Using Best Practices to Extract, Organize, and Reuse Embedded Decision Support Content Knowledge Rules from Mature Clinical Systems

PubMed Central

DesAutels, Spencer J.; Fox, Zachary E.; Giuse, Dario A.; Williams, Annette M.; Kou, Qing-hua; Weitkamp, Asli; Neal R, Patel; Bettinsoli Giuse, Nunzia

2016-01-01

Clinical decision support (CDS) knowledge, embedded over time in mature medical systems, presents an interesting and complex opportunity for information organization, maintenance, and reuse. To have a holistic view of all decision support requires an in-depth understanding of each clinical system as well as expert knowledge of the latest evidence. This approach to clinical decision support presents an opportunity to unify and externalize the knowledge within rules-based decision support. Driven by an institutional need to prioritize decision support content for migration to new clinical systems, the Center for Knowledge Management and Health Information Technology teams applied their unique expertise to extract content from individual systems, organize it through a single extensible schema, and present it for discovery and reuse through a newly created Clinical Support Knowledge Acquisition and Archival Tool (CS-KAAT). CS-KAAT can build and maintain the underlying knowledge infrastructure needed by clinical systems. PMID:28269846

Pharmacogenomic knowledge representation, reasoning and genome-based clinical decision support based on OWL 2 DL ontologies.

PubMed

Samwald, Matthias; Miñarro Giménez, Jose Antonio; Boyce, Richard D; Freimuth, Robert R; Adlassnig, Klaus-Peter; Dumontier, Michel

2015-02-22

Every year, hundreds of thousands of patients experience treatment failure or adverse drug reactions (ADRs), many of which could be prevented by pharmacogenomic testing. However, the primary knowledge needed for clinical pharmacogenomics is currently dispersed over disparate data structures and captured in unstructured or semi-structured formalizations. This is a source of potential ambiguity and complexity, making it difficult to create reliable information technology systems for enabling clinical pharmacogenomics. We developed Web Ontology Language (OWL) ontologies and automated reasoning methodologies to meet the following goals: 1) provide a simple and concise formalism for representing pharmacogenomic knowledge, 2) finde errors and insufficient definitions in pharmacogenomic knowledge bases, 3) automatically assign alleles and phenotypes to patients, 4) match patients to clinically appropriate pharmacogenomic guidelines and clinical decision support messages and 5) facilitate the detection of inconsistencies and overlaps between pharmacogenomic treatment guidelines from different sources. We evaluated different reasoning systems and test our approach with a large collection of publicly available genetic profiles. Our methodology proved to be a novel and useful choice for representing, analyzing and using pharmacogenomic data. The Genomic Clinical Decision Support (Genomic CDS) ontology represents 336 SNPs with 707 variants; 665 haplotypes related to 43 genes; 22 rules related to drug-response phenotypes; and 308 clinical decision support rules. OWL reasoning identified CDS rules with overlapping target populations but differing treatment recommendations. Only a modest number of clinical decision support rules were triggered for a collection of 943 public genetic profiles. We found significant performance differences across available OWL reasoners. The ontology-based framework we developed can be used to represent, organize and reason over the growing wealth of

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

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

PubMed

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

2015-10-01

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

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.

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

Efficacy of an evidence-based clinical decision support in primary care practices: a randomized clinical trial.

PubMed

McGinn, Thomas G; McCullagh, Lauren; Kannry, Joseph; Knaus, Megan; Sofianou, Anastasia; Wisnivesky, Juan P; Mann, Devin M

2013-09-23

There is consensus that incorporating clinical decision support into electronic health records will improve quality of care, contain costs, and reduce overtreatment, but this potential has yet to be demonstrated in clinical trials. To assess the influence of a customized evidence-based clinical decision support tool on the management of respiratory tract infections and on the effectiveness of integrating evidence at the point of care. In a randomized clinical trial, we implemented 2 well-validated integrated clinical prediction rules, namely, the Walsh rule for streptococcal pharyngitis and the Heckerling rule for pneumonia. INTERVENTIONS AND MAIN OUTCOMES AND MEASURES: The intervention group had access to the integrated clinical prediction rule tool and chose whether to complete risk score calculators, order medications, and generate progress notes to assist with complex decision making at the point of care. The intervention group completed the integrated clinical prediction rule tool in 57.5% of visits. Providers in the intervention group were significantly less likely to order antibiotics than the control group (age-adjusted relative risk, 0.74; 95% CI, 0.60-0.92). The absolute risk of the intervention was 9.2%, and the number needed to treat was 10.8. The intervention group was significantly less likely to order rapid streptococcal tests compared with the control group (relative risk, 0.75; 95% CI, 0.58-0.97; P= .03). The integrated clinical prediction rule process for integrating complex evidence-based clinical decision report tools is of relevant importance for national initiatives, such as Meaningful Use. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT01386047.

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,...

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.

Lung Cancer Assistant: a hybrid clinical decision support application for lung cancer care.

PubMed

Sesen, M Berkan; Peake, Michael D; Banares-Alcantara, Rene; Tse, Donald; Kadir, Timor; Stanley, Roz; Gleeson, Fergus; Brady, Michael

2014-09-06

Multidisciplinary team (MDT) meetings are becoming the model of care for cancer patients worldwide. While MDTs have improved the quality of cancer care, the meetings impose substantial time pressure on the members, who generally attend several such MDTs. We describe Lung Cancer Assistant (LCA), a clinical decision support (CDS) prototype designed to assist the experts in the treatment selection decisions in the lung cancer MDTs. A novel feature of LCA is its ability to provide rule-based and probabilistic decision support within a single platform. The guideline-based CDS is based on clinical guideline rules, while the probabilistic CDS is based on a Bayesian network trained on the English Lung Cancer Audit Database (LUCADA). We assess rule-based and probabilistic recommendations based on their concordances with the treatments recorded in LUCADA. Our results reveal that the guideline rule-based recommendations perform well in simulating the recorded treatments with exact and partial concordance rates of 0.57 and 0.79, respectively. On the other hand, the exact and partial concordance rates achieved with probabilistic results are relatively poorer with 0.27 and 0.76. However, probabilistic decision support fulfils a complementary role in providing accurate survival estimations. Compared to recorded treatments, both CDS approaches promote higher resection rates and multimodality treatments.

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

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

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.

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).

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.

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.

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.

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

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.

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.

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

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.

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 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.

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

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.

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.

Web-based health services and clinical decision support.

PubMed

Jegelevicius, Darius; Marozas, Vaidotas; Lukosevicius, Arunas; Patasius, Martynas

2004-01-01

The purpose of this study was the development of a Web-based e-health service for comprehensive assistance and clinical decision support. The service structure consists of a Web server, a PHP-based Web interface linked to a clinical SQL database, Java applets for interactive manipulation and visualization of signals and a Matlab server linked with signal and data processing algorithms implemented by Matlab programs. The service ensures diagnostic signal- and image analysis-sbased clinical decision support. By using the discussed methodology, a pilot service for pathology specialists for automatic calculation of the proliferation index has been developed. Physicians use a simple Web interface for uploading the pictures under investigation to the server; subsequently a Java applet interface is used for outlining the region of interest and, after processing on the server, the requested proliferation index value is calculated. There is also an "expert corner", where experts can submit their index estimates and comments on particular images, which is especially important for system developers. These expert evaluations are used for optimization and verification of automatic analysis algorithms. Decision support trials have been conducted for ECG and ophthalmology ultrasonic investigations of intraocular tumor differentiation. Data mining algorithms have been applied and decision support trees constructed. These services are under implementation by a Web-based system too. The study has shown that the Web-based structure ensures more effective, flexible and accessible services compared with standalone programs and is very convenient for biomedical engineers and physicians, especially in the development phase.

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 Eb

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

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.

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.

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

A knowledge-based decision support system for payload scheduling

NASA Technical Reports Server (NTRS)

Floyd, Stephen; Ford, Donnie

1988-01-01

The role that artificial intelligence/expert systems technologies play in the development and implementation of effective decision support systems is illustrated. A recently developed prototype system for supporting the scheduling of subsystems and payloads/experiments for NASA's Space Station program is presented and serves to highlight various concepts. The potential integration of knowledge based systems and decision support systems which has been proposed in several recent articles and presentations is illustrated.

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

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.

A Web-Based Tool to Support Data-Based Early Intervention Decision Making

ERIC Educational Resources Information Center

Buzhardt, Jay; Greenwood, Charles; Walker, Dale; Carta, Judith; Terry, Barbara; Garrett, Matthew

2010-01-01

Progress monitoring and data-based intervention decision making have become key components of providing evidence-based early childhood special education services. Unfortunately, there is a lack of tools to support early childhood service providers' decision-making efforts. The authors describe a Web-based system that guides service providers…

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

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.

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.

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.

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.

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

Knowledge-Based Information Management in Decision Support for Ecosystem Management

Treesearch

Keith Reynolds; Micahel Saunders; Richard Olson; Daniel Schmoldt; Michael Foster; Donald Latham; Bruce Miller; John Steffenson; Lawrence Bednar; Patrick Cunningham

1995-01-01

The Pacific Northwest Research Station (USDA Forest Service) is developing a knowledge-based information management system to provide decision support for watershed analysis in the Pacific Northwest region of the U.S. The decision support system includes: (1) a GIS interface that allows users to graphically navigate to specific provinces and watersheds and display a...

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.

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.

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.

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

Supporting decision-making processes for evidence-based mental health promotion.

PubMed

Jané-Llopis, Eva; Katschnig, Heinz; McDaid, David; Wahlbeck, Kristian

2011-12-01

The use of evidence is critical in guiding decision-making, but evidence from effect studies will be only one of a number of factors that will need to be taken into account in the decision-making processes. Equally important for policymakers will be the use of different types of evidence including implementation essentials and other decision-making principles such as social justice, political, ethical, equity issues, reflecting public attitudes and the level of resources available, rather than be based on health outcomes alone. This paper, aimed to support decision-makers, highlights the importance of commissioning high-quality evaluations, the key aspects to assess levels of evidence, the importance of supporting evidence-based implementation and what to look out for before, during and after implementation of mental health promotion and mental disorder prevention programmes.

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.

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

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

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.…

Modular Architecture for Integrated Model-Based Decision Support.

PubMed

Gaebel, Jan; Schreiber, Erik; Oeser, Alexander; Oeltze-Jafra, Steffen

2018-01-01

Model-based decision support systems promise to be a valuable addition to oncological treatments and the implementation of personalized therapies. For the integration and sharing of decision models, the involved systems must be able to communicate with each other. In this paper, we propose a modularized architecture of dedicated systems for the integration of probabilistic decision models into existing hospital environments. These systems interconnect via web services and provide model sharing and processing capabilities for clinical information systems. Along the lines of IHE integration profiles from other disciplines and the meaningful reuse of routinely recorded patient data, our approach aims for the seamless integration of decision models into hospital infrastructure and the physicians' daily work.

An Integrated Web-based Decision Support System in Disaster Risk Management

NASA Astrophysics Data System (ADS)

Aye, Z. C.; Jaboyedoff, M.; Derron, M. H.

2012-04-01

Nowadays, web based decision support systems (DSS) play an essential role in disaster risk management because of their supporting abilities which help the decision makers to improve their performances and make better decisions without needing to solve complex problems while reducing human resources and time. Since the decision making process is one of the main factors which highly influence the damages and losses of society, it is extremely important to make right decisions at right time by combining available risk information with advanced web technology of Geographic Information System (GIS) and Decision Support System (DSS). This paper presents an integrated web-based decision support system (DSS) of how to use risk information in risk management efficiently and effectively while highlighting the importance of a decision support system in the field of risk reduction. Beyond the conventional systems, it provides the users to define their own strategies starting from risk identification to the risk reduction, which leads to an integrated approach in risk management. In addition, it also considers the complexity of changing environment from different perspectives and sectors with diverse stakeholders' involvement in the development process. The aim of this platform is to contribute a part towards the natural hazards and geosciences society by developing an open-source web platform where the users can analyze risk profiles and make decisions by performing cost benefit analysis, Environmental Impact Assessment (EIA) and Strategic Environmental Assessment (SEA) with the support of others tools and resources provided. There are different access rights to the system depending on the user profiles and their responsibilities. The system is still under development and the current version provides maps viewing, basic GIS functionality, assessment of important infrastructures (e.g. bridge, hospital, etc.) affected by landslides and visualization of the impact

Launching a virtual decision lab: development and field-testing of a web-based patient decision support research platform.

PubMed

Hoffman, Aubri S; Llewellyn-Thomas, Hilary A; Tosteson, Anna N A; O'Connor, Annette M; Volk, Robert J; Tomek, Ivan M; Andrews, Steven B; Bartels, Stephen J

2014-12-12

Over 100 trials show that patient decision aids effectively improve patients' information comprehension and values-based decision making. However, gaps remain in our understanding of several fundamental and applied questions, particularly related to the design of interactive, personalized decision aids. This paper describes an interdisciplinary development process for, and early field testing of, a web-based patient decision support research platform, or virtual decision lab, to address these questions. An interdisciplinary stakeholder panel designed the web-based research platform with three components: a) an introduction to shared decision making, b) a web-based patient decision aid, and c) interactive data collection items. Iterative focus groups provided feedback on paper drafts and online prototypes. A field test assessed a) feasibility for using the research platform, in terms of recruitment, usage, and acceptability; and b) feasibility of using the web-based decision aid component, compared to performance of a videobooklet decision aid in clinical care. This interdisciplinary, theory-based, patient-centered design approach produced a prototype for field-testing in six months. Participants (n = 126) reported that: the decision aid component was easy to use (98%), information was clear (90%), the length was appropriate (100%), it was appropriately detailed (90%), and it held their interest (97%). They spent a mean of 36 minutes using the decision aid and 100% preferred using their home/library computer. Participants scored a mean of 75% correct on the Decision Quality, Knowledge Subscale, and 74 out of 100 on the Preparation for Decision Making Scale. Completing the web-based decision aid reduced mean Decisional Conflict scores from 31.1 to 19.5 (p < 0.01). Combining decision science and health informatics approaches facilitated rapid development of a web-based patient decision support research platform that was feasible for use in research studies in

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).

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.

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...

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

An Intelligent Decision Support System for Workforce Forecast

DTIC Science & Technology

2011-01-01

ARIMA ) model to forecast the demand for construction skills in Hong Kong. This model was based...Decision Trees ARIMA Rule Based Forecasting Segmentation Forecasting Regression Analysis Simulation Modeling Input-Output Models LP and NLP Markovian...data • When results are needed as a set of easily interpretable rules 4.1.4 ARIMA Auto-regressive, integrated, moving-average ( ARIMA ) models

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.

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.

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.

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.

Adaptation of a Knowledge-Based Decision-Support System in the Tactical Environment.

DTIC Science & Technology

1981-12-01

002-04-6411S1CURITY CL All PICATION OF 1,416 PAGE (00HIR Onto ea0aOW .L10 *GU9WVC 4bGSI.CAYON S. Voss 10466lVka t... OftesoE ’ making decisons . The...noe..aaw Ad tdlalttt’ IV 680011 MMib) Artificial Intelligence; Decision-Support Systems; Tactical Decision- making ; Knowledge-based Decision-support...tactical information to assist tactical commanders in making decisions. The system, TAC*, for "Tactical Adaptable Consultant," incorporates a database

Concurrent approach for evolving compact decision rule sets

NASA Astrophysics Data System (ADS)

Marmelstein, Robert E.; Hammack, Lonnie P.; Lamont, Gary B.

1999-02-01

The induction of decision rules from data is important to many disciplines, including artificial intelligence and pattern recognition. To improve the state of the art in this area, we introduced the genetic rule and classifier construction environment (GRaCCE). It was previously shown that GRaCCE consistently evolved decision rule sets from data, which were significantly more compact than those produced by other methods (such as decision tree algorithms). The primary disadvantage of GRaCCe, however, is its relatively poor run-time execution performance. In this paper, a concurrent version of the GRaCCE architecture is introduced, which improves the efficiency of the original algorithm. A prototype of the algorithm is tested on an in- house parallel processor configuration and the results are discussed.

