastic rule-based model to determine pre-assembly buffer content

NASA Astrophysics Data System (ADS)

Gunay, Elif Elcin; Kula, Ufuk

2018-01-01

This study considers instant decision-making needs of the automobile manufactures for resequencing vehicles before final assembly (FA). We propose a rule-based two-stage stochastic model to determine the number of spare vehicles that should be kept in the pre-assembly buffer to restore the altered sequence due to paint defects and upstream department constraints. First stage of the model decides the spare vehicle quantities, where the second stage model recovers the scrambled sequence respect to pre-defined rules. The problem is solved by sample average approximation (SAA) algorithm. We conduct a numerical study to compare the solutions of heuristic model with optimal ones and provide following insights: (i) as the mismatch between paint entrance and scheduled sequence decreases, the rule-based heuristic model recovers the scrambled sequence as good as the optimal resequencing model, (ii) the rule-based model is more sensitive to the mismatch between the paint entrance and scheduled sequences for recovering the scrambled sequence, (iii) as the defect rate increases, the difference in recovery effectiveness between rule-based heuristic and optimal solutions increases, (iv) as buffer capacity increases, the recovery effectiveness of the optimization model outperforms heuristic model, (v) as expected the rule-based model holds more inventory than the optimization model.

Revealing Significant Relations between Chemical/Biological Features and Activity: Associative Classification Mining for Drug Discovery

ERIC Educational Resources Information Center

Yu, Pulan

2012-01-01

Classification, clustering and association mining are major tasks of data mining and have been widely used for knowledge discovery. Associative classification mining, the combination of both association rule mining and classification, has emerged as an indispensable way to support decision making and scientific research. In particular, it offers a…

Support Tool in the Diagnosis of Major Depressive Disorder

NASA Astrophysics Data System (ADS)

Nunes, Luciano Comin; Pinheiro, Plácido Rogério; Pequeno, Tarcísio Cavalcante; Pinheiro, Mirian Calíope Dantas

Major Depressive Disorder have been responsible for millions of professionals temporary removal, and even permanent, from diverse fields of activities around the world, generating damage to social, financial, productive systems and social security, and especially damage to the image of the individual and his family that these disorders produce in individuals who are patients, characteristics that make them stigmatized and discriminated into their society, making difficult their return to the production system. The lack of early diagnosis has provided reactive and late measures, only when the professional suffering psychological disorder is already showing signs of incapacity for working and social relationships. This article aims to assist in the decision making to establish early diagnosis of these types of psychological disorders. It presents a proposal for a hybrid model composed of expert system structured methodologies for decision support (Multi-Criteria Decision Analysis - MCDA) and representations of knowledge structured in logical rules of production and probabilities (Artificial Intelligence - AI).

Developing a Satellite Based Automatic System for Crop Monitoring: Kenya's Great Rift Valley, A Case Study

NASA Astrophysics Data System (ADS)

Lucciani, Roberto; Laneve, Giovanni; Jahjah, Munzer; Mito, Collins

2016-08-01

The crop growth stage represents essential information for agricultural areas management. In this study we investigate the feasibility of a tool based on remotely sensed satellite (Landsat 8) imagery, capable of automatically classify crop fields and how much resolution enhancement based on pan-sharpening techniques and phenological information extraction, useful to create decision rules that allow to identify semantic class to assign to an object, can effectively support the classification process. Moreover we investigate the opportunity to extract vegetation health status information from remotely sensed assessment of the equivalent water thickness (EWT). Our case study is the Kenya's Great Rift valley, in this area a ground truth campaign was conducted during August 2015 in order to collect crop fields GPS measurements, leaf area index (LAI) and chlorophyll samples.

Using an improved association rules mining optimization algorithm in web-based mobile-learning system

NASA Astrophysics Data System (ADS)

Huang, Yin; Chen, Jianhua; Xiong, Shaojun

2009-07-01

Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.