Design and realization of tourism spatial decision support system based on GIS

NASA Astrophysics Data System (ADS)

Ma, Zhangbao; Qi, Qingwen; Xu, Li

2008-10-01

In this paper, the existing problems of current tourism management information system are analyzed. GIS, tourism as well as spatial decision support system are introduced, and the application of geographic information system technology and spatial decision support system to tourism management and the establishment of tourism spatial decision support system based on GIS are proposed. System total structure, system hardware and software environment, database design and structure module design of this system are introduced. Finally, realization methods of this systemic core functions are elaborated.

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.

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.

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.

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.

Decision blocks: A tool for automating decision making in CLIPS

NASA Technical Reports Server (NTRS)

Eick, Christoph F.; Mehta, Nikhil N.

1991-01-01

The human capability of making complex decision is one of the most fascinating facets of human intelligence, especially if vague, judgemental, default or uncertain knowledge is involved. Unfortunately, most existing rule based forward chaining languages are not very suitable to simulate this aspect of human intelligence, because of their lack of support for approximate reasoning techniques needed for this task, and due to the lack of specific constructs to facilitate the coding of frequently reoccurring decision block to provide better support for the design and implementation of rule based decision support systems. A language called BIRBAL, which is defined on the top of CLIPS, for the specification of decision blocks, is introduced. Empirical experiments involving the comparison of the length of CLIPS program with the corresponding BIRBAL program for three different applications are surveyed. The results of these experiments suggest that for decision making intensive applications, a CLIPS program tends to be about three times longer than the corresponding BIRBAL program.

A diagnosis-based clinical decision rule for spinal pain part 2: review of the literature

PubMed Central

Murphy, Donald R; Hurwitz, Eric L; Nelson, Craig F

2008-01-01

Background Spinal pain is a common and often disabling problem. The research on various treatments for spinal pain has, for the most part, suggested that while several interventions have demonstrated mild to moderate short-term benefit, no single treatment has a major impact on either pain or disability. There is great need for more accurate diagnosis in patients with spinal pain. In a previous paper, the theoretical model of a diagnosis-based clinical decision rule was presented. The approach is designed to provide the clinician with a strategy for arriving at a specific working diagnosis from which treatment decisions can be made. It is based on three questions of diagnosis. In the current paper, the literature on the reliability and validity of the assessment procedures that are included in the diagnosis-based clinical decision rule is presented. Methods The databases of Medline, Cinahl, Embase and MANTIS were searched for studies that evaluated the reliability and validity of clinic-based diagnostic procedures for patients with spinal pain that have relevance for questions 2 (which investigates characteristics of the pain source) and 3 (which investigates perpetuating factors of the pain experience). In addition, the reference list of identified papers and authors' libraries were searched. Results A total of 1769 articles were retrieved, of which 138 were deemed relevant. Fifty-one studies related to reliability and 76 related to validity. One study evaluated both reliability and validity. Conclusion Regarding some aspects of the DBCDR, there are a number of studies that allow the clinician to have a reasonable degree of confidence in his or her findings. This is particularly true for centralization signs, neurodynamic signs and psychological perpetuating factors. There are other aspects of the DBCDR in which a lesser degree of confidence is warranted, and in which further research is needed. PMID:18694490

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

A prototype knowledge-based decision support system for industrial waste management. Part 1: The decision support system

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

Boyle, C.A.; Baetz, B.W.

1998-12-31

Although there are a number of expert systems available which are designed to assist in resolving environmental problems, there is still a need for a system which would assist managers in determining waste management options for all types of wastes from one or more industrial plants, giving priority to sustainable use of resources, reuse and recycling. A prototype model was developed to determine the potentials for reuse and recycling of waste materials, to select the treatments needed to recycle waste materials or for treatment before disposal, and to determine potentials for co-treatment of wastes. A knowledge-based decision support system wasmore » then designed using this model. This paper describes the prototype model, the developed knowledge-based decision support system, the input and storage of data within the system and the inference engine developed for the system to determine the treatment options for the wastes. Options for sorting and selecting treatment trains are described, along with a discussion of the limitations of the approach and future developments needed for the system.« less

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.

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.

Decision rules for unbiased inventory estimates

NASA Technical Reports Server (NTRS)

Argentiero, P. D.; Koch, D.

1979-01-01

An efficient and accurate procedure for estimating inventories from remote sensing scenes is presented. In place of the conventional and expensive full dimensional Bayes decision rule, a one-dimensional feature extraction and classification technique was employed. It is shown that this efficient decision rule can be used to develop unbiased inventory estimates and that for large sample sizes typical of satellite derived remote sensing scenes, resulting accuracies are comparable or superior to more expensive alternative procedures. Mathematical details of the procedure are provided in the body of the report and in the appendix. Results of a numerical simulation of the technique using statistics obtained from an observed LANDSAT scene are included. The simulation demonstrates the effectiveness of the technique in computing accurate inventory estimates.

Knowledge bases, clinical decision support systems, and rapid learning in oncology.

PubMed

Yu, Peter Paul

2015-03-01

One of the most important benefits of health information technology is to assist the cognitive process of the human mind in the face of vast amounts of health data, limited time for decision making, and the complexity of the patient with cancer. Clinical decision support tools are frequently cited as a technologic solution to this problem, but to date useful clinical decision support systems (CDSS) have been limited in utility and implementation. This article describes three unique sources of health data that underlie fundamentally different types of knowledge bases which feed into CDSS. CDSS themselves comprise a variety of models which are discussed. The relationship of knowledge bases and CDSS to rapid learning health systems design is critical as CDSS are essential drivers of rapid learning in clinical care. Copyright © 2015 by American Society of Clinical Oncology.

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.

EMDS users guide (version 2.0): knowledge-based decision support for ecological assessment.

Treesearch

Keith M. Reynolds

1999-01-01

The USDA Forest Service Pacific Northwest Research Station in Corvallis, Oregon, has developed the ecosystem management decision support (EMDS) system. The system integrates the logical formalism of knowledge-based reasoning into a geographic information system (GIS) environment to provide decision support for ecological landscape assessment and evaluation. The...

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.

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.

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.

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.

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.

CorRECTreatment: A Web-based Decision Support Tool for Rectal Cancer Treatment that Uses the Analytic Hierarchy Process and Decision Tree

PubMed Central

Karakülah, G.; Dicle, O.; Sökmen, S.; Çelikoğlu, C.C.

2015-01-01

Summary Background The selection of appropriate rectal cancer treatment is a complex multi-criteria decision making process, in which clinical decision support systems might be used to assist and enrich physicians’ decision making. Objective The objective of the study was to develop a web-based clinical decision support tool for physicians in the selection of potentially beneficial treatment options for patients with rectal cancer. Methods The updated decision model contained 8 and 10 criteria in the first and second steps respectively. The decision support model, developed in our previous study by combining the Analytic Hierarchy Process (AHP) method which determines the priority of criteria and decision tree that formed using these priorities, was updated and applied to 388 patients data collected retrospectively. Later, a web-based decision support tool named corRECTreatment was developed. The compatibility of the treatment recommendations by the expert opinion and the decision support tool was examined for its consistency. Two surgeons were requested to recommend a treatment and an overall survival value for the treatment among 20 different cases that we selected and turned into a scenario among the most common and rare treatment options in the patient data set. Results In the AHP analyses of the criteria, it was found that the matrices, generated for both decision steps, were consistent (consistency ratio<0.1). Depending on the decisions of experts, the consistency value for the most frequent cases was found to be 80% for the first decision step and 100% for the second decision step. Similarly, for rare cases consistency was 50% for the first decision step and 80% for the second decision step. Conclusions The decision model and corRECTreatment, developed by applying these on real patient data, are expected to provide potential users with decision support in rectal cancer treatment processes and facilitate them in making projections about treatment options

CorRECTreatment: a web-based decision support tool for rectal cancer treatment that uses the analytic hierarchy process and decision tree.

PubMed

Suner, A; Karakülah, G; Dicle, O; Sökmen, S; Çelikoğlu, C C

2015-01-01

The selection of appropriate rectal cancer treatment is a complex multi-criteria decision making process, in which clinical decision support systems might be used to assist and enrich physicians' decision making. The objective of the study was to develop a web-based clinical decision support tool for physicians in the selection of potentially beneficial treatment options for patients with rectal cancer. The updated decision model contained 8 and 10 criteria in the first and second steps respectively. The decision support model, developed in our previous study by combining the Analytic Hierarchy Process (AHP) method which determines the priority of criteria and decision tree that formed using these priorities, was updated and applied to 388 patients data collected retrospectively. Later, a web-based decision support tool named corRECTreatment was developed. The compatibility of the treatment recommendations by the expert opinion and the decision support tool was examined for its consistency. Two surgeons were requested to recommend a treatment and an overall survival value for the treatment among 20 different cases that we selected and turned into a scenario among the most common and rare treatment options in the patient data set. In the AHP analyses of the criteria, it was found that the matrices, generated for both decision steps, were consistent (consistency ratio<0.1). Depending on the decisions of experts, the consistency value for the most frequent cases was found to be 80% for the first decision step and 100% for the second decision step. Similarly, for rare cases consistency was 50% for the first decision step and 80% for the second decision step. The decision model and corRECTreatment, developed by applying these on real patient data, are expected to provide potential users with decision support in rectal cancer treatment processes and facilitate them in making projections about treatment options.

A Hybrid Approach Using Case-Based Reasoning and Rule-Based Reasoning to Support Cancer Diagnosis: A Pilot Study.

PubMed

Saraiva, Renata M; Bezerra, João; Perkusich, Mirko; Almeida, Hyggo; Siebra, Clauirton

2015-01-01

Recently there has been an increasing interest in applying information technology to support the diagnosis of diseases such as cancer. In this paper, we present a hybrid approach using case-based reasoning (CBR) and rule-based reasoning (RBR) to support cancer diagnosis. We used symptoms, signs, and personal information from patients as inputs to our model. To form specialized diagnoses, we used rules to define the input factors' importance according to the patient's characteristics. The model's output presents the probability of the patient having a type of cancer. To carry out this research, we had the approval of the ethics committee at Napoleão Laureano Hospital, in João Pessoa, Brazil. To define our model's cases, we collected real patient data at Napoleão Laureano Hospital. To define our model's rules and weights, we researched specialized literature and interviewed health professional. To validate our model, we used K-fold cross validation with the data collected at Napoleão Laureano Hospital. The results showed that our approach is an effective CBR system to diagnose cancer.

A web-based decision support tool for prognosis simulation in multiple sclerosis.

PubMed

Veloso, Mário

2014-09-01

A multiplicity of natural history studies of multiple sclerosis provides valuable knowledge of the disease progression but individualized prognosis remains elusive. A few decision support tools that assist the clinician in such task have emerged but have not received proper attention from clinicians and patients. The objective of the current work is to implement a web-based tool, conveying decision relevant prognostic scientific evidence, which will help clinicians discuss prognosis with individual patients. Data were extracted from a set of reference studies, especially those dealing with the natural history of multiple sclerosis. The web-based decision support tool for individualized prognosis simulation was implemented with NetLogo, a program environment suited for the development of complex adaptive systems. Its prototype has been launched online; it enables clinicians to predict both the likelihood of CIS to CDMS conversion, and the long-term prognosis of disability level and SPMS conversion, as well as assess and monitor the effects of treatment. More robust decision support tools, which convey scientific evidence and satisfy the needs of clinical practice by helping clinicians discuss prognosis expectations with individual patients, are required. The web-based simulation model herein introduced proposes to be a step forward toward this purpose. Copyright © 2014 Elsevier B.V. All rights reserved.

A GH-Based Ontology to Support Applications for Automating Decision Support

DTIC Science & Technology

2005-03-01

architecture for a decision support sys - tem. For this reason, it obtains data from, and updates, a database. IDA also wanted the prototype’s architecture...Chief In- formation Officer CoABS Control of Agent Based Sys - tems DBMS Database Management System DoD Department of Defense DTD Document Type...Generic Hub, the Moyeu Générique, and the Generische Nabe , specifying each as a separate service description with property names and values of the GH

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

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.

Design Rules for Life Support Systems

NASA Technical Reports Server (NTRS)

Jones, Harry

2002-01-01

This paper considers some of the common assumptions and engineering rules of thumb used in life support system design. One general design rule is that the longer the mission, the more the life support system should use recycling and regenerable technologies. A more specific rule is that, if the system grows more than half the food, the food plants will supply all the oxygen needed for the crew life support. There are many such design rules that help in planning the analysis of life support systems and in checking results. These rules are typically if-then statements describing the results of steady-state, "back of the envelope," mass flow calculations. They are useful in identifying plausible candidate life support system designs and in rough allocations between resupply and resource recovery. Life support system designers should always review the design rules and make quick steady state calculations before doing detailed design and dynamic simulation. This paper develops the basis for the different assumptions and design rules and discusses how they should be used. We start top-down, with the highest level requirement to sustain human beings in a closed environment off Earth. We consider the crew needs for air, water, and food. We then discuss atmosphere leakage and recycling losses. The needs to support the crew and to make up losses define the fundamental life support system requirements. We consider the trade-offs between resupplying and recycling oxygen, water, and food. The specific choices between resupply and recycling are determined by mission duration, presence of in-situ resources, etc., and are defining parameters of life support system design.

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.

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.

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.

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.

Artificial intelligence based decision support for trumpeter swan management

USGS Publications Warehouse

Sojda, Richard S.

2002-01-01

The number of trumpeter swans (Cygnus buccinator) breeding in the Tri-State area where Montana, Idaho, and Wyoming come together has declined to just a few hundred pairs. However, these birds are part of the Rocky Mountain Population which additionally has over 3,500 birds breeding in Alberta, British Columbia, Northwest Territories, and Yukon Territory. To a large degree, these birds seem to have abandoned traditional migratory pathways in the flyway. Waterfowl managers have been interested in decision support tools that would help them explore simulated management scenarios in their quest towards reaching population recovery and the reestablishment of traditional migratory pathways. I have developed a decision support system to assist biologists with such management, especially related to wetland ecology. Decision support systems use a combination of models, analytical techniques, and information retrieval to help develop and evaluate appropriate alternatives. Swan management is a domain that is ecologically complex, and this complexity is compounded by spatial and temporal issues. As such, swan management is an inherently distributed problem. Therefore, the ecological context for modeling swan movements in response to management actions was built as a multiagent system of interacting intelligent agents that implements a queuing model representing swan migration. These agents accessed ecological knowledge about swans, their habitats, and flyway management principles from three independent expert systems. The agents were autonomous, had some sensory capability, and could respond to changing conditions. A key problem when developing ecological decision support systems is empirically determining that the recommendations provided are valid. Because Rocky Mountain trumpeter swans have been surveyed for a long period of time, I was able to compare simulated distributions provided by the system with actual field observations across 20 areas for the period 1988

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.

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.

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.

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.

Implementation of clinical decision rules in the emergency department.

PubMed

Stiell, Ian G; Bennett, Carol

2007-11-01

Clinical decision rules (CDRs) are tools designed to help clinicians make bedside diagnostic and therapeutic decisions. The development of a CDR involves three stages: derivation, validation, and implementation. Several criteria need to be considered when designing and evaluating the results of an implementation trial. In this article, the authors review the results of implementation studies evaluating the effect of four CDRs: the Ottawa Ankle Rules, the Ottawa Knee Rule, the Canadian C-Spine Rule, and the Canadian CT Head Rule. Four implementation studies demonstrated that the implementation of CDRs in the emergency department (ED) safely reduced the use of radiography for ankle, knee, and cervical spine injuries. However, a recent trial failed to demonstrate an impact on computed tomography imaging rates. Well-developed and validated CDRs can be successfully implemented into practice, efficiently standardizing ED care. However, further research is needed to identify barriers to implementation in order to achieve improved uptake in the ED.

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...

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.

An Environment for Guideline-based Decision Support Systems for Outpatients Monitoring.

PubMed

Zini, Elisa M; Lanzola, Giordano; Bossi, Paolo; Quaglini, Silvana

2017-08-11

We propose an architecture for monitoring outpatients that relies on mobile technologies for acquiring data. The goal is to better control the onset of possible side effects between the scheduled visits at the clinic. We analyze the architectural components required to ensure a high level of abstraction from data. Clinical practice guidelines were formalized with Alium, an authoring tool based on the PROforma language, using SNOMED-CT as a terminology standard. The Alium engine is accessible through a set of APIs that may be leveraged for implementing an application based on standard web technologies to be used by doctors at the clinic. Data sent by patients using mobile devices need to be complemented with those already available in the Electronic Health Record to generate personalized recommendations. Thus a middleware pursuing data abstraction is required. To comply with current standards, we adopted the HL7 Virtual Medical Record for Clinical Decision Support Logical Model, Release 2. The developed architecture for monitoring outpatients includes: (1) a guideline-based Decision Support System accessible through a web application that helps the doctors with prevention, diagnosis and treatment of therapy side effects; (2) an application for mobile devices, which allows patients to regularly send data to the clinic. In order to tailor the monitoring procedures to the specific patient, the Decision Support System also helps physicians with the configuration of the mobile application, suggesting the data to be collected and the associated collection frequency that may change over time, according to the individual patient's conditions. A proof of concept has been developed with a system for monitoring the side effects of chemo-radiotherapy in head and neck cancer patients. Our environment introduces two main innovation elements with respect to similar works available in the literature. First, in order to meet the specific patients' needs, in our work the Decision

Improving the anesthetic process by a fuzzy rule based medical decision system.

PubMed

Mendez, Juan Albino; Leon, Ana; Marrero, Ayoze; Gonzalez-Cava, Jose M; Reboso, Jose Antonio; Estevez, Jose Ignacio; Gomez-Gonzalez, José F

2018-01-01

The main objective of this research is the design and implementation of a new fuzzy logic tool for automatic drug delivery in patients undergoing general anesthesia. The aim is to adjust the drug dose to the real patient needs using heuristic knowledge provided by clinicians. A two-level computer decision system is proposed. The idea is to release the clinician from routine tasks so that he can focus on other variables of the patient. The controller uses the Bispectral Index (BIS) to assess the hypnotic state of the patient. Fuzzy controller was included in a closed-loop system to reach the BIS target and reject disturbances. BIS was measured using a BIS VISTA monitor, a device capable of calculating the hypnosis level of the patient through EEG information. An infusion pump with propofol 1% is used to supply the drug to the patient. The inputs to the fuzzy inference system are BIS error and BIS rate. The output is infusion rate increment. The mapping of the input information and the appropriate output is given by a rule-base based on knowledge of clinicians. To evaluate the performance of the fuzzy closed-loop system proposed, an observational study was carried out. Eighty one patients scheduled for ambulatory surgery were randomly distributed in 2 groups: one group using a fuzzy logic based closed-loop system (FCL) to automate the administration of propofol (42 cases); the second group using manual delivering of the drug (39 cases). In both groups, the BIS target was 50. The FCL, designed with intuitive logic rules based on the clinician experience, performed satisfactorily and outperformed the manual administration in patients in terms of accuracy through the maintenance stage. Copyright © 2018 Elsevier B.V. All rights reserved.

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

A simulation-optimization-based decision support tool for mitigating traffic congestion.

DOT National Transportation Integrated Search

2009-12-01

"Traffic congestion has grown considerably in the United States over the past twenty years. In this paper, we develop : a robust decision support tool based on simulation optimization to evaluate and recommend congestion-mitigation : strategies to tr...

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.

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.

Web-based decision support system to predict risk level of long term rice production

NASA Astrophysics Data System (ADS)

Mukhlash, Imam; Maulidiyah, Ratna; Sutikno; Setiyono, Budi

2017-09-01

Appropriate decision making in risk management of rice production is very important in agricultural planning, especially for Indonesia which is an agricultural country. Good decision would be obtained if the supporting data required are satisfied and using appropriate methods. This study aims to develop a Decision Support System that can be used to predict the risk level of rice production in some districts which are central of rice production in East Java. Web-based decision support system is constructed so that the information can be easily accessed and understood. Components of the system are data management, model management, and user interface. This research uses regression models of OLS and Copula. OLS model used to predict rainfall while Copula model used to predict harvested area. Experimental results show that the models used are successfully predict the harvested area of rice production in some districts which are central of rice production in East Java at any given time based on the conditions and climate of a region. Furthermore, it can predict the amount of rice production with the level of risk. System generates prediction of production risk level in the long term for some districts that can be used as a decision support for the authorities.

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.

Evaluation of Internet-Based Clinical Decision Support Systems

PubMed Central

Thomas, Karl W; Dayton, Charles S

1999-01-01

Background Scientifically based clinical guidelines have become increasingly used to educate physicians and improve quality of care. While individual guidelines are potentially useful, repeated studies have shown that guidelines are ineffective in changing physician behavior. The Internet has evolved as a potentially useful tool for guideline education, dissemination, and implementation because of its open standards and its ability to provide concise, relevant clinical information at the location and time of need. Objective Our objective was to develop and test decision support systems (DSS) based on clinical guidelines which could be delivered over the Internet for two disease models: asthma and tuberculosis (TB) preventive therapy. Methods Using open standards of HTML and CGI, we developed an acute asthma severity assessment DSS and a preventative tuberculosis treatment DSS based on content from national guidelines that are recognized as standards of care. Both DSS's are published on the Internet and operate through a decision algorithm developed from the parent guidelines with clinical information provided by the user at the point of clinical care. We tested the effectiveness of each DSS in influencing physician decisions using clinical scenario testing. Results We first validated the asthma algorithm by comparing asthma experts' decisions with the decisions reached by nonpulmonary nurses using the computerized DSS. Using the DSS, nurses scored the same as experts (89% vs. 88%; p = NS). Using the same scenario test instrument, we next compared internal medicine residents using the DSS with residents using a printed version of the National Asthma Education Program-2 guidelines. Residents using the computerized DSS scored significantly better than residents using the paper-based guidelines (92% vs. 84%; p <0.002). We similarly compared residents using the computerized TB DSS to residents using a printed reference card; the residents using the computerized DSS

Fuzzy Based Decision Support System for Condition Assessment and Rating of Bridges

NASA Astrophysics Data System (ADS)

Srinivas, Voggu; Sasmal, Saptarshi; Karusala, Ramanjaneyulu

2016-09-01

In this work, a knowledge based decision support system has been developed to efficiently handle the issues such as distress diagnosis, assessment of damages and condition rating of existing bridges towards developing an exclusive and robust Bridge Management System (BMS) for sustainable bridges. The Knowledge Based Expert System (KBES) diagnoses the distresses and finds the cause of distress in the bridge by processing the data which are heuristic and combined with site inspection results, laboratory test results etc. The coupling of symbolic and numeric type of data has been successfully implemented in the expert system to strengthen its decision making process. Finally, the condition rating of the bridge is carried out using the assessment results obtained from the KBES and the information received from the bridge inspector. A systematic procedure has been developed using fuzzy mathematics for condition rating of bridges by combining the fuzzy weighted average and resolution identity technique. The proposed methodologies and the decision support system will facilitate in developing a robust and exclusive BMS for a network of bridges across the country and allow the bridge engineers and decision makers to carry out maintenance of bridges in a rational and systematic way.

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.

Form and Objective of the Decision Rule in Absolute Identification

NASA Technical Reports Server (NTRS)

Balakrishnan, J. D.

1997-01-01

In several conditions of a line length identification experiment, the subjects' decision making strategies were systematically biased against the responses on the edges of the stimulus range. When the range and number of the stimuli were small, the bias caused the percentage of correct responses to be highest in the center and lowest on the extremes of the range. Two general classes of decision rules that would explain these results are considered. The first class assumes that subjects intend to adopt an optimal decision rule, but systematically misrepresent one or more parameters of the decision making context. The second class assumes that subjects use a different measure of performance than the one assumed by the experimenter: instead of maximizing the chances of a correct response, the subject attempts to minimize the expected size of the response error (a "fidelity criterion"). In a second experiment, extended experience and feedback did not diminish the bias effect, but explicitly penalizing all response errors equally, regardless of their size, did reduce or eliminate it in some subjects. Both results favor the fidelity criterion over the optimal rule.

Toward the Modularization of Decision Support Systems

NASA Astrophysics Data System (ADS)

Raskin, R. G.

2009-12-01

Decision support systems are typically developed entirely from scratch without the use of modular components. This “stovepiped” approach is inefficient and costly because it prevents a developer from leveraging the data, models, tools, and services of other developers. Even when a decision support component is made available, it is difficult to know what problem it solves, how it relates to other components, or even that the component exists, The Spatial Decision Support (SDS) Consortium was formed in 2008 to organize the body of knowledge in SDS within a common portal. The portal identifies the canonical steps in the decision process and enables decision support components to be registered, categorized, and searched. This presentation describes how a decision support system can be assembled from modular models, data, tools and services, based on the needs of the Earth science application.

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

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.

Preparing for a decision support system.

PubMed

Callan, K

2000-08-01

The increasing pressure to reduce costs and improve outcomes is driving the health care industry to view information as a competitive advantage. Timely information is required to help reduce inefficiencies and improve patient care. Numerous disparate operational or transactional information systems with inconsistent and often conflicting data are no longer adequate to meet the information needs of integrated care delivery systems and networks in competitive managed care environments. This article reviews decision support system characteristics and describes a process to assess the preparedness of an organization to implement and use decision support systems to achieve a more effective, information-based decision process. Decision support tools included in this article range from reports to data mining.

Knowledge base and sensor bus messaging service architecture for critical tsunami warning and decision-support

NASA Astrophysics Data System (ADS)

Sabeur, Z. A.; Wächter, J.; Middleton, S. E.; Zlatev, Z.; Häner, R.; Hammitzsch, M.; Loewe, P.

2012-04-01

The intelligent management of large volumes of environmental monitoring data for early tsunami warning requires the deployment of robust and scalable service oriented infrastructure that is supported by an agile knowledge-base for critical decision-support In the TRIDEC project (TRIDEC 2010-2013), a sensor observation service bus of the TRIDEC system is being developed for the advancement of complex tsunami event processing and management. Further, a dedicated TRIDEC system knowledge-base is being implemented to enable on-demand access to semantically rich OGC SWE compliant hydrodynamic observations and operationally oriented meta-information to multiple subscribers. TRIDEC decision support requires a scalable and agile real-time processing architecture which enables fast response to evolving subscribers requirements as the tsunami crisis develops. This is also achieved with the support of intelligent processing services which specialise in multi-level fusion methods with relevance feedback and deep learning. The TRIDEC knowledge base development work coupled with that of the generic sensor bus platform shall be presented to demonstrate advanced decision-support with situation awareness in context of tsunami early warning and crisis management.

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.

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.

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.

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...

Visualization-based decision support for value-driven system design

NASA Astrophysics Data System (ADS)

Tibor, Elliott

In the past 50 years, the military, communication, and transportation systems that permeate our world, have grown exponentially in size and complexity. The development and production of these systems has seen ballooning costs and increased risk. This is particularly critical for the aerospace industry. The inability to deal with growing system complexity is a crippling force in the advancement of engineered systems. Value-Driven Design represents a paradigm shift in the field of design engineering that has potential to help counteract this trend. The philosophy of Value-Driven Design places the desires of the stakeholder at the forefront of the design process to capture true preferences and reveal system alternatives that were never previously thought possible. Modern aerospace engineering design problems are large, complex, and involve multiple levels of decision-making. To find the best design, the decision-maker is often required to analyze hundreds or thousands of combinations of design variables and attributes. Visualization can be used to support these decisions, by communicating large amounts of data in a meaningful way. Understanding the design space, the subsystem relationships, and the design uncertainties is vital to the advancement of Value-Driven Design as an accepted process for the development of more effective, efficient, robust, and elegant aerospace systems. This research investigates the use of multi-dimensional data visualization tools to support decision-making under uncertainty during the Value-Driven Design process. A satellite design system comprising a satellite, ground station, and launch vehicle is used to demonstrate effectiveness of new visualization methods to aid in decision support during complex aerospace system design. These methods are used to facilitate the exploration of the feasible design space by representing the value impact of system attribute changes and comparing the results of multi-objective optimization formulations

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

Rule-based support system for multiple UMLS semantic type assignments

PubMed Central

Geller, James; He, Zhe; Perl, Yehoshua; Morrey, C. Paul; Xu, Julia

2012-01-01

Background When new concepts are inserted into the UMLS, they are assigned one or several semantic types from the UMLS Semantic Network by the UMLS editors. However, not every combination of semantic types is permissible. It was observed that many concepts with rare combinations of semantic types have erroneous semantic type assignments or prohibited combinations of semantic types. The correction of such errors is resource-intensive. Objective We design a computational system to inform UMLS editors as to whether a specific combination of two, three, four, or five semantic types is permissible or prohibited or questionable. Methods We identify a set of inclusion and exclusion instructions in the UMLS Semantic Network documentation and derive corresponding rule-categories as well as rule-categories from the UMLS concept content. We then design an algorithm adviseEditor based on these rule-categories. The algorithm specifies rules for an editor how to proceed when considering a tuple (pair, triple, quadruple, quintuple) of semantic types to be assigned to a concept. Results Eight rule-categories were identified. A Web-based system was developed to implement the adviseEditor algorithm, which returns for an input combination of semantic types whether it is permitted, prohibited or (in a few cases) requires more research. The numbers of semantic type pairs assigned to each rule-category are reported. Interesting examples for each rule-category are illustrated. Cases of semantic type assignments that contradict rules are listed, including recently introduced ones. Conclusion The adviseEditor system implements explicit and implicit knowledge available in the UMLS in a system that informs UMLS editors about the permissibility of a desired combination of semantic types. Using adviseEditor might help accelerate the work of the UMLS editors and prevent erroneous semantic type assignments. PMID:23041716

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.

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

A simple threshold rule is sufficient to explain sophisticated collective decision-making.

PubMed

Robinson, Elva J H; Franks, Nigel R; Ellis, Samuel; Okuda, Saki; Marshall, James A R

2011-01-01

Decision-making animals can use slow-but-accurate strategies, such as making multiple comparisons, or opt for simpler, faster strategies to find a 'good enough' option. Social animals make collective decisions about many group behaviours including foraging and migration. The key to the collective choice lies with individual behaviour. We present a case study of a collective decision-making process (house-hunting ants, Temnothorax albipennis), in which a previously proposed decision strategy involved both quality-dependent hesitancy and direct comparisons of nests by scouts. An alternative possible decision strategy is that scouting ants use a very simple quality-dependent threshold rule to decide whether to recruit nest-mates to a new site or search for alternatives. We use analytical and simulation modelling to demonstrate that this simple rule is sufficient to explain empirical patterns from three studies of collective decision-making in ants, and can account parsimoniously for apparent comparison by individuals and apparent hesitancy (recruitment latency) effects, when available nests differ strongly in quality. This highlights the need to carefully design experiments to detect individual comparison. We present empirical data strongly suggesting that best-of-n comparison is not used by individual ants, although individual sequential comparisons are not ruled out. However, by using a simple threshold rule, decision-making groups are able to effectively compare options, without relying on any form of direct comparison of alternatives by individuals. This parsimonious mechanism could promote collective rationality in group decision-making.

Application of a web-based Decision Support System in risk management

NASA Astrophysics Data System (ADS)

Aye, Zar Chi; Jaboyedoff, Michel; Derron, Marc-Henri

2013-04-01

Increasingly, risk information is widely available with the help of advanced technologies such as earth observation satellites, global positioning technologies, coupled with hazard modeling and analysis, and geographical information systems (GIS). Even though it exists, no effort will be put into action if it is not properly presented to the decision makers. These information need to be communicated clearly and show its usefulness so that people can make better informed decision. Therefore, communicating available risk information has become an important challenge and decision support systems have been one of the significant approaches which can help not only in presenting risk information to the decision makers but also in making efficient decisions while reducing human resources and time needed. In this study, the conceptual framework of an internet-based decision support system is presented to highlight its importance role in risk management framework and how it can be applied in case study areas chosen. The main purpose of the proposed system is to facilitate the available risk information in risk reduction by taking into account of the changes in climate, land use and socio-economic along with the risk scenarios. It allows the users to formulate, compare and select risk reduction scenarios (mainly for floods and landslides) through an enhanced participatory platform with diverse stakeholders' involvement in the decision making process. It is based on the three-tier (client-server) architecture which integrates web-GIS plus DSS functionalities together with cost benefit analysis and other supporting tools. Embedding web-GIS provides its end users to make better planning and informed decisions referenced to a geographical location, which is the one of the essential factors in disaster risk reduction programs. Different risk reduction measures of a specific area (local scale) will be evaluated using this web-GIS tool, available risk scenarios obtained from

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.