Combining Decision Rules from Classification Tree Models and Expert Assessment to Estimate Occupational Exposure to Diesel Exhaust for a Case-Control Study

PubMed Central

Friesen, Melissa C.; Wheeler, David C.; Vermeulen, Roel; Locke, Sarah J.; Zaebst, Dennis D.; Koutros, Stella; Pronk, Anjoeka; Colt, Joanne S.; Baris, Dalsu; Karagas, Margaret R.; Malats, Nuria; Schwenn, Molly; Johnson, Alison; Armenti, Karla R.; Rothman, Nathanial; Stewart, Patricia A.; Kogevinas, Manolis; Silverman, Debra T.

2016-01-01

Objectives: To efficiently and reproducibly assess occupational diesel exhaust exposure in a Spanish case-control study, we examined the utility of applying decision rules that had been extracted from expert estimates and questionnaire response patterns using classification tree (CT) models from a similar US study. Methods: First, previously extracted CT decision rules were used to obtain initial ordinal (0–3) estimates of the probability, intensity, and frequency of occupational exposure to diesel exhaust for the 10 182 jobs reported in a Spanish case-control study of bladder cancer. Second, two experts reviewed the CT estimates for 350 jobs randomly selected from strata based on each CT rule’s agreement with the expert ratings in the original study [agreement rate, from 0 (no agreement) to 1 (perfect agreement)]. Their agreement with each other and with the CT estimates was calculated using weighted kappa (κ w) and guided our choice of jobs for subsequent expert review. Third, an expert review comprised all jobs with lower confidence (low-to-moderate agreement rates or discordant assignments, n = 931) and a subset of jobs with a moderate to high CT probability rating and with moderately high agreement rates (n = 511). Logistic regression was used to examine the likelihood that an expert provided a different estimate than the CT estimate based on the CT rule agreement rates, the CT ordinal rating, and the availability of a module with diesel-related questions. Results: Agreement between estimates made by two experts and between estimates made by each of the experts and the CT estimates was very high for jobs with estimates that were determined by rules with high CT agreement rates (κ w: 0.81–0.90). For jobs with estimates based on rules with lower agreement rates, moderate agreement was observed between the two experts (κ w: 0.42–0.67) and poor-to-moderate agreement was observed between the experts and the CT estimates (κ w: 0.09–0.57). In total, the expert review of 1442 jobs changed 156 probability estimates, 128 intensity estimates, and 614 frequency estimates. The expert was more likely to provide a different estimate when the CT rule agreement rate was <0.8, when the CT ordinal ratings were low to moderate, or when a module with diesel questions was available. Conclusions: Our reliability assessment provided important insight into where to prioritize additional expert review; as a result, only 14% of the jobs underwent expert review, substantially reducing the exposure assessment burden. Overall, we found that we could efficiently, reproducibly, and reliably apply CT decision rules from one study to assess exposure in another study. PMID:26732820

Use of clinical prediction rules and D-dimer tests in the diagnostic management of pregnant patients with suspected acute pulmonary embolism.

PubMed

Van der Pol, L M; Mairuhu, A T A; Tromeur, C; Couturaud, F; Huisman, M V; Klok, F A

2017-03-01

Because pregnant women have an increased risk of venous thromboembolism (VTE) and at the same time normal pregnancy is associated with symptoms, mimicking those present in the setting of acute pulmonary embolism (PE), the latter diagnosis is frequently suspected in this patient category. Since imaging tests expose both mother and foetus to ionizing radiation, the ability to rule out PE based on non-radiological diagnostic tests is of paramount importance. However, clinical decision rules have only been scarcely evaluated in the pregnant population with suspected PE, while D-dimer levels lose diagnostic accuracy due to a physiological increase during normal pregnancy. Consequently, clinical guidelines provide contradicting and weak recommendations on this subject and the optimal diagnostic strategy remains highly debated. With this systematic review, we aimed to summarize current evidence on the safety and efficacy of clinical decision rules and biomarkers used in the diagnostic management of suspected acute PE in pregnant patients. Copyright © 2016 Elsevier Ltd. All rights reserved.

A probabilistic method to diagnose faults of air handling units

NASA Astrophysics Data System (ADS)

Dey, Debashis

Air handling unit (AHU) is one of the most extensively used equipment in large commercial buildings. This device is typically customized and lacks quality system integration which can result in hardwire failures and controller errors. Air handling unit Performance Assessment Rules (APAR) is a fault detection tool that uses a set of expert rules derived from mass and energy balances to detect faults in air handling units. APAR is computationally simple enough that it can be embedded in commercial building automation and control systems and relies only upon sensor data and control signals that are commonly available in these systems. Although APAR has many advantages over other methods, for example no training data required and easy to implement commercially, most of the time it is unable to provide the diagnosis of the faults. For instance, a fault on temperature sensor could be fixed bias, drifting bias, inappropriate location, complete failure. Also a fault in mixing box can be return and outdoor damper leak or stuck. In addition, when multiple rules are satisfied the list of faults increases. There is no proper way to have the correct diagnosis for rule based fault detection system. To overcome this limitation we proposed Bayesian Belief Network (BBN) as a diagnostic tool. BBN can be used to simulate diagnostic thinking of FDD experts through a probabilistic way. In this study we developed a new way to detect and diagnose faults in AHU through combining APAR rules and Bayesian Belief network. Bayesian Belief Network is used as a decision support tool for rule based expert system. BBN is highly capable to prioritize faults when multiple rules are satisfied simultaneously. Also it can get information from previous AHU operating conditions and maintenance records to provide proper diagnosis. The proposed model is validated with real time measured data of a campus building at University of Texas at San Antonio (UTSA).The results show that BBN is correctly able to prioritize faults which can be verified by manual investigation.

Data Clustering and Evolving Fuzzy Decision Tree for Data Base Classification Problems

NASA Astrophysics Data System (ADS)

Chang, Pei-Chann; Fan, Chin-Yuan; Wang, Yen-Wen

Data base classification suffers from two well known difficulties, i.e., the high dimensionality and non-stationary variations within the large historic data. This paper presents a hybrid classification model by integrating a case based reasoning technique, a Fuzzy Decision Tree (FDT), and Genetic Algorithms (GA) to construct a decision-making system for data classification in various data base applications. The model is major based on the idea that the historic data base can be transformed into a smaller case-base together with a group of fuzzy decision rules. As a result, the model can be more accurately respond to the current data under classifying from the inductions by these smaller cases based fuzzy decision trees. Hit rate is applied as a performance measure and the effectiveness of our proposed model is demonstrated by experimentally compared with other approaches on different data base classification applications. The average hit rate of our proposed model is the highest among others.

A Comparison of Computational Cognitive Models: Agent-Based Systems Versus Rule-Based Architectures

DTIC Science & Technology

2003-03-01

Java™ How To Program , Prentice Hall, 1999. Friedman-Hill, E., Jess, The Expert System Shell for the Java Platform, Sandia National Laboratories, 2001...transition from the descriptive NDM theory to a computational model raises several questions: Who is an experienced decision maker? How do you model the...progression from being a novice to an experienced decision maker? How does the model account for previous experiences? Are there situations where

Sport-related concussions in adolescent athletes: a critical public health problem for which prevention remains an elusive goal

PubMed Central

Fidrocki, Diana; Parachuri, Venu

2017-01-01

Sport-related concussions in young athletes are common, generally under reported and often unrecognized. Preventive strategies include education, modification of sport rules, use of equipment such as headgears, face masks and mouth guards, and neck muscle training. Evidence is limited to support effectiveness of these preventive measures with the exception of rule modification in some sports. In the United States, laws have been enacted that require medical evaluation and clearance prior to return to play; however, evidence thus far does not show that laws have been effective in reducing the incidence of concussions in sport. More research is needed in all areas of preventive measures. Sports participation is a complex personal decision on the part of the adolescent and his or her family. They should be provided with all information on inherent risks so that they can make an informed decision. PMID:28795000

Probabilistic reward- and punishment-based learning in opioid addiction: Experimental and computational data.