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

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.

Multi-criteria clinical decision support: A primer on the use of multiple criteria decision making methods to promote evidence-based, patient-centered healthcare.

PubMed

Dolan, James G

2010-01-01

Current models of healthcare quality recommend that patient management decisions be evidence-based and patient-centered. Evidence-based decisions require a thorough understanding of current information regarding the natural history of disease and the anticipated outcomes of different management options. Patient-centered decisions incorporate patient preferences, values, and unique personal circumstances into the decision making process and actively involve both patients along with health care providers as much as possible. Fundamentally, therefore, evidence-based, patient-centered decisions are multi-dimensional and typically involve multiple decision makers.Advances in the decision sciences have led to the development of a number of multiple criteria decision making methods. These multi-criteria methods are designed to help people make better choices when faced with complex decisions involving several dimensions. They are especially helpful when there is a need to combine "hard data" with subjective preferences, to make trade-offs between desired outcomes, and to involve multiple decision makers. Evidence-based, patient-centered clinical decision making has all of these characteristics. This close match suggests that clinical decision support systems based on multi-criteria decision making techniques have the potential to enable patients and providers to carry out the tasks required to implement evidence-based, patient-centered care effectively and efficiently in clinical settings.The goal of this paper is to give readers a general introduction to the range of multi-criteria methods available and show how they could be used to support clinical decision-making. Methods discussed include the balance sheet, the even swap method, ordinal ranking methods, direct weighting methods, multi-attribute decision analysis, and the analytic hierarchy process (AHP).

Multi-criteria clinical decision support: A primer on the use of multiple criteria decision making methods to promote evidence-based, patient-centered healthcare

PubMed Central

Dolan, James G.

2010-01-01

Current models of healthcare quality recommend that patient management decisions be evidence-based and patient-centered. Evidence-based decisions require a thorough understanding of current information regarding the natural history of disease and the anticipated outcomes of different management options. Patient-centered decisions incorporate patient preferences, values, and unique personal circumstances into the decision making process and actively involve both patients along with health care providers as much as possible. Fundamentally, therefore, evidence-based, patient-centered decisions are multi-dimensional and typically involve multiple decision makers. Advances in the decision sciences have led to the development of a number of multiple criteria decision making methods. These multi-criteria methods are designed to help people make better choices when faced with complex decisions involving several dimensions. They are especially helpful when there is a need to combine “hard data” with subjective preferences, to make trade-offs between desired outcomes, and to involve multiple decision makers. Evidence-based, patient-centered clinical decision making has all of these characteristics. This close match suggests that clinical decision support systems based on multi-criteria decision making techniques have the potential to enable patients and providers to carry out the tasks required to implement evidence-based, patient-centered care effectively and efficiently in clinical settings. The goal of this paper is to give readers a general introduction to the range of multi-criteria methods available and show how they could be used to support clinical decision-making. Methods discussed include the balance sheet, the even swap method, ordinal ranking methods, direct weighting methods, multi-attribute decision analysis, and the analytic hierarchy process (AHP) PMID:21394218

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.

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.

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.

Decision support systems in health economics.

PubMed

Quaglini, S; Dazzi, L; Stefanelli, M; Barosi, G; Marchetti, M

1999-08-01

This article describes a system addressed to different health care professionals for building, using, and sharing decision support systems for resource allocation. The system deals with selected areas, namely the choice of diagnostic tests, the therapy planning, and the instrumentation purchase. Decision support is based on decision-analytic models, incorporating an explicit knowledge representation of both the medical domain knowledge and the economic evaluation theory. Application models are built on top of meta-models, that are used as guidelines for making explicit both the cost and effectiveness components. This approach improves the transparency and soundness of the collaborative decision-making process and facilitates the result interpretation.

A Bayesian model averaging method for the derivation of reservoir operating rules

NASA Astrophysics Data System (ADS)

Zhang, Jingwen; Liu, Pan; Wang, Hao; Lei, Xiaohui; Zhou, Yanlai

2015-09-01

Because the intrinsic dynamics among optimal decision making, inflow processes and reservoir characteristics are complex, functional forms of reservoir operating rules are always determined subjectively. As a result, the uncertainty of selecting form and/or model involved in reservoir operating rules must be analyzed and evaluated. In this study, we analyze the uncertainty of reservoir operating rules using the Bayesian model averaging (BMA) model. Three popular operating rules, namely piecewise linear regression, surface fitting and a least-squares support vector machine, are established based on the optimal deterministic reservoir operation. These individual models provide three-member decisions for the BMA combination, enabling the 90% release interval to be estimated by the Markov Chain Monte Carlo simulation. A case study of China's the Baise reservoir shows that: (1) the optimal deterministic reservoir operation, superior to any reservoir operating rules, is used as the samples to derive the rules; (2) the least-squares support vector machine model is more effective than both piecewise linear regression and surface fitting; (3) BMA outperforms any individual model of operating rules based on the optimal trajectories. It is revealed that the proposed model can reduce the uncertainty of operating rules, which is of great potential benefit in evaluating the confidence interval of decisions.

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…

Nurses' Clinical Decision Making on Adopting a Wound Clinical Decision Support System.

PubMed

Khong, Peck Chui Betty; Hoi, Shu Yin; Holroyd, Eleanor; Wang, Wenru

2015-07-01

Healthcare information technology systems are considered the ideal tool to inculcate evidence-based nursing practices. The wound clinical decision support system was built locally to support nurses to manage pressure ulcer wounds in their daily practice. However, its adoption rate is not optimal. The study's objective was to discover the concepts that informed the RNs' decisions to adopt the wound clinical decision support system as an evidence-based technology in their nursing practice. This was an exploratory, descriptive, and qualitative design using face-to-face interviews, individual interviews, and active participatory observation. A purposive, theoretical sample of 14 RNs was recruited from one of the largest public tertiary hospitals in Singapore after obtaining ethics approval. After consenting, the nurses were interviewed and observed separately. Recruitment stopped when data saturation was reached. All transcribed interview data underwent a concurrent thematic analysis, whereas observational data were content analyzed independently and subsequently triangulated with the interview data. Eight emerging themes were identified, namely, use of the wound clinical decision support system, beliefs in the wound clinical decision support system, influences of the workplace culture, extent of the benefits, professional control over nursing practices, use of knowledge, gut feelings, and emotions (fear, doubt, and frustration). These themes represented the nurses' mental outlook as they made decisions on adopting the wound clinical decision support system in light of the complexities of their roles and workloads. This research has provided insight on the nurses' thoughts regarding their decision to interact with the computer environment in a Singapore context. It captured the nurses' complex thoughts when deciding whether to adopt or reject information technology as they practice in a clinical setting.

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.

Using Visualization in Cockpit Decision Support Systems

NASA Technical Reports Server (NTRS)

Aragon, Cecilia R.

2005-01-01

In order to safely operate their aircraft, pilots must make rapid decisions based on integrating and processing large amounts of heterogeneous information. Visual displays are often the most efficient method of presenting safety-critical data to pilots in real time. However, care must be taken to ensure the pilot is provided with the appropriate amount of information to make effective decisions and not become cognitively overloaded. The results of two usability studies of a prototype airflow hazard visualization cockpit decision support system are summarized. The studies demonstrate that such a system significantly improves the performance of helicopter pilots landing under turbulent conditions. Based on these results, design principles and implications for cockpit decision support systems using visualization are presented.

Therapy Decision Support Based on Recommender System Methods

PubMed Central

Gräßer, Felix; Beckert, Stefanie; Küster, Denise; Schmitt, Jochen; Abraham, Susanne; Malberg, Hagen

2017-01-01

We present a system for data-driven therapy decision support based on techniques from the field of recommender systems. Two methods for therapy recommendation, namely, Collaborative Recommender and Demographic-based Recommender, are proposed. Both algorithms aim to predict the individual response to different therapy options using diverse patient data and recommend the therapy which is assumed to provide the best outcome for a specific patient and time, that is, consultation. The proposed methods are evaluated using a clinical database incorporating patients suffering from the autoimmune skin disease psoriasis. The Collaborative Recommender proves to generate both better outcome predictions and recommendation quality. However, due to sparsity in the data, this approach cannot provide recommendations for the entire database. In contrast, the Demographic-based Recommender performs worse on average but covers more consultations. Consequently, both methods profit from a combination into an overall recommender system. PMID:29065657

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.

A Fuzzy-Based Decision Support Model for Selecting the Best Dialyser Flux in Haemodialysis.

PubMed

Oztürk, Necla; Tozan, Hakan

2015-01-01

Decision making is an important procedure for every organization. The procedure is particularly challenging for complicated multi-criteria problems. Selection of dialyser flux is one of the decisions routinely made for haemodialysis treatment provided for chronic kidney failure patients. This study provides a decision support model for selecting the best dialyser flux between high-flux and low-flux dialyser alternatives. The preferences of decision makers were collected via a questionnaire. A total of 45 questionnaires filled by dialysis physicians and nephrologists were assessed. A hybrid fuzzy-based decision support software that enables the use of Analytic Hierarchy Process (AHP), Fuzzy Analytic Hierarchy Process (FAHP), Analytic Network Process (ANP), and Fuzzy Analytic Network Process (FANP) was used to evaluate the flux selection model. In conclusion, the results showed that a high-flux dialyser is the best. option for haemodialysis treatment.

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.

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

A Decision Support Framework for Science-Based, Multi-Stakeholder Deliberation: A Coral Reef Example

NASA Astrophysics Data System (ADS)

Rehr, Amanda P.; Small, Mitchell J.; Bradley, Patricia; Fisher, William S.; Vega, Ann; Black, Kelly; Stockton, Tom

2012-12-01

We present a decision support framework for science-based assessment and multi-stakeholder deliberation. The framework consists of two parts: a DPSIR (Drivers-Pressures-States-Impacts-Responses) analysis to identify the important causal relationships among anthropogenic environmental stressors, processes, and outcomes; and a Decision Landscape analysis to depict the legal, social, and institutional dimensions of environmental decisions. The Decision Landscape incorporates interactions among government agencies, regulated businesses, non-government organizations, and other stakeholders. It also identifies where scientific information regarding environmental processes is collected and transmitted to improve knowledge about elements of the DPSIR and to improve the scientific basis for decisions. Our application of the decision support framework to coral reef protection and restoration in the Florida Keys focusing on anthropogenic stressors, such as wastewater, proved to be successful and offered several insights. Using information from a management plan, it was possible to capture the current state of the science with a DPSIR analysis as well as important decision options, decision makers and applicable laws with a the Decision Landscape analysis. A structured elicitation of values and beliefs conducted at a coral reef management workshop held in Key West, Florida provided a diversity of opinion and also indicated a prioritization of several environmental stressors affecting coral reef health. The integrated DPSIR/Decision landscape framework for the Florida Keys developed based on the elicited opinion and the DPSIR analysis can be used to inform management decisions, to reveal the role that further scientific information and research might play to populate the framework, and to facilitate better-informed agreement among participants.

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.

Decision Support | Solar Research | NREL

Science.gov Websites

informed solar decision making with credible, objective, accessible, and timely resources. Solar Energy Decision Support Decision Support NREL provides technical and analytical support to support provide unbiased information on solar policies and issues for state and local government decision makers

The Cape Town Clinical Decision Rule for Streptococcal Pharyngitis in Children

PubMed Central

Engel, Mark Emmanuel; Cohen, Karen; Gounden, Ronald; Kengne, Andre P.; Barth, Dylan Dominic; Whitelaw, Andrew C; Francis, Veronica; Badri, Motasim; Stewart, Annemie; Dale, James B.; Mayosi, Bongani M.; Maartens, Gary

2016-01-01

Background Existing clinical decision rules (CDR) to diagnose group A streptococcal (GAS) pharyngitis have not been validated in sub-Saharan Africa. We developed a locally applicable CDR while evaluating existing CDRs for diagnosing GAS pharyngitis in South African children. Methods We conducted a prospective cohort study and enrolled 997 children aged 3-15 years presenting to primary care clinics with a complaint of sore throat, and whose parents provided consent. Main outcome measures were signs and symptoms of pharyngitis, and a positive GAS culture from a throat swab. Bivariate and multivariate analyses were used to develop the clinical decision rule. In addition, the diagnostic effectiveness of six existing rules for predicting a positive culture in our cohort was assessed. Results 206 of 982 children (21%) had a positive GAS culture. Tonsillar swelling, tonsillar exudates, tender or enlarged anterior cervical lymph nodes, absence of cough and absence of rhinorrhea were associated with positive cultures in bivariate and multivariate analyses. Four variables (tonsillar swelling and one of tonsillar exudate, no rhinorrhea, no cough), when used in a cumulative score, showed 83.7% sensitivity and 32.2% specificity for GAS pharyngitis. Of existing rules tested, the McIsaac rule had the highest positive predictive value (28%), but missed 49% of the culture-positive children who should have been treated. Conclusion The new four-variable clinical decision rule for GAS pharyngitis (i.e., tonsillar swelling and one of tonsillar exudate, no rhinorrhea, no cough) outperformed existing rules for GAS pharyngitis diagnosis in children with symptomatic sore throat in Cape Town. PMID:27870815

Choosing the Rules: Distinct and Overlapping Frontoparietal Representations of Task Rules for Perceptual Decisions

PubMed Central

Kriegeskorte, Nikolaus; Carlin, Johan D.; Rowe, James B.

2013-01-01

Behavior is governed by rules that associate stimuli with responses and outcomes. Human and monkey studies have shown that rule-specific information is widely represented in the frontoparietal cortex. However, it is not known how establishing a rule under different contexts affects its neural representation. Here, we use event-related functional MRI (fMRI) and multivoxel pattern classification methods to investigate the human brain's mechanisms of establishing and maintaining rules for multiple perceptual decision tasks. Rules were either chosen by participants or specifically instructed to them, and the fMRI activation patterns representing rule-specific information were compared between these contexts. We show that frontoparietal regions differ in the properties of their rule representations during active maintenance before execution. First, rule-specific information maintained in the dorsolateral and medial frontal cortex depends on the context in which it was established (chosen vs specified). Second, rule representations maintained in the ventrolateral frontal and parietal cortex are independent of the context in which they were established. Furthermore, we found that the rule-specific coding maintained in anticipation of stimuli may change with execution of the rule: representations in context-independent regions remain invariant from maintenance to execution stages, whereas rule representations in context-dependent regions do not generalize to execution stage. The identification of distinct frontoparietal systems with context-independent and context-dependent task rule representations, and the distinction between anticipatory and executive rule representations, provide new insights into the functional architecture of goal-directed behavior. PMID:23864675

Creating a GIS-Based Decision-Support System

NASA Technical Reports Server (NTRS)

Alvarado, Lori; Gates, Ann Q.; Gray, Bob; Reyes, Raul

1998-01-01

Tilting the Balance: Climate Variability and Water Resource Management in the Southwest, a regional conference hosted by the Pan American Center for Environmental Studies, will be held at The University of Texas at El Paso on March 2-4, 1998. The conference is supported through the US Global Change Research Program (USGCRP) established by the President in 1989, and codified by Congress in the Global Change Research Act of 1990. The NASA Mission to Planet Earth program is one of the workshops sponsors. The purpose of the regional workshops is to improve understanding of the consequences of global change. This workshop will be focused on issues along the border and the Rio Grande River and thus will bring together stakeholders from Mexico, California, Texas, New Mexico, Arizona and Colorado representing federal, state, and local governments; universities and laboratories; industry, agricultural and natural resource managers; and non-governmental organizations. This paper discusses the efforts of the NASA PACES center create a GIS-based decision-support system that can be used to facilitate discussion of the complex issues of resource management within the targeted international region.