PubMed

Myers, Catherine E; Sheynin, Jony; Balsdon, Tarryn; Luzardo, Andre; Beck, Kevin D; Hogarth, Lee; Haber, Paul; Moustafa, Ahmed A

2016-01-01

Addiction is the continuation of a habit in spite of negative consequences. A vast literature gives evidence that this poor decision-making behavior in individuals addicted to drugs also generalizes to laboratory decision making tasks, suggesting that the impairment in decision-making is not limited to decisions about taking drugs. In the current experiment, opioid-addicted individuals and matched controls with no history of illicit drug use were administered a probabilistic classification task that embeds both reward-based and punishment-based learning trials, and a computational model of decision making was applied to understand the mechanisms describing individuals' performance on the task. Although behavioral results showed that opioid-addicted individuals performed as well as controls on both reward- and punishment-based learning, the modeling results suggested subtle differences in how decisions were made between the two groups. Specifically, the opioid-addicted group showed decreased tendency to repeat prior responses, meaning that they were more likely to "chase reward" when expectancies were violated, whereas controls were more likely to stick with a previously-successful response rule, despite occasional expectancy violations. This tendency to chase short-term reward, potentially at the expense of developing rules that maximize reward over the long term, may be a contributing factor to opioid addiction. Further work is indicated to better understand whether this tendency arises as a result of brain changes in the wake of continued opioid use/abuse, or might be a pre-existing factor that may contribute to risk for addiction. Copyright © 2015 Elsevier B.V. All rights reserved.

Some Memories Are Odder than Others: Judgments of Episodic Oddity Violate Known Decision Rules

ERIC Educational Resources Information Center

O'Connor, Akira R.; Guhl, Emily N.; Cox, Justin C.; Dobbins, Ian G.

2011-01-01

Current decision models of recognition memory are based almost entirely on one paradigm, single item old/new judgments accompanied by confidence ratings. This task results in receiver operating characteristics (ROCs) that are well fit by both signal-detection and dual-process models. Here we examine an entirely new recognition task, the judgment…

Diagnostic games: from adequate formalization of clinical experience to structure discovery.

PubMed

Shifrin, Michael A; Kasparova, Eva I

2008-01-01

A method of obtaining well-founded and reproducible results in clinical decision making is presented. It is based on "diagnostic games", a procedure of elicitation and formalization of experts' knowledge and experience. The use of this procedure allows formulating decision rules in the terms of an adequate language, that are both unambiguous and clinically clear.

16 CFR 3.51 - Initial decision.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 16 Commercial Practices 1 2010-01-01 2010-01-01 false Initial decision. 3.51 Section 3.51 Commercial Practices FEDERAL TRADE COMMISSION ORGANIZATION, PROCEDURES AND RULES OF PRACTICE RULES OF PRACTICE FOR ADJUDICATIVE PROCEEDINGS Decision § 3.51 Initial decision. (a) When filed and when effective. The Administrative Law Judge shall file an...

16 CFR 3.52 - Appeal from initial decision.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 16 Commercial Practices 1 2010-01-01 2010-01-01 false Appeal from initial decision. 3.52 Section 3.52 Commercial Practices FEDERAL TRADE COMMISSION ORGANIZATION, PROCEDURES AND RULES OF PRACTICE RULES OF PRACTICE FOR ADJUDICATIVE PROCEEDINGS Decision § 3.52 Appeal from initial decision. (a) Automatic review of cases in which the Commission sough...

49 CFR 1503.631 - Interlocutory appeals.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Rules of Practice in TSA Civil Penalty Actions § 1503.631 Interlocutory appeals. (a) General. Unless otherwise provided in this subpart, a party may not appeal a ruling or decision of the ALJ to the TSA decision maker until the initial decision has been entered on the record. A decision or order of the TSA...

Evaluation and Selection of Best Priority Sequencing Rule in Job Shop Scheduling using Hybrid MCDM Technique

NASA Astrophysics Data System (ADS)

Kiran Kumar, Kalla; Nagaraju, Dega; Gayathri, S.; Narayanan, S.