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…

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.

34 CFR 31.8 - Rules of decision.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 34 Education 1 2010-07-01 2010-07-01 false Rules of decision. 31.8 Section 31.8 Education Office of the Secretary, Department of Education SALARY OFFSET FOR FEDERAL EMPLOYEES WHO ARE INDEBTED TO THE.... (3) The act or omission of an institution of higher education at which the employee was enrolled does...

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…

Diagnostic accuracy and reproducibility of the Ottawa Knee Rule vs the Pittsburgh Decision Rule.

PubMed

Cheung, Tung C; Tank, Yeliz; Breederveld, Roelf S; Tuinebreijer, Wim E; de Lange-de Klerk, Elly S M; Derksen, Robert J

2013-04-01

The aim of this present study was to compare the diagnostic accuracy and reproducibility of 2 clinical decision rules (the Ottawa Knee Rules [OKR] and Pittsburgh Decision Rules [PDR]) developed for selective use of x-rays in the evaluation of isolated knee trauma. Application of a decision rule leads to a more efficient evaluation of knee injuries and a reduction in health care costs. The diagnostic accuracy and reproducibility are compared in this study. A cross-sectional interobserver study was conducted in the emergency department of an urban teaching hospital from October 2008 to July 2009. Two observer groups collected data on standardized case-report forms: emergency medicine residents and surgical residents. Standard knee radiographs were performed in each patient. Participants were patients 18 years and older with isolated knee injuries. Pooled sensitivity and specificity were compared using χ(2) statistics, and interobserver agreement was calculated by using κ statistics. Ninety injuries were assessed. Seven injuries concerned fractures (7.8%). For the OKR, the pooled sensitivity and specificity were 0.86 (95% confidence interval [CI], 0.57-0.96) and 0.27 (95% CI, 0.21-0.35), respectively. The PDR had a pooled sensitivity and specificity of 0.86 (95% CI, 0.57-0.96) and 0.51 (95% CI, 0.44-0.59). The PDR was significantly (P = .002) more specific. The κ values for the OKR and PDR were 0.51 (95% CI, 0.32-0.71) and 0.71 (95% CI, 0.57-0.86), respectively. The PDR was found to be more specific than the OKR, with equal sensitivity. Interobserver agreement was moderate for the OKR and substantial for the PDR. Copyright © 2013 Elsevier Inc. All rights reserved.

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.

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

IBM's Health Analytics and Clinical Decision Support.

PubMed

Kohn, M S; Sun, J; Knoop, S; Shabo, A; Carmeli, B; Sow, D; Syed-Mahmood, T; Rapp, W

2014-08-15

This survey explores the role of big data and health analytics developed by IBM in supporting the transformation of healthcare by augmenting evidence-based decision-making. Some problems in healthcare and strategies for change are described. It is argued that change requires better decisions, which, in turn, require better use of the many kinds of healthcare information. Analytic resources that address each of the information challenges are described. Examples of the role of each of the resources are given. There are powerful analytic tools that utilize the various kinds of big data in healthcare to help clinicians make more personalized, evidenced-based decisions. Such resources can extract relevant information and provide insights that clinicians can use to make evidence-supported decisions. There are early suggestions that these resources have clinical value. As with all analytic tools, they are limited by the amount and quality of data. Big data is an inevitable part of the future of healthcare. There is a compelling need to manage and use big data to make better decisions to support the transformation of healthcare to the personalized, evidence-supported model of the future. Cognitive computing resources are necessary to manage the challenges in employing big data in healthcare. Such tools have been and are being developed. The analytic resources, themselves, do not drive, but support healthcare transformation.

A task-based support architecture for developing point-of-care clinical decision support systems for the emergency department.

PubMed

Wilk, S; Michalowski, W; O'Sullivan, D; Farion, K; Sayyad-Shirabad, J; Kuziemsky, C; Kukawka, B

2013-01-01

The purpose of this study was to create a task-based support architecture for developing clinical decision support systems (CDSSs) that assist physicians in making decisions at the point-of-care in the emergency department (ED). The backbone of the proposed architecture was established by a task-based emergency workflow model for a patient-physician encounter. The architecture was designed according to an agent-oriented paradigm. Specifically, we used the O-MaSE (Organization-based Multi-agent System Engineering) method that allows for iterative translation of functional requirements into architectural components (e.g., agents). The agent-oriented paradigm was extended with ontology-driven design to implement ontological models representing knowledge required by specific agents to operate. The task-based architecture allows for the creation of a CDSS that is aligned with the task-based emergency workflow model. It facilitates decoupling of executable components (agents) from embedded domain knowledge (ontological models), thus supporting their interoperability, sharing, and reuse. The generic architecture was implemented as a pilot system, MET3-AE--a CDSS to help with the management of pediatric asthma exacerbation in the ED. The system was evaluated in a hospital ED. The architecture allows for the creation of a CDSS that integrates support for all tasks from the task-based emergency workflow model, and interacts with hospital information systems. Proposed architecture also allows for reusing and sharing system components and knowledge across disease-specific CDSSs.

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.

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

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

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

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

Automatic evidence quality prediction to support evidence-based decision making.

PubMed

Sarker, Abeed; Mollá, Diego; Paris, Cécile

2015-06-01

Evidence-based medicine practice requires practitioners to obtain the best available medical evidence, and appraise the quality of the evidence when making clinical decisions. Primarily due to the plethora of electronically available data from the medical literature, the manual appraisal of the quality of evidence is a time-consuming process. We present a fully automatic approach for predicting the quality of medical evidence in order to aid practitioners at point-of-care. Our approach extracts relevant information from medical article abstracts and utilises data from a specialised corpus to apply supervised machine learning for the prediction of the quality grades. Following an in-depth analysis of the usefulness of features (e.g., publication types of articles), they are extracted from the text via rule-based approaches and from the meta-data associated with the articles, and then applied in the supervised classification model. We propose the use of a highly scalable and portable approach using a sequence of high precision classifiers, and introduce a simple evaluation metric called average error distance (AED) that simplifies the comparison of systems. We also perform elaborate human evaluations to compare the performance of our system against human judgments. We test and evaluate our approaches on a publicly available, specialised, annotated corpus containing 1132 evidence-based recommendations. Our rule-based approach performs exceptionally well at the automatic extraction of publication types of articles, with F-scores of up to 0.99 for high-quality publication types. For evidence quality classification, our approach obtains an accuracy of 63.84% and an AED of 0.271. The human evaluations show that the performance of our system, in terms of AED and accuracy, is comparable to the performance of humans on the same data. The experiments suggest that our structured text classification framework achieves evaluation results comparable to those of human performance

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.

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.

Free and open source enabling technologies for patient-centric, guideline-based clinical decision support: a survey.

PubMed

Leong, T Y; Kaiser, K; Miksch, S

2007-01-01

Guideline-based clinical decision support is an emerging paradigm to help reduce error, lower cost, and improve quality in evidence-based medicine. The free and open source (FOS) approach is a promising alternative for delivering cost-effective information technology (IT) solutions in health care. In this paper, we survey the current FOS enabling technologies for patient-centric, guideline-based care, and discuss the current trends and future directions of their role in clinical decision support. We searched PubMed, major biomedical informatics websites, and the web in general for papers and links related to FOS health care IT systems. We also relied on our background and knowledge for specific subtopics. We focused on the functionalities of guideline modeling tools, and briefly examined the supporting technologies for terminology, data exchange and electronic health record (EHR) standards. To effectively support patient-centric, guideline-based care, the computerized guidelines and protocols need to be integrated with existing clinical information systems or EHRs. Technologies that enable such integration should be accessible, interoperable, and scalable. A plethora of FOS tools and techniques for supporting different knowledge management and quality assurance tasks involved are available. Many challenges, however, remain in their implementation. There are active and growing trends of deploying FOS enabling technologies for integrating clinical guidelines, protocols, and pathways into the main care processes. The continuing development and maturation of such technologies are likely to make increasingly significant contributions to patient-centric, guideline-based clinical decision support.

Web-Based Versus Usual Care and Other Formats of Decision Aids to Support Prostate Cancer Screening Decisions: Systematic Review and Meta-Analysis.

PubMed

Baptista, Sofia; Teles Sampaio, Elvira; Heleno, Bruno; Azevedo, Luís Filipe; Martins, Carlos

2018-06-26

Prostate cancer is a leading cause of cancer among men. Because screening for prostate cancer is a controversial issue, many experts in the field have defended the use of shared decision making using validated decision aids, which can be presented in different formats (eg, written, multimedia, Web). Recent studies have concluded that decision aids improve knowledge and reduce decisional conflict. This meta-analysis aimed to investigate the impact of using Web-based decision aids to support men's prostate cancer screening decisions in comparison with usual care and other formats of decision aids. We searched PubMed, CINAHL, PsycINFO, and Cochrane CENTRAL databases up to November 2016. This search identified randomized controlled trials, which assessed Web-based decision aids for men making a prostate cancer screening decision and reported quality of decision-making outcomes. Two reviewers independently screened citations for inclusion criteria, extracted data, and assessed risk of bias. Using a random-effects model, meta-analyses were conducted pooling results using mean differences (MD), standardized mean differences (SMD), and relative risks (RR). Of 2406 unique citations, 7 randomized controlled trials met the inclusion criteria. For risk of bias, selective outcome reporting and participant/personnel blinding were mostly rated as unclear due to inadequate reporting. Based on seven items, two studies had high risk of bias for one item. Compared to usual care, Web-based decision aids increased knowledge (SMD 0.46; 95% CI 0.18-0.75), reduced decisional conflict (MD -7.07%; 95% CI -9.44 to -4.71), and reduced the practitioner control role in the decision-making process (RR 0.50; 95% CI 0.31-0.81). Web-based decision aids compared to printed decision aids yielded no differences in knowledge, decisional conflict, and participation in decision or screening behaviors. Compared to video decision aids, Web-based decision aids showed lower average knowledge scores (SMD -0

Evaluating a Web-Based MMR Decision Aid to Support Informed Decision-Making by UK Parents: A Before-and-After Feasibility Study

ERIC Educational Resources Information Center

Jackson, Cath; Cheater, Francine M.; Peacock, Rose; Leask, Julie; Trevena, Lyndal

2010-01-01

Objective: The objective of this feasibility study was to evaluate the acceptability and potential effectiveness of a web-based MMR decision aid in supporting informed decision-making for the MMR vaccine. Design: This was a prospective before-and-after evaluation. Setting: Thirty parents of children eligible for MMR vaccination were recruited from…

A rule-based approach for the correlation of alarms to support Disaster and Emergency Management

NASA Astrophysics Data System (ADS)

Gloria, M.; Minei, G.; Lersi, V.; Pasquariello, D.; Monti, C.; Saitto, A.

2009-04-01

application as CiscoWorks, HP OpenView NNM and Operation, BMC Patrol, etc. Analysis of text of an alarm can detect some keywords that allow to classify the particular event. The inference rules were developed by means an analysis about news regard real emergency found by web reaserches. We have seen that often a kind of emergency is characterized by more keyword. Keywords are not uniquely associated with a specific emergency, but they can be shared by different types of emergencies (such as. keyword "landslide" can be associated both emergency "landslide" and emergency "Flood"). However, the identification of two or more keywords associated with a particular type of emergency identified in most cases the correct type of emergency. So, for example, if text contains words as "water", "flood", "overflowing", "landslide" o other words belonging to the set of defined keywords or words that have some root of keywords, the system "decides" that this alarm belongs to specific typology, in this case "flood typology". The system has the memory of this information, so if a new alarm is reported and belongs to one of the typology already identified, it proceeds with the comparison of coordinates. The comparison between the centers of the alarms allows to see if they describe an area inscribed in an ideal circle that has centered on the first alarm and radius defined by the typology above mentioned. If this happens the system CI6 creates an emergency that has centered on the centre of that area and typology equal to that of the alarms. It follows that an emergency is represented by at least two alarms. Thus, the system suggests to manager (CI6's user) the possibility that most alarms can concern same events and makes a classification of this event. It is important to stress that CI6 is a system of decision support, hence also this service is limited to providing advice to the user to facilitate his task, leaving him the decision to accept it or not. REFERENCES SEC (Simple Event Correlator

Bayesian Decision Support for Adaptive Lung Treatments

NASA Astrophysics Data System (ADS)

McShan, Daniel; Luo, Yi; Schipper, Matt; TenHaken, Randall

2014-03-01

Purpose: A Bayesian Decision Network will be demonstrated to provide clinical decision support for adaptive lung response-driven treatment management based on evidence that physiologic metrics may correlate better with individual patient response than traditional (population-based) dose and volume-based metrics. Further, there is evidence that information obtained during the course of radiation therapy may further improve response predictions. Methods: Clinical factors were gathered for 58 patients including planned mean lung dose, and the bio-markers IL-8 and TGF-β1 obtained prior to treatment and two weeks into treatment along with complication outcomes for these patients. A Bayesian Decision Network was constructed using Netica 5.0.2 from Norsys linking these clinical factors to obtain a prediction of radiation induced lung disese (RILD) complication. A decision node was added to the network to provide a plan adaption recommendation based on the trade-off between the RILD prediction and complexity of replanning. A utility node provides the weighting cost between the competing factors. Results: The decision node predictions were optimized against the data for the 58 cases. With this decision network solution, one can consider the decision result for a new patient with specific findings to obtain a recommendation to adaptively modify the originally planned treatment course. Conclusions: A Bayesian approach allows handling and propagating probabilistic data in a logical and principled manner. Decision networks provide the further ability to provide utility-based trade-offs, reflecting non-medical but practical cost/benefit analysis. The network demonstrated illustrates the basic concept, but many other factors may affect these decisions and work on building better models are being designed and tested. Acknowledgement: Supported by NIH-P01-CA59827

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.

Enabling Cross-Platform Clinical Decision Support through Web-Based Decision Support in Commercial Electronic Health Record Systems: Proposal and Evaluation of Initial Prototype Implementations

PubMed Central

Zhang, Mingyuan; Velasco, Ferdinand T.; Musser, R. Clayton; Kawamoto, Kensaku

2013-01-01

Enabling clinical decision support (CDS) across multiple electronic health record (EHR) systems has been a desired but largely unattained aim of clinical informatics, especially in commercial EHR systems. A potential opportunity for enabling such scalable CDS is to leverage vendor-supported, Web-based CDS development platforms along with vendor-supported application programming interfaces (APIs). Here, we propose a potential staged approach for enabling such scalable CDS, starting with the use of custom EHR APIs and moving towards standardized EHR APIs to facilitate interoperability. We analyzed three commercial EHR systems for their capabilities to support the proposed approach, and we implemented prototypes in all three systems. Based on these analyses and prototype implementations, we conclude that the approach proposed is feasible, already supported by several major commercial EHR vendors, and potentially capable of enabling cross-platform CDS at scale. PMID:24551426

A study on spatial decision support systems for HIV/AIDS prevention based on COM GIS technology

NASA Astrophysics Data System (ADS)

Yang, Kun; Luo, Huasong; Peng, Shungyun; Xu, Quanli

2007-06-01

Based on the deeply analysis of the current status and the existing problems of GIS technology applications in Epidemiology, this paper has proposed the method and process for establishing the spatial decision support systems of AIDS epidemic prevention by integrating the COM GIS, Spatial Database, GPS, Remote Sensing, and Communication technologies, as well as ASP and ActiveX software development technologies. One of the most important issues for constructing the spatial decision support systems of AIDS epidemic prevention is how to integrate the AIDS spreading models with GIS. The capabilities of GIS applications in the AIDS epidemic prevention have been described here in this paper firstly. Then some mature epidemic spreading models have also been discussed for extracting the computation parameters. Furthermore, a technical schema has been proposed for integrating the AIDS spreading models with GIS and relevant geospatial technologies, in which the GIS and model running platforms share a common spatial database and the computing results can be spatially visualized on Desktop or Web GIS clients. Finally, a complete solution for establishing the decision support systems of AIDS epidemic prevention has been offered in this paper based on the model integrating methods and ESRI COM GIS software packages. The general decision support systems are composed of data acquisition sub-systems, network communication sub-systems, model integrating sub-systems, AIDS epidemic information spatial database sub-systems, AIDS epidemic information querying and statistical analysis sub-systems, AIDS epidemic dynamic surveillance sub-systems, AIDS epidemic information spatial analysis and decision support sub-systems, as well as AIDS epidemic information publishing sub-systems based on Web GIS.