2017-05-01

Priority Sequencing Rules provide the guidance for the order in which the jobs are to be processed at a workstation. The application of different priority rules in job shop scheduling gives different order of scheduling. More experimentation needs to be conducted before a final choice is made to know the best priority sequencing rule. Hence, a comprehensive method of selecting the right choice is essential in managerial decision making perspective. This paper considers seven different priority sequencing rules in job shop scheduling. For evaluation and selection of the best priority sequencing rule, a set of eight criteria are considered. The aim of this work is to demonstrate the methodology of evaluating and selecting the best priority sequencing rule by using hybrid multi criteria decision making technique (MCDM), i.e., analytical hierarchy process (AHP) with technique for order preference by similarity to ideal solution (TOPSIS). The criteria weights are calculated by using AHP whereas the relative closeness values of all priority sequencing rules are computed based on TOPSIS with the help of data acquired from the shop floor of a manufacturing firm. Finally, from the findings of this work, the priority sequencing rules are ranked from most important to least important. The comprehensive methodology presented in this paper is very much essential for the management of a workstation to choose the best priority sequencing rule among the available alternatives for processing the jobs with maximum benefit.

Is there a need for a clinical decision rule in blunt wrist trauma?

PubMed

van den Brand, Crispijn L; van Leerdam, Roderick H; van Ufford, Jet H M E Quarles; Rhemrev, Steven J

2013-11-01

Blunt wrist trauma is a very common injury in emergency medicine. However, in contrast to other extremity trauma, there is no clinical decision rule for radiography in patients with blunt wrist trauma. The purpose of this study is to describe current practice and to assess the need and feasibility for a clinical decision rule for radiography in patients with blunt wrist trauma. All patients with blunt wrist trauma who presented to our Emergency Department (ED) during a 6-month period were included in this study. Basic demographics were analysed and the radiography ratio was determined. The radiography results were compared for different demographic groups. Current practice and the need and feasibility for a decision rule were evaluated using Stiell's checklist for clinical decision rules. A total of 1019 patients with 1032 blunt wrist injuries presented at our ED in a period of 6 months. In 91.4% of patients, radiographs were taken. In 41.6% of those radiographed, a fracture was visible on plain radiography. Fractures were most common in the paediatric and senior age groups. However, even in the lower-risk groups we observed a fracture incidence of about 20%. There is no need for a clinical decision rule for radiography in patients with blunt wrist trauma because the fracture ratio is high. Neither does it seem feasible to develop a highly sensitive and efficient decision rule. Therefore, the authors recommend radiography in all patients with blunt wrist trauma presenting to the ED. Copyright © 2013 Elsevier Ltd. All rights reserved.

Does science speak clearly and fairly in trade and food safety disputes? The search for an optimal response of WTO adjudication to problematic international standard-making.

PubMed

Ni, Kuei-Jung

2013-01-01

Most international health-related standards are voluntary per se. However, the incorporation of international standard-making into WTO agreements like the SPS Agreement has drastically changed the status and effectiveness of the standards. WTO members are urged to follow international standards, even when not required to comply fully with them. Indeed, such standards have attained great influence in the trade system. Yet evidence shows that the credibility of the allegedly scientific approach of these international standard-setting institutions, especially the Codex Alimentarius Commission (Codex) governing food safety standards, has been eroded and diluted by industrial and political influences. Its decision-making is no longer based on consensus, but voting. The adoption of new safety limits for the veterinary drug ractopamine in 2012, by a very close vote, is simply another instance of the problematic operations of the Codex. These dynamics have led skeptics to question the legitimacy of the standard setting body and to propose solutions to rectify the situation. Prior WTO rulings have yet to pay attention to the defect in the decision-making processes of the Codex. Nevertheless, the recent Appellate Body decision on Hormones II is indicative of a deferential approach to national measures that are distinct from Codex formulas. The ruling also rejects the reliance on those experts who authored the Codex standards to assess new measures of the European Community. This approach provides an opportunity to contemplate what the proper relationship between the WTO and Codex ought to be. Through a critical review of WTO rulings and academic proposals, this article aims to analyze how the WTO ought to define such interactions and respond to the politicized standard-making process in an optimal manner. This article argues that building a more systematic approach and normative basis for WTO judicial review of standard-setting decisions and the selection of technical experts would be instrumental to strengthening the mutual supports between the WTO and international standard-setting organizations, and may help avoid the introduction of a prejudice toward a justified science finding.