Reducing Diagnostic Error with Computer-Based Clinical Decision Support

ERIC Educational Resources Information Center

Greenes, Robert A.

2009-01-01

Information technology approaches to delivering diagnostic clinical decision support (CDS) are the subject of the papers to follow in the proceedings. These will address the history of CDS and present day approaches (Miller), evaluation of diagnostic CDS methods (Friedman), and the role of clinical documentation in supporting diagnostic decision…

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

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.

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.

Decision support system based on DPSIR framework for a low flow Mediterranean river basin

NASA Astrophysics Data System (ADS)

Bangash, Rubab Fatima; Kumar, Vikas; Schuhmacher, Marta

2013-04-01

The application of decision making practices are effectively enhanced by adopting a procedural approach setting out a general methodological framework within which specific methods, models and tools can be integrated. Integrated Catchment Management is a process that recognizes the river catchment as a basic organizing unit for understanding and managing ecosystem process. Decision support system becomes more complex by considering unavoidable human activities within a catchment that are motivated by multiple and often competing criteria and/or constraints. DPSIR is a causal framework for describing the interactions between society and the environment. This framework has been adopted by the European Environment Agency and the components of this model are: Driving forces, Pressures, States, Impacts and Responses. The proposed decision support system is a two step framework based on DPSIR. Considering first three component of DPSIR, Driving forces, Pressures and States, hydrological and ecosystem services models are developed. The last two components, Impact and Responses, helped to develop Bayesian Network to integrate the models. This decision support system also takes account of social, economic and environmental aspects. A small river of Catalonia (Northeastern Spain), Francoli River with a low flow (~2 m3/s) is selected for integration of catchment assessment models and to improve knowledge transfer from research to the stakeholders with a view to improve decision making process. DHI's MIKE BASIN software is used to evaluate the low-flow Francolí River with respect to the water bodies' characteristics and also to assess the impact of human activities aiming to achieve good water status for all waters to comply with the WFD's River Basin Management Plan. Based on ArcGIS, MIKE BASIN is a versatile decision support tool that provides a simple and powerful framework for managers and stakeholders to address multisectoral allocation and environmental issues in river

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

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

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.

Effects of documentation-based decision support on chronic disease management.

PubMed

Schnipper, Jeffrey L; Linder, Jeffrey A; Palchuk, Matvey B; Yu, D Tony; McColgan, Kerry E; Volk, Lynn A; Tsurikova, Ruslana; Melnikas, Andrea J; Einbinder, Jonathan S; Middleton, Blackford

2010-12-01

To evaluate whether a new documentation-based clinical decision support system (CDSS) is effective in addressing deficiencies in the care of patients with coronary artery disease (CAD) and diabetes mellitus (DM). Controlled trial randomized by physician. We assigned primary care physicians (PCPs) in 10 ambulatory practices to usual care or the CAD/DM Smart Form for 9 months. The primary outcome was the proportion of deficiencies in care that were addressed within 30 days after a patient visit. The Smart Form was used for 5.6% of eligible patients. In the intention-to-treat analysis, patients of intervention PCPs had a greater proportion of deficiencies addressed within 30 days of a visit compared with controls (11.4% vs 10.1%, adjusted and clustered odds ratio =1.14; 95% confidence interval, 1.02-1.28; P = .02). Differences were more pronounced in the "on-treatment" analysis: 17.0% of deficiencies were addressed after visits in which the Smart Form was used compared with 10.6% of deficiencies after visits in which it was not used (P <.001). Measures that improved included documentation of smoking status and prescription of antiplatelet agents when appropriate. Overall use of the CAD/DM Smart Form was low, and improvements in management were modest. When used, documentation-based decision support shows promise, and future studies should focus on refining such tools, integrating them into current electronic health record platforms, and promoting their use, perhaps through organizational changes to primary care practices.

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.

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

Moral empiricism and the bias for act-based rules.

PubMed

Ayars, Alisabeth; Nichols, Shaun

2017-10-01

Previous studies on rule learning show a bias in favor of act-based rules, which prohibit intentionally producing an outcome but not merely allowing the outcome. Nichols, Kumar, Lopez, Ayars, and Chan (2016) found that exposure to a single sample violation in which an agent intentionally causes the outcome was sufficient for participants to infer that the rule was act-based. One explanation is that people have an innate bias to think rules are act-based. We suggest an alternative empiricist account: since most rules that people learn are act-based, people form an overhypothesis (Goodman, 1955) that rules are typically act-based. We report three studies that indicate that people can use information about violations to form overhypotheses about rules. In study 1, participants learned either three "consequence-based" rules that prohibited allowing an outcome or three "act-based" rules that prohibiting producing the outcome; in a subsequent learning task, we found that participants who had learned three consequence-based rules were more likely to think that the new rule prohibited allowing an outcome. In study 2, we presented participants with either 1 consequence-based rule or 3 consequence-based rules, and we found that those exposed to 3 such rules were more likely to think that a new rule was also consequence based. Thus, in both studies, it seems that learning 3 consequence-based rules generates an overhypothesis to expect new rules to be consequence-based. In a final study, we used a more subtle manipulation. We exposed participants to examples act-based or accident-based (strict liability) laws and then had them learn a novel rule. We found that participants who were exposed to the accident-based laws were more likely to think a new rule was accident-based. The fact that participants' bias for act-based rules can be shaped by evidence from other rules supports the idea that the bias for act-based rules might be acquired as an overhypothesis from the

New approaches for real time decision support systems

NASA Technical Reports Server (NTRS)

Hair, D. Charles; Pickslay, Kent

1994-01-01

NCCOSC RDT&E Division (NRaD) is conducting research into ways of improving decision support systems (DSS) that are used in tactical Navy decision making situations. The research has focused on the incorporation of findings about naturalistic decision-making processes into the design of the DSS. As part of that research, two computer tools were developed that model the two primary naturalistic decision-making strategies used by Navy experts in tactical settings. Current work is exploring how best to incorporate the information produced by those tools into an existing simulation of current Navy decision support systems. This work has implications for any applications involving the need to make decisions under time constraints, based on incomplete or ambiguous data.

Design, implementation, use, and preliminary evaluation of SEBASTIAN, a standards-based Web service for clinical decision support.

PubMed

Kawamoto, Kensaku; Lobach, David F

2005-01-01

Despite their demonstrated ability to improve care quality, clinical decision support systems are not widely used. In part, this limited use is due to the difficulty of sharing medical knowledge in a machine-executable format. To address this problem, we developed a decision support Web service known as SEBASTIAN. In SEBASTIAN, individual knowledge modules define the data requirements for assessing a patient, the conclusions that can be drawn using that data, and instructions on how to generate those conclusions. Using standards-based XML messages transmitted over HTTP, client decision support applications provide patient data to SEBASTIAN and receive patient-specific assessments and recommendations. SEBASTIAN has been used to implement four distinct decision support systems; an architectural overview is provided for one of these systems. Preliminary assessments indicate that SEBASTIAN fulfills all original design objectives, including the re-use of executable medical knowledge across diverse applications and care settings, the straightforward authoring of knowledge modules, and use of the framework to implement decision support applications with significant clinical utility.

Clinical-decision support based on medical literature: A complex network approach

NASA Astrophysics Data System (ADS)

Jiang, Jingchi; Zheng, Jichuan; Zhao, Chao; Su, Jia; Guan, Yi; Yu, Qiubin

2016-10-01

In making clinical decisions, clinicians often review medical literature to ensure the reliability of diagnosis, test, and treatment because the medical literature can answer clinical questions and assist clinicians making clinical decisions. Therefore, finding the appropriate literature is a critical problem for clinical-decision support (CDS). First, the present study employs search engines to retrieve relevant literature about patient records. However, the result of the traditional method is usually unsatisfactory. To improve the relevance of the retrieval result, a medical literature network (MLN) based on these retrieved papers is constructed. Then, we show that this MLN has small-world and scale-free properties of a complex network. According to the structural characteristics of the MLN, we adopt two methods to further identify the potential relevant literature in addition to the retrieved literature. By integrating these potential papers into the MLN, a more comprehensive MLN is built to answer the question of actual patient records. Furthermore, we propose a re-ranking model to sort all papers by relevance. We experimentally find that the re-ranking model can improve the normalized discounted cumulative gain of the results. As participants of the Text Retrieval Conference 2015, our clinical-decision method based on the MLN also yields higher scores than the medians in most topics and achieves the best scores for topics: #11 and #12. These research results indicate that our study can be used to effectively assist clinicians in making clinical decisions, and the MLN can facilitate the investigation of CDS.

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.

Order of Verdict Consideration and Decision Rule Effects on Mock Jury Decision Making.

ERIC Educational Resources Information Center

Olaye, Imafidon M.

A study investigated the effects of order verdict consideration and decision rule on jury verdicts. After reading the summary of an actual trial, 240 mock jurors drawn from undergraduate communications classes were randomly assigned to six-member juries. Jury assignments were made under two verdict orders (ascending and descending order of…

A rule-based automatic sleep staging method.

PubMed

Liang, Sheng-Fu; Kuo, Chin-En; Hu, Yu-Han; Cheng, Yu-Shian

2012-03-30

In this paper, a rule-based automatic sleep staging method was proposed. Twelve features including temporal and spectrum analyses of the EEG, EOG, and EMG signals were utilized. Normalization was applied to each feature to eliminating individual differences. A hierarchical decision tree with fourteen rules was constructed for sleep stage classification. Finally, a smoothing process considering the temporal contextual information was applied for the continuity. The overall agreement and kappa coefficient of the proposed method applied to the all night polysomnography (PSG) of seventeen healthy subjects compared with the manual scorings by R&K rules can reach 86.68% and 0.79, respectively. This method can integrate with portable PSG system for sleep evaluation at-home in the near future. Copyright © 2012 Elsevier B.V. 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 Decision Support Framework For Science-Based, Multi-Stakeholder Deliberation: A Coral Reef Example

EPA Science Inventory

We present a decision support framework for science-based assessment and multi-stakeholder deliberation. The framework consists of two parts: a DPSIR (Drivers-Pressures-States-Impacts-Responses) analysis to identify the important causal relationships among anthropogenic environ...

IBM’s Health Analytics and Clinical Decision Support

PubMed Central

Sun, J.; Knoop, S.; Shabo, A.; Carmeli, B.; Sow, D.; Syed-Mahmood, T.; Rapp, W.

2014-01-01

Summary Objectives This survey explores the role of big data and health analytics developed by IBM in supporting the transformation of healthcare by augmenting evidence-based decision-making. Methods Some problems in healthcare and strategies for change are described. It is argued that change requires better decisions, which, in turn, require better use of the many kinds of healthcare information. Analytic resources that address each of the information challenges are described. Examples of the role of each of the resources are given. Results There are powerful analytic tools that utilize the various kinds of big data in healthcare to help clinicians make more personalized, evidenced-based decisions. Such resources can extract relevant information and provide insights that clinicians can use to make evidence-supported decisions. There are early suggestions that these resources have clinical value. As with all analytic tools, they are limited by the amount and quality of data. Conclusion Big data is an inevitable part of the future of healthcare. There is a compelling need to manage and use big data to make better decisions to support the transformation of healthcare to the personalized, evidence-supported model of the future. Cognitive computing resources are necessary to manage the challenges in employing big data in healthcare. Such tools have been and are being developed. The analytic resources, themselves, do not drive, but support healthcare transformation. PMID:25123736

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.

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…

Geospatial decision support systems for societal decision making

USGS Publications Warehouse

Bernknopf, R.L.

2005-01-01

While science provides reliable information to describe and understand the earth and its natural processes, it can contribute more. There are many important societal issues in which scientific information can play a critical role. Science can add greatly to policy and management decisions to minimize loss of life and property from natural and man-made disasters, to manage water, biological, energy, and mineral resources, and in general, to enhance and protect our quality of life. However, the link between science and decision-making is often complicated and imperfect. Technical language and methods surround scientific research and the dissemination of its results. Scientific investigations often are conducted under different conditions, with different spatial boundaries, and in different timeframes than those needed to support specific policy and societal decisions. Uncertainty is not uniformly reported in scientific investigations. If society does not know that data exist, what the data mean, where to use the data, or how to include uncertainty when a decision has to be made, then science gets left out -or misused- in a decision making process. This paper is about using Geospatial Decision Support Systems (GDSS) for quantitative policy analysis. Integrated natural -social science methods and tools in a Geographic Information System that respond to decision-making needs can be used to close the gap between science and society. The GDSS has been developed so that nonscientists can pose "what if" scenarios to evaluate hypothetical outcomes of policy and management choices. In this approach decision makers can evaluate the financial and geographic distribution of potential policy options and their societal implications. Actions, based on scientific information, can be taken to mitigate hazards, protect our air and water quality, preserve the planet's biodiversity, promote balanced land use planning, and judiciously exploit natural resources. Applications using the

Shared decision-making and decision support: their role in obstetrics and gynecology.

PubMed

Tucker Edmonds, Brownsyne

2014-12-01

To discuss the role for shared decision-making in obstetrics/gynecology and to review evidence on the impact of decision aids on reproductive health decision-making. Among the 155 studies included in a 2014 Cochrane review of decision aids, 31 (29%) addressed reproductive health decisions. Although the majority did not show evidence of an effect on treatment choice, there was a greater uptake of mammography in selected groups of women exposed to decision aids compared with usual care; and a statistically significant reduction in the uptake of hormone replacement therapy among detailed decision aid users compared with simple decision aid users. Studies also found an effect on patient-centered outcomes of care, such as medication adherence, quality-of-life measures, and anxiety scores. In maternity care, only decision analysis tools affected final treatment choice, and patient-directed aids yielded no difference in planned mode of birth after cesarean. There is untapped potential for obstetricians/gynecologists to optimize decision support for reproductive health decisions. Given the limited evidence-base guiding practice, the preference-sensitive nature of reproductive health decisions, and the increase in policy efforts and financial incentives to optimize patients' satisfaction, it is increasingly important for obstetricians/gynecologists to appreciate the role of shared decision-making and decision support in providing patient-centered reproductive healthcare.

Development of transportation asset management decision support tools : final report.

DOT National Transportation Integrated Search

2017-08-09

This study developed a web-based prototype decision support platform to demonstrate the benefits of transportation asset management in monitoring asset performance, supporting asset funding decisions, planning budget tradeoffs, and optimizing resourc...

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

Decision Support System Requirements Definition for Human Extravehicular Activity Based on Cognitive Work Analysis

PubMed Central

Miller, Matthew James; McGuire, Kerry M.; Feigh, Karen M.

2016-01-01

The design and adoption of decision support systems within complex work domains is a challenge for cognitive systems engineering (CSE) practitioners, particularly at the onset of project development. This article presents an example of applying CSE techniques to derive design requirements compatible with traditional systems engineering to guide decision support system development. Specifically, it demonstrates the requirements derivation process based on cognitive work analysis for a subset of human spaceflight operations known as extravehicular activity. The results are presented in two phases. First, a work domain analysis revealed a comprehensive set of work functions and constraints that exist in the extravehicular activity work domain. Second, a control task analysis was performed on a subset of the work functions identified by the work domain analysis to articulate the translation of subject matter states of knowledge to high-level decision support system requirements. This work emphasizes an incremental requirements specification process as a critical component of CSE analyses to better situate CSE perspectives within the early phases of traditional systems engineering design. PMID:28491008

Decision Support System Requirements Definition for Human Extravehicular Activity Based on Cognitive Work Analysis.

PubMed

Miller, Matthew James; McGuire, Kerry M; Feigh, Karen M

2017-06-01

The design and adoption of decision support systems within complex work domains is a challenge for cognitive systems engineering (CSE) practitioners, particularly at the onset of project development. This article presents an example of applying CSE techniques to derive design requirements compatible with traditional systems engineering to guide decision support system development. Specifically, it demonstrates the requirements derivation process based on cognitive work analysis for a subset of human spaceflight operations known as extravehicular activity . The results are presented in two phases. First, a work domain analysis revealed a comprehensive set of work functions and constraints that exist in the extravehicular activity work domain. Second, a control task analysis was performed on a subset of the work functions identified by the work domain analysis to articulate the translation of subject matter states of knowledge to high-level decision support system requirements. This work emphasizes an incremental requirements specification process as a critical component of CSE analyses to better situate CSE perspectives within the early phases of traditional systems engineering design.

Spatial decision support system for tobacco enterprise based on spatial data mining

NASA Astrophysics Data System (ADS)

Mei, Xin; Liu, Junyi; Zhang, Xuexia; Cui, Weihong

2007-11-01

Tobacco enterprise is a special enterprise, which has strong correlation to regional geography. But in the past research and application, the combination between tobacco and GIS is limited to use digital maps to assist cigarette distribution. How to comprehensively import 3S technique and spatial data mining (SDM) to construct spatial decision support system (SDSS) of tobacco enterprise is the main research aspect in this paper. The paper concretely analyzes the GIS requirements in tobacco enterprise for planning location of production, monitoring production management and product sale at the beginning. Then holistic solution is presented and frame design for tobacco enterprise spatial decision based on SDM is given. This paper describes how to use spatial analysis and data mining to realize the spatial decision processing such as monitoring tobacco planted acreage, analyzing and planning the cigarette sale network and so on.

Implementation of workflow engine technology to deliver basic clinical decision support functionality

PubMed Central

2011-01-01

Background 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. Results 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. Conclusions We describe an implementation of

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

A medical informatics perspective on clinical decision support systems. Findings from the yearbook 2013 section on decision support.

PubMed

Bouaud, J; Lamy, J-B

2013-01-01

To summarize excellent research and to select best papers published in 2012 in the field of computer-based decision support in healthcare. A bibliographic search focused on clinical decision support systems (CDSSs) and computer provider order entry was performed, followed by a double-blind literature review. The review process yielded six papers, illustrating various aspects of clinical decision support. The first paper is a systematic review of CDSS intervention trials in real settings, and considers different types of possible outcomes. It emphasizes the heterogeneity of studies and confirms that CDSSs can improve process measures but that evidence lacks for other types of outcomes, especially clinical or economic. Four other papers tackle the safety of drug prescribing and show that CDSSs can be efficient in reducing prescription errors. The sixth paper exemplifies the growing role of ontological resources which can be used for several applications including decision support. CDSS research has to be continuously developed and assessed. The wide variety of systems and of interventions limits the understanding of factors of success of CDSS implementations. A standardization in the characterization of CDSSs and of intervention trial reporting will help to overcome this obstacle.

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.

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

Intelligent ship traffic monitoring for oil spill prevention: risk based decision support building on AIS.

PubMed

Eide, Magnus S; Endresen, Oyvind; Brett, Per Olaf; Ervik, Jon Leon; Røang, Kjell

2007-02-01

The paper describes a model, which estimates the risk levels of individual crude oil tankers. The intended use of the model, which is ready for trial implementation at The Norwegian Coastal Administrations new Vardø VTS (Vessel Traffic Service) centre, is to facilitate the comparison of ships and to support a risk based decision on which ships to focus attention on. For a VTS operator, tasked with monitoring hundreds of ships, this is a valuable decision support tool. The model answers the question, "Which ships are likely to produce an oil spill accident, and how much is it likely to spill?".

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).

Decision support tools to support the operations of traffic management centers (TMC)

DOT National Transportation Integrated Search

2011-01-31

The goal of this project is to develop decision support tools to support traffic management operations based on collected intelligent transportation system (ITS) data. The project developments are in accordance with the needs of traffic management ce...

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

User-centered design to improve clinical decision support in primary care.

PubMed

Brunner, Julian; Chuang, Emmeline; Goldzweig, Caroline; Cain, Cindy L; Sugar, Catherine; Yano, Elizabeth M

2017-08-01

A growing literature has demonstrated the ability of user-centered design to make clinical decision support systems more effective and easier to use. However, studies of user-centered design have rarely examined more than a handful of sites at a time, and have frequently neglected the implementation climate and organizational resources that influence clinical decision support. The inclusion of such factors was identified by a systematic review as "the most important improvement that can be made in health IT evaluations." (1) Identify the prevalence of four user-centered design practices at United States Veterans Affairs (VA) primary care clinics and assess the perceived utility of clinical decision support at those clinics; (2) Evaluate the association between those user-centered design practices and the perceived utility of clinical decision support. We analyzed clinic-level survey data collected in 2006-2007 from 170 VA primary care clinics. We examined four user-centered design practices: 1) pilot testing, 2) provider satisfaction assessment, 3) formal usability assessment, and 4) analysis of impact on performance improvement. We used a regression model to evaluate the association between user-centered design practices and the perceived utility of clinical decision support, while accounting for other important factors at those clinics, including implementation climate, available resources, and structural characteristics. We also examined associations separately at community-based clinics and at hospital-based clinics. User-centered design practices for clinical decision support varied across clinics: 74% conducted pilot testing, 62% conducted provider satisfaction assessment, 36% conducted a formal usability assessment, and 79% conducted an analysis of impact on performance improvement. Overall perceived utility of clinical decision support was high, with a mean rating of 4.17 (±.67) out of 5 on a composite measure. "Analysis of impact on performance

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

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.

A National Crop Progress Monitoring and Decision Support System Based on NASA Earth Science Results

NASA Astrophysics Data System (ADS)

di, L.; Yang, Z.

2009-12-01

Timely and accurate information on weekly crop progress and development is essential to a dynamic agricultural industry in the U. S. and the world. By law, the National Agricultural Statistics Service (NASS) of the U. S. Department of Agriculture’s (USDA) is responsible for monitoring and assessing U.S. agricultural production. Currently NASS compiles and issues weekly state and national crop progress and development reports based on reports from knowledgeable state and county agricultural officials and farmers. Such survey-based reports are subjectively estimated for an entire county, lack spatial coverage, and are labor intensive. There has been limited use of remote sensing data to assess crop conditions. NASS produces weekly 1-km resolution un-calibrated AVHRR-based NDVI static images to represent national vegetation conditions but there is no quantitative crop progress information. This presentation discusses the early result for developing a National Crop Progress Monitoring and Decision Support System. The system will overcome the shortcomings of the existing systems by integrating NASA satellite and model-based land surface and weather products, NASS’ wealth of internal crop progress and condition data and Cropland Data Layers (CDL), and the Farm Service Agency’s (FSA) Common Land Units (CLU). The system, using service-oriented architecture and web service technologies, will automatically produce and disseminate quantitative national crop progress maps and associated decision support data at 250-m resolution, as well as summary reports to support NASS and worldwide users in their decision-making. It will provide overall and specific crop progress for individual crops from the state level down to CLU field level to meet different users’ needs on all known croplands. This will greatly enhance the effectiveness and accuracy of the NASS aggregated crop condition data and charts of and provides objective and scientific evidence and guidance for the

Designing Computerized Decision Support That Works for Clinicians and Families

PubMed Central

Fiks, Alexander G.

2011-01-01

Evidence-based decision-making is central to the practice of pediatrics. Clinical trials and other biomedical research provide a foundation for this process, and practice guidelines, drawing from their results, inform the optimal management of an increasing number of childhood health problems. However, many clinicians fail to adhere to guidelines. Clinical decision support delivered using health information technology, often in the form of electronic health records, provides a tool to deliver evidence-based information to the point of care and has the potential to overcome barriers to evidence-based practice. An increasing literature now informs how these systems should be designed and implemented to most effectively improve outcomes in pediatrics. Through the examples of computerized physician order entry, as well as the impact of alerts at the point of care on immunization rates, the delivery of evidence-based asthma care, and the follow-up of children with attention deficit hyperactivity disorder, the following review addresses strategies for success in using these tools. The following review argues that, as decision support evolves, the clinician should no longer be the sole target of information and alerts. Through the Internet and other technologies, families are increasingly seeking health information and gathering input to guide health decisions. By enlisting clinical decision support systems to deliver evidence-based information to both clinicians and families, help families express their preferences and goals, and connect families to the medical home, clinical decision support may ultimately be most effective in improving outcomes. PMID:21315295

Decision Support Framework (DSF) (Formerly Decision Support Platform)

EPA Science Inventory

The Science Advisory Board (SAB) provided several comments on the draft Ecosystem Services Research Program's (ESRP's) Multi-Year Plan (MYP). This presentation provides a response to comments related to the decision support framework (DSF) part of Long-Term Goal 1. The comments...

Interactive decision support in hepatic surgery

PubMed Central

Dugas, Martin; Schauer, Rolf; Volk, Andreas; Rau, Horst

2002-01-01

Background Hepatic surgery is characterized by complicated operations with a significant peri- and postoperative risk for the patient. We developed a web-based, high-granular research database for comprehensive documentation of all relevant variables to evaluate new surgical techniques. Methods To integrate this research system into the clinical setting, we designed an interactive decision support component. The objective is to provide relevant information for the surgeon and the patient to assess preoperatively the risk of a specific surgical procedure. Based on five established predictors of patient outcomes, the risk assessment tool searches for similar cases in the database and aggregates the information to estimate the risk for an individual patient. Results The physician can verify the analysis and exclude manually non-matching cases according to his expertise. The analysis is visualized by means of a Kaplan-Meier plot. To evaluate the decision support component we analyzed data on 165 patients diagnosed with hepatocellular carcinoma (period 1996–2000). The similarity search provides a two-peak distribution indicating there are groups of similar patients and singular cases which are quite different to the average. The results of the risk estimation are consistent with the observed survival data, but must be interpreted with caution because of the limited number of matching reference cases. Conclusion Critical issues for the decision support system are clinical integration, a transparent and reliable knowledge base and user feedback. PMID:12003639

Privacy-preserving clinical decision support system using Gaussian kernel-based classification.

PubMed

Rahulamathavan, Yogachandran; Veluru, Suresh; Phan, Raphael C-W; Chambers, Jonathon A; Rajarajan, Muttukrishnan

2014-01-01

A clinical decision support system forms a critical capability to link health observations with health knowledge to influence choices by clinicians for improved healthcare. Recent trends toward remote outsourcing can be exploited to provide efficient and accurate clinical decision support in healthcare. In this scenario, clinicians can use the health knowledge located in remote servers via the Internet to diagnose their patients. However, the fact that these servers are third party and therefore potentially not fully trusted raises possible privacy concerns. In this paper, we propose a novel privacy-preserving protocol for a clinical decision support system where the patients' data always remain in an encrypted form during the diagnosis process. Hence, the server involved in the diagnosis process is not able to learn any extra knowledge about the patient's data and results. Our experimental results on popular medical datasets from UCI-database demonstrate that the accuracy of the proposed protocol is up to 97.21% and the privacy of patient data is not compromised.

Relational Algebra in Spatial Decision Support Systems Ontologies.

PubMed

Diomidous, Marianna; Chardalias, Kostis; Koutonias, Panagiotis; Magnita, Adrianna; Andrianopoulos, Charalampos; Zimeras, Stelios; Mechili, Enkeleint Aggelos

2017-01-01

Decision Support Systems (DSS) is a powerful tool, for facilitates researchers to choose the correct decision based on their final results. Especially in medical cases where doctors could use these systems, to overcome the problem with the clinical misunderstanding. Based on these systems, queries must be constructed based on the particular questions that doctors must answer. In this work, combination between questions and queries would be presented via relational algebra.

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

GIS-based spatial decision support system for grain logistics management

NASA Astrophysics Data System (ADS)

Zhen, Tong; Ge, Hongyi; Jiang, Yuying; Che, Yi

2010-07-01

Grain logistics is the important component of the social logistics, which can be attributed to frequent circulation and the great quantity. At present time, there is no modern grain logistics distribution management system, and the logistics cost is the high. Geographic Information Systems (GIS) have been widely used for spatial data manipulation and model operations and provide effective decision support through its spatial database management capabilities and cartographic visualization. In the present paper, a spatial decision support system (SDSS) is proposed to support policy makers and to reduce the cost of grain logistics. The system is composed of two major components: grain logistics goods tracking model and vehicle routing problem optimization model and also allows incorporation of data coming from external sources. The proposed system is an effective tool to manage grain logistics in order to increase the speed of grain logistics and reduce the grain circulation cost.

48 CFR 6101.26 - Reconsideration; amendment of decisions; new hearings [Rule 26].

Code of Federal Regulations, 2012 CFR

2012-10-01

... in 6101.27(a) (Rule 27(a)) and the reasons established by the rules of common law or equity... for granting a new hearing. Upon granting a motion for a new hearing, the Board will take additional testimony and, if a decision has been issued, either amend its findings of fact and conclusions or law or...

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

Some Results of Weak Anticipative Concept Applied in Simulation Based Decision Support in Enterprise

NASA Astrophysics Data System (ADS)

Kljajić, Miroljub; Kofjač, Davorin; Kljajić Borštnar, Mirjana; Škraba, Andrej

2010-11-01

The simulation models are used as for decision support and learning in enterprises and in schools. Tree cases of successful applications demonstrate usefulness of weak anticipative information. Job shop scheduling production with makespan criterion presents a real case customized flexible furniture production optimization. The genetic algorithm for job shop scheduling optimization is presented. Simulation based inventory control for products with stochastic lead time and demand describes inventory optimization for products with stochastic lead time and demand. Dynamic programming and fuzzy control algorithms reduce the total cost without producing stock-outs in most cases. Values of decision making information based on simulation were discussed too. All two cases will be discussed from optimization, modeling and learning point of view.

18 CFR 385.704 - Rights of participants before initial decision (Rule 704).

Code of Federal Regulations, 2010 CFR

2010-04-01

... 18 Conservation of Power and Water Resources 1 2010-04-01 2010-04-01 false Rights of participants... PROCEDURE Decisions § 385.704 Rights of participants before initial decision (Rule 704). After testimony is... good cause, deny opportunity for reply or limit the issues which may be addressed in any reply. ...

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.

Computerized Clinical Decision Support: Contributions from 2015

PubMed Central

Bouaud, J.

2016-01-01

Summary Objective To summarize recent research and select the best papers published in 2015 in the field of computerized clinical decision support for the Decision Support section of the IMIA yearbook. Method A literature review was performed by searching two bibliographic databases for papers related to clinical decision support systems (CDSSs) and computerized provider order entry (CPOE) systems. The aim was to identify a list of candidate best papers from the retrieved papers that were then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the IMIA editorial team was finally conducted to conclude in the best paper selection. Results Among the 974 retrieved papers, the entire review process resulted in the selection of four best papers. One paper reports on a CDSS routinely applied in pediatrics for more than 10 years, relying on adaptations of the Arden Syntax. Another paper assessed the acceptability and feasibility of an important CPOE evaluation tool in hospitals outside the US where it was developed. The third paper is a systematic, qualitative review, concerning usability flaws of medication-related alerting functions, providing an important evidence-based, methodological contribution in the domain of CDSS design and development in general. Lastly, the fourth paper describes a study quantifying the effect of a complex, continuous-care, guideline-based CDSS on the correctness and completeness of clinicians’ decisions. Conclusions While there are notable examples of routinely used decision support systems, this 2015 review on CDSSs and CPOE systems still shows that, despite methodological contributions, theoretical frameworks, and prototype developments, these technologies are not yet widely spread (at least with their full functionalities) in routine clinical practice. Further research, testing, evaluation, and training are still needed for these tools to be adopted in clinical practice and, ultimately, illustrate

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.