A detailed comparison of optimality and simplicity in perceptual decision-making

PubMed Central

Shen, Shan; Ma, Wei Ji

2017-01-01

Two prominent ideas in the study of decision-making have been that organisms behave near-optimally, and that they use simple heuristic rules. These principles might be operating in different types of tasks, but this possibility cannot be fully investigated without a direct, rigorous comparison within a single task. Such a comparison was lacking in most previous studies, because a) the optimal decision rule was simple; b) no simple suboptimal rules were considered; c) it was unclear what was optimal, or d) a simple rule could closely approximate the optimal rule. Here, we used a perceptual decision-making task in which the optimal decision rule is well-defined and complex, and makes qualitatively distinct predictions from many simple suboptimal rules. We find that all simple rules tested fail to describe human behavior, that the optimal rule accounts well for the data, and that several complex suboptimal rules are indistinguishable from the optimal one. Moreover, we found evidence that the optimal model is close to the true model: first, the better the trial-to-trial predictions of a suboptimal model agree with those of the optimal model, the better that suboptimal model fits; second, our estimate of the Kullback-Leibler divergence between the optimal model and the true model is not significantly different from zero. When observers receive no feedback, the optimal model still describes behavior best, suggesting that sensory uncertainty is implicitly represented and taken into account. Beyond the task and models studied here, our results have implications for best practices of model comparison. PMID:27177259

Decision technology.

PubMed

Edwards, W; Fasolo, B

2001-01-01

This review is about decision technology-the rules and tools that help us make wiser decisions. First, we review the three rules that are at the heart of most traditional decision technology-multi-attribute utility, Bayes' theorem, and subjective expected utility maximization. Since the inception of decision research, these rules have prescribed how we should infer values and probabilities and how we should combine them to make better decisions. We suggest how to make best use of all three rules in a comprehensive 19-step model. The remainder of the review explores recently developed tools of decision technology. It examines the characteristics and problems of decision-facilitating sites on the World Wide Web. Such sites now provide anyone who can use a personal computer with access to very sophisticated decision-aiding tools structured mainly to facilitate consumer decision making. It seems likely that the Web will be the mode by means of which decision tools will be distributed to lay users. But methods for doing such apparently simple things as winnowing 3000 options down to a more reasonable number, like 10, contain traps for unwary decision technologists. The review briefly examines Bayes nets and influence diagrams-judgment and decision-making tools that are available as computer programs. It very briefly summarizes the state of the art of eliciting probabilities from experts. It concludes that decision tools will be as important in the 21st century as spreadsheets were in the 20th.

Effective application of improved profit-mining algorithm for the interday trading model.

PubMed

Hsieh, Yu-Lung; Yang, Don-Lin; Wu, Jungpin

2014-01-01

Many real world applications of association rule mining from large databases help users make better decisions. However, they do not work well in financial markets at this time. In addition to a high profit, an investor also looks for a low risk trading with a better rate of winning. The traditional approach of using minimum confidence and support thresholds needs to be changed. Based on an interday model of trading, we proposed effective profit-mining algorithms which provide investors with profit rules including information about profit, risk, and winning rate. Since profit-mining in the financial market is still in its infant stage, it is important to detail the inner working of mining algorithms and illustrate the best way to apply them. In this paper we go into details of our improved profit-mining algorithm and showcase effective applications with experiments using real world trading data. The results show that our approach is practical and effective with good performance for various datasets.

Effective Application of Improved Profit-Mining Algorithm for the Interday Trading Model

PubMed Central

Wu, Jungpin

2014-01-01

Many real world applications of association rule mining from large databases help users make better decisions. However, they do not work well in financial markets at this time. In addition to a high profit, an investor also looks for a low risk trading with a better rate of winning. The traditional approach of using minimum confidence and support thresholds needs to be changed. Based on an interday model of trading, we proposed effective profit-mining algorithms which provide investors with profit rules including information about profit, risk, and winning rate. Since profit-mining in the financial market is still in its infant stage, it is important to detail the inner working of mining algorithms and illustrate the best way to apply them. In this paper we go into details of our improved profit-mining algorithm and showcase effective applications with experiments using real world trading data. The results show that our approach is practical and effective with good performance for various datasets. PMID:24688442

Introduction to Decision Support Systems for Risk Based Management of Contaminated Sites

EPA Science Inventory

A book on Decision Support Systems for Risk-based Management of contaminated sites is appealing for two reasons. First, it addresses the problem of contaminated sites, which has worldwide importance. Second, it presents Decision Support Systems (DSSs), which are powerful comput...

Repeating the Past: Prevention Focus Motivates Repetition, Even For Unethical Decisions

PubMed Central

Zhang, Shu; Cornwell, James F.M.; Higgins, E. Tory

2013-01-01

Prevention-focused individuals are motivated to maintain the status quo. Given this, we predicted that individuals with a strong prevention focus, either as a chronic predisposition or situationally induced, would treat their initial decision of how to behave on a first task as the status quo, and thus be motivated to repeat that decision on a subsequent task—even for decisions that are ethically questionable. Five studies supported this prediction in multiple ethical domains: whether or not to overstate performance (Studies 1, 2a, 2b), to disclose disadvantageous facts (Study 3), and to pledge a donation (Study 4). The prevention-repetition effect was observed when initial and subsequent decisions were in the same domain (Studies 1-3) and in different domains (Study 4). Alternative accounts such as justification for the initial decision and preference for consistency were ruled out (Study 2b). PMID:24277774

Mitigating circumstances in death penalty decisions: using evidence-based research to inform social work practice in capital trials.

PubMed

Schroeder, Julie; Guin, Cecile C; Pogue, Rene; Bordelon, Danna

2006-10-01

Providing an effective defense for individuals charged with capital crimes requires a diligent, thorough investigation by a mitigation specialist. However, research suggests that mitigation often plays a small role in the decision for life. Jurors often make sentencing decisions prematurely, basing those decisions on their personal reactions to the defendant (for example, fear, anger), their confusion about the rules of law, and their lack of understanding regarding their role and responsibilities. This article proposes an evidence-based conceptual model of the complicating problems surrounding mitigation practice and a focused discussion about how traditional social work mitigation strategies might be evolved to a set of best practices that more effectively ensure jurors' careful consideration of mitigation evidence.

Decision Support Systems and the Conflict Model of Decision Making: A Stimulus for New Computer-Assisted Careers Guidance Systems.

ERIC Educational Resources Information Center

Ballantine, R. Malcolm

Decision Support Systems (DSSs) are computer-based decision aids to use when making decisions which are partially amenable to rational decision-making procedures but contain elements where intuitive judgment is an essential component. In such situations, DSSs are used to improve the quality of decision-making. The DSS approach is based on Simon's…

A decision tree-based on-line preventive control strategy for power system transient instability prevention

NASA Astrophysics Data System (ADS)

Xu, Yan; Dong, Zhao Yang; Zhang, Rui; Wong, Kit Po

2014-02-01

Maintaining transient stability is a basic requirement for secure power system operations. Preventive control deals with modifying the system operating point to withstand probable contingencies. In this article, a decision tree (DT)-based on-line preventive control strategy is proposed for transient instability prevention of power systems. Given a stability database, a distance-based feature estimation algorithm is first applied to identify the critical generators, which are then used as features to develop a DT. By interpreting the splitting rules of DT, preventive control is realised by formulating the rules in a standard optimal power flow model and solving it. The proposed method is transparent in control mechanism, on-line computation compatible and convenient to deal with multi-contingency. The effectiveness and efficiency of the method has been verified on New England 10-machine 39-bus test system.

Forest fire autonomous decision system based on fuzzy logic

NASA Astrophysics Data System (ADS)

Lei, Z.; Lu, Jianhua

2010-11-01

The proposed system integrates GPS / pseudolite / IMU and thermal camera in order to autonomously process the graphs by identification, extraction, tracking of forest fire or hot spots. The airborne detection platform, the graph-based algorithms and the signal processing frame are analyzed detailed; especially the rules of the decision function are expressed in terms of fuzzy logic, which is an appropriate method to express imprecise knowledge. The membership function and weights of the rules are fixed through a supervised learning process. The perception system in this paper is based on a network of sensorial stations and central stations. The sensorial stations collect data including infrared and visual images and meteorological information. The central stations exchange data to perform distributed analysis. The experiment results show that working procedure of detection system is reasonable and can accurately output the detection alarm and the computation of infrared oscillations.

Distributed collaborative decision support environments for predictive awareness

NASA Astrophysics Data System (ADS)

McQuay, William K.; Stilman, Boris; Yakhnis, Vlad

2005-05-01

The past decade has produced significant changes in the conduct of military operations: asymmetric warfare, the reliance on dynamic coalitions, stringent rules of engagement, increased concern about collateral damage, and the need for sustained air operations. Mission commanders need to assimilate a tremendous amount of information, rapidly assess the enemy"s course of action (eCOA) or possible actions and promulgate their own course of action (COA) - a need for predictive awareness. Decision support tools in a distributed collaborative environment offer the capability of decomposing complex multitask processes and distributing them over a dynamic set of execution assets that include modeling, simulations, and analysis tools. Revolutionary new approaches to strategy generation and assessment such as Linguistic Geometry (LG) permit the rapid development of COA vs. enemy COA (eCOA). LG tools automatically generate and permit the operators to take advantage of winning strategies and tactics for mission planning and execution in near real-time. LG is predictive and employs deep "look-ahead" from the current state and provides a realistic, reactive model of adversary reasoning and behavior. Collaborative environments provide the framework and integrate models, simulations, and domain specific decision support tools for the sharing and exchanging of data, information, knowledge, and actions. This paper describes ongoing research efforts in applying distributed collaborative environments to decision support for predictive mission awareness.

Robot decisions: on the importance of virtuous judgment in clinical decision making.

PubMed

Gelhaus, Petra

2011-10-01

The aim of this article is to argue for the necessity of emotional professional virtues in the understanding of good clinical practice. This understanding is required for a proper balance of capacities in medical education and further education of physicians. For this reason an ideal physician, incarnating the required virtues, skills and knowledge is compared with a non-emotional robot that is bound to moral rules. This fictive confrontation is meant to clarify why certain demands on the personality of the physician are justified, in addition to a rule- and principle-based moral orientation and biomedical knowledge and skills. Philosophical analysis of thought experiments inspired by science fiction literature by Isaac Asimov. Although prima facie a rule-oriented robot seems more reliable and trustworthy, the complexity of clinical judgment is not met by an encompassing and never contradictory set of rules from which one could logically derive decisions. There are different ways how the robot could still work, but at the cost of the predictability of its behaviour and its moral orientation. In comparison, a virtuous human doctor who is also bound to these rules, although less strictly, will more reliably keep at moral objectives, be understandable, be more flexible in case the rules come to their limits, and will be more predictable in these critical situations. Apart from these advantages of the virtuous human doctor referring to her own person, the most problematic deficit of the robot is its lacking deeper understanding of the inner mental events of patients which makes good contact, good communication and good influence impossible. Although an infallibly rule-oriented robot seems more reliable at first view, in situations that require complex decisions like clinical practice the agency of a moral human person is more trustworthy. Furthermore, the understanding of the patient's emotions must remain insufficient for a non-emotional, non-human being. Because these are crucial preconditions for good clinical practice, enough attention should be given to develop these virtues and emotional skills, in addition to the usual attention on knowledge, technical skills and the obedience to moral rules and principles. © 2011 Blackwell Publishing Ltd.

Systematic Review of Medical Informatics-Supported Medication Decision Making.

PubMed

Melton, Brittany L

2017-01-01

This systematic review sought to assess the applications and implications of current medical informatics-based decision support systems related to medication prescribing and use. Studies published between January 2006 and July 2016 which were indexed in PubMed and written in English were reviewed, and 39 studies were ultimately included. Most of the studies looked at computerized provider order entry or clinical decision support systems. Most studies examined decision support systems as a means of reducing errors or risk, particularly associated with medication prescribing, whereas a few studies evaluated the impact medical informatics-based decision support systems have on workflow or operations efficiency. Most studies identified benefits associated with decision support systems, but some indicate there is room for improvement.