RuleMonkey: software for stochastic simulation of rule-based models

PubMed Central

2010-01-01

Background The system-level dynamics of many molecular interactions, particularly protein-protein interactions, can be conveniently represented using reaction rules, which can be specified using model-specification languages, such as the BioNetGen language (BNGL). A set of rules implicitly defines a (bio)chemical reaction network. The reaction network implied by a set of rules is often very large, and as a result, generation of the network implied by rules tends to be computationally expensive. Moreover, the cost of many commonly used methods for simulating network dynamics is a function of network size. Together these factors have limited application of the rule-based modeling approach. Recently, several methods for simulating rule-based models have been developed that avoid the expensive step of network generation. The cost of these "network-free" simulation methods is independent of the number of reactions implied by rules. Software implementing such methods is now needed for the simulation and analysis of rule-based models of biochemical systems. Results Here, we present a software tool called RuleMonkey, which implements a network-free method for simulation of rule-based models that is similar to Gillespie's method. The method is suitable for rule-based models that can be encoded in BNGL, including models with rules that have global application conditions, such as rules for intramolecular association reactions. In addition, the method is rejection free, unlike other network-free methods that introduce null events, i.e., steps in the simulation procedure that do not change the state of the reaction system being simulated. We verify that RuleMonkey produces correct simulation results, and we compare its performance against DYNSTOC, another BNGL-compliant tool for network-free simulation of rule-based models. We also compare RuleMonkey against problem-specific codes implementing network-free simulation methods. Conclusions RuleMonkey enables the simulation of

FEMA's Earthquake Incident Journal: A Web-Based Data Integration and Decision Support Tool for Emergency Management

NASA Astrophysics Data System (ADS)

Jones, M.; Pitts, R.

2017-12-01

For emergency managers, government officials, and others who must respond to rapidly changing natural disasters, timely access to detailed information related to affected terrain, population and infrastructure is critical for planning, response and recovery operations. Accessing, analyzing and disseminating such disparate information in near real-time are critical decision support components. However, finding a way to handle a variety of informative yet complex datasets poses a challenge when preparing for and responding to disasters. Here, we discuss the implementation of a web-based data integration and decision support tool for earthquakes developed by the Federal Emergency Management Agency (FEMA) as a solution to some of these challenges. While earthquakes are among the most well- monitored and measured of natural hazards, the spatially broad impacts of shaking, ground deformation, landslides, liquefaction, and even tsunamis, are extremely difficult to quantify without accelerated access to data, modeling, and analytics. This web-based application, deemed the "Earthquake Incident Journal", provides real-time access to authoritative and event-specific data from external (e.g. US Geological Survey, NASA, state and local governments, etc.) and internal (FEMA) data sources. The journal includes a GIS-based model for exposure analytics, allowing FEMA to assess the severity of an event, estimate impacts to structures and population in near real-time, and then apply planning factors to exposure estimates to answer questions such as: What geographic areas are impacted? Will federal support be needed? What resources are needed to support survivors? And which infrastructure elements or essential facilities are threatened? This presentation reviews the development of the Earthquake Incident Journal, detailing the data integration solutions, the methodology behind the GIS-based automated exposure model, and the planning factors as well as other analytical advances that

Evaluation of decision rules for identifying low bone density in postmenopausal African-American women.

PubMed Central

Wallace, Lorraine Silver; Ballard, Joyce E.; Holiday, David; Turner, Lori W.; Keenum, Amy J.; Pearman, Cynthia M.

2004-01-01

OBJECTIVE: While African-American women tend to have greater bone mineral density (BMD) than caucasian women, they are still at risk of developing osteoporosis later in life. Clinical decision rules (i.e., algorithms) have been developed to assist clinicians identify women at greatest risk of low BMD. However, such tools have only been validated in caucasian and Asian populations. Accordingly, the objective of this study was to compare the performance of five clinical decision rules in identifying postmenopausal African-American women at greatest risk for low femoral BMD. METHODOLOGY: One hundred-seventy-four (n=174) postmenopausal African-American women completed a valid and reliable oral questionnaire to assess lifestyle characteristics, and completed height and weight measures. BMD at the femoral neck was measured via dual energy x-ray absorptiometry (DXA). We calculated sensitivity, specificity, positive predictive value, and negative predictive value for identifying African-American women with low BMD (T-Score < or = -2.0 SD) using five clinical decision rules: Age, Body Size, No Estrogen (ABONE), Osteoporosis Risk Assessment Instrument (ORAI), Osteoporosis Self-Assessment Tool (OST), Simple Calculated Osteoporosis Risk Estimation (SCORE), and body weight less than 70 kg. RESULTS: Approximately 30% of African-American women had low BMD, half of whom had osteoporosis (BMD T-Score < or = -2.5 SD). Sensitivity for identifying women with a low BMD (T-Score < or = -2.0 SD) ranged from 65.57-83.61%, while specificity ranged from 53.85-78.85%. Positive predictive values ranged from 80.95-87.91%, while negative predictive values ranged from 48.44-58.33%. CONCLUSION: Our data suggest that the clinical decision rules analyzed in this study have some usefulness for identifying postmenopausal African-American women with low BMD. However, there is a need to establish cut-points for these clinical decision rules in a larger, more diverse sample of African-American women

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…

"Smart Forms" in an Electronic Medical Record: documentation-based clinical decision support to improve disease management.

PubMed

Schnipper, Jeffrey L; Linder, Jeffrey A; Palchuk, Matvey B; Einbinder, Jonathan S; Li, Qi; Postilnik, Anatoly; Middleton, Blackford

2008-01-01

Clinical decision support systems (CDSS) integrated within Electronic Medical Records (EMR) hold the promise of improving healthcare quality. To date the effectiveness of CDSS has been less than expected, especially concerning the ambulatory management of chronic diseases. This is due, in part, to the fact that clinicians do not use CDSS fully. Barriers to clinicians' use of CDSS have included lack of integration into workflow, software usability issues, and relevance of the content to the patient at hand. At Partners HealthCare, we are developing "Smart Forms" to facilitate documentation-based clinical decision support. Rather than being interruptive in nature, the Smart Form enables writing a multi-problem visit note while capturing coded information and providing sophisticated decision support in the form of tailored recommendations for care. The current version of the Smart Form is designed around two chronic diseases: coronary artery disease and diabetes mellitus. The Smart Form has potential to improve the care of patients with both acute and chronic conditions.

Compartmental and Spatial Rule-Based Modeling with Virtual Cell.

PubMed

Blinov, Michael L; Schaff, James C; Vasilescu, Dan; Moraru, Ion I; Bloom, Judy E; Loew, Leslie M

2017-10-03

In rule-based modeling, molecular interactions are systematically specified in the form of reaction rules that serve as generators of reactions. This provides a way to account for all the potential molecular complexes and interactions among multivalent or multistate molecules. Recently, we introduced rule-based modeling into the Virtual Cell (VCell) modeling framework, permitting graphical specification of rules and merger of networks generated automatically (using the BioNetGen modeling engine) with hand-specified reaction networks. VCell provides a number of ordinary differential equation and stochastic numerical solvers for single-compartment simulations of the kinetic systems derived from these networks, and agent-based network-free simulation of the rules. In this work, compartmental and spatial modeling of rule-based models has been implemented within VCell. To enable rule-based deterministic and stochastic spatial simulations and network-free agent-based compartmental simulations, the BioNetGen and NFSim engines were each modified to support compartments. In the new rule-based formalism, every reactant and product pattern and every reaction rule are assigned locations. We also introduce the rule-based concept of molecular anchors. This assures that any species that has a molecule anchored to a predefined compartment will remain in this compartment. Importantly, in addition to formulation of compartmental models, this now permits VCell users to seamlessly connect reaction networks derived from rules to explicit geometries to automatically generate a system of reaction-diffusion equations. These may then be simulated using either the VCell partial differential equations deterministic solvers or the Smoldyn stochastic simulator. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

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.

Geospatial Data Fusion and Multigroup Decision Support for Surface Water Quality Management

NASA Astrophysics Data System (ADS)

Sun, A. Y.; Osidele, O.; Green, R. T.; Xie, H.

2010-12-01

Social networking and social media have gained significant popularity and brought fundamental changes to many facets of our everyday life. With the ever-increasing adoption of GPS-enabled gadgets and technology, location-based content is likely to play a central role in social networking sites. While location-based content is not new to the geoscience community, where geographic information systems (GIS) are extensively used, the delivery of useful geospatial data to targeted user groups for decision support is new. Decision makers and modelers ought to make more effective use of the new web-based tools to expand the scope of environmental awareness education, public outreach, and stakeholder interaction. Environmental decision processes are often rife with uncertainty and controversy, requiring integration of multiple sources of information and compromises between diverse interests. Fusing of multisource, multiscale environmental data for multigroup decision support is a challenging task. Toward this goal, a multigroup decision support platform should strive to achieve transparency, impartiality, and timely synthesis of information. The latter criterion often constitutes a major technical bottleneck to traditional GIS-based media, featuring large file or image sizes and requiring special processing before web deployment. Many tools and design patterns have appeared in recent years to ease the situation somewhat. In this project, we explore the use of Web 2.0 technologies for “pushing” location-based content to multigroups involved in surface water quality management and decision making. In particular, our granular bottom-up approach facilitates effective delivery of information to most relevant user groups. Our location-based content includes in-situ and remotely sensed data disseminated by NASA and other national and local agencies. Our project is demonstrated for managing the total maximum daily load (TMDL) program in the Arroyo Colorado coastal river basin

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

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.

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…

Decision rules for GHB (γ-hydroxybutyric acid) detoxification: a vignette study.

PubMed

Kamal, Rama M; van Iwaarden, Sjacco; Dijkstra, Boukje A G; de Jong, Cornelis A J

2014-02-01

GHB dependent patients can suffer from a severe and sometimes life-threatening withdrawal syndrome. Therefore, most of the patients are treated within inpatient settings. However, some prefers an outpatient approach to treatment. The aim of this study was to develop decision rules for addiction physicians to determine whether an outpatient or inpatient setting should be chosen for a safe GHB detoxification. A prospective vignette study was performed. Forty addiction medicine specialists from various treatment settings and residents of the Addiction Medicine postgraduate Master training were asked to contribute vignettes of GHB dependent patients. A focus group of 15 psychiatrists and addiction medicine specialists was asked to recommend an outpatient or inpatient setting for GHB detoxification treatment per vignette. Finally, five addiction medicine specialists, experts in GHB dependence treatment in the Netherlands, assessed the bio-psychosocial reasons for the choices of the focus group and formulated the recommended criteria. Based on the bio-psychosocial state of twenty vignette patients, addiction physicians and psychiatrists established the criteria and conditions recommended for the indication of an outpatient GHB detoxification. Intensity of addiction (GHB dose ≤32 g/d and frequency of abuse ≤2 h) was stated as the primary criterion in determining the setting as well as the complexity of the psychiatric comorbid disorders. The importance of a stable support system was emphasised. The vignette study resulted in a set of criteria with which addiction medicine specialists can make a weighted decision as to an outpatient or inpatient setting for GHB detoxification. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Rule-based modeling with Virtual Cell

PubMed Central

Schaff, James C.; Vasilescu, Dan; Moraru, Ion I.; Loew, Leslie M.; Blinov, Michael L.

2016-01-01

Summary: Rule-based modeling is invaluable when the number of possible species and reactions in a model become too large to allow convenient manual specification. The popular rule-based software tools BioNetGen and NFSim provide powerful modeling and simulation capabilities at the cost of learning a complex scripting language which is used to specify these models. Here, we introduce a modeling tool that combines new graphical rule-based model specification with existing simulation engines in a seamless way within the familiar Virtual Cell (VCell) modeling environment. A mathematical model can be built integrating explicit reaction networks with reaction rules. In addition to offering a large choice of ODE and stochastic solvers, a model can be simulated using a network free approach through the NFSim simulation engine. Availability and implementation: Available as VCell (versions 6.0 and later) at the Virtual Cell web site (http://vcell.org/). The application installs and runs on all major platforms and does not require registration for use on the user’s computer. Tutorials are available at the Virtual Cell website and Help is provided within the software. Source code is available at Sourceforge. Contact: vcell_support@uchc.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27497444

Oxytocin conditions trait-based rule adherence

PubMed Central

De Dreu, Carsten K.W.

2017-01-01

Abstract Rules, whether in the form of norms, taboos or laws, regulate and coordinate human life. Some rules, however, are arbitrary and adhering to them can be personally costly. Rigidly sticking to such rules can be considered maladaptive. Here, we test whether, at the neurobiological level, (mal)adaptive rule adherence is reduced by oxytocin—a hypothalamic neuropeptide that biases the biobehavioural approach-avoidance system. Participants (N = 139) self-administered oxytocin or placebo intranasally, and reported their need for structure and approach-avoidance sensitivity. Next, participants made binary decisions and were given an arbitrary rule that demanded to forgo financial benefits. Under oxytocin, participants violated the rule more often, especially when they had high need for structure and high approach sensitivity. Possibly, oxytocin dampens the need for a highly structured environment and enables individuals to flexibly trade-off internal desires against external restrictions. Implications for the treatment of clinical disorders marked by maladaptive rule adherence are discussed. PMID:27664999

Personalization of Rule-based Web Services.

PubMed

Choi, Okkyung; Han, Sang Yong

2008-04-04

Nowadays Web users have clearly expressed their wishes to receive personalized services directly. Personalization is the way to tailor services directly to the immediate requirements of the user. However, the current Web Services System does not provide any features supporting this such as consideration of personalization of services and intelligent matchmaking. In this research a flexible, personalized Rule-based Web Services System to address these problems and to enable efficient search, discovery and construction across general Web documents and Semantic Web documents in a Web Services System is proposed. This system utilizes matchmaking among service requesters', service providers' and users' preferences using a Rule-based Search Method, and subsequently ranks search results. A prototype of efficient Web Services search and construction for the suggested system is developed based on the current work.

48 CFR 6101.26 - Reconsideration; amendment of decisions; new hearings [Rule 26].

Code of Federal Regulations, 2010 CFR

2010-10-01

... amend a decision or order for any reason that would justify such action on motion of a party. (d) Effect... for granting a new hearing. Upon granting a motion for a new hearing, the Board will take additional... issue a new decision. (b) Procedure. Any motion under 6101.26 (Rule 26) shall comply with the provisions...

48 CFR 6101.26 - Reconsideration; amendment of decisions; new hearings [Rule 26].

Code of Federal Regulations, 2011 CFR

2011-10-01

... amend a decision or order for any reason that would justify such action on motion of a party. (d) Effect... for granting a new hearing. Upon granting a motion for a new hearing, the Board will take additional... issue a new decision. (b) Procedure. Any motion under 6101.26 (Rule 26) shall comply with the provisions...

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.

Decision support for clinical laboratory capacity planning.

PubMed

van Merode, G G; Hasman, A; Derks, J; Goldschmidt, H M; Schoenmaker, B; Oosten, M

1995-01-01

The design of a decision support system for capacity planning in clinical laboratories is discussed. The DSS supports decisions concerning the following questions: how should the laboratory be divided into job shops (departments/sections), how should staff be assigned to workstations and how should samples be assigned to workstations for testing. The decision support system contains modules for supporting decisions at the overall laboratory level (concerning the division of the laboratory into job shops) and for supporting decisions at the job shop level (assignment of staff to workstations and sample scheduling). Experiments with these modules are described showing both the functionality and the validity.

Dashboard visualizations: Supporting real-time throughput decision-making.

PubMed

Franklin, Amy; Gantela, Swaroop; Shifarraw, Salsawit; Johnson, Todd R; Robinson, David J; King, Brent R; Mehta, Amit M; Maddow, Charles L; Hoot, Nathan R; Nguyen, Vickie; Rubio, Adriana; Zhang, Jiajie; Okafor, Nnaemeka G

2017-07-01

Providing timely and effective care in the emergency department (ED) requires the management of individual patients as well as the flow and demands of the entire department. Strategic changes to work processes, such as adding a flow coordination nurse or a physician in triage, have demonstrated improvements in throughput times. However, such global strategic changes do not address the real-time, often opportunistic workflow decisions of individual clinicians in the ED. We believe that real-time representation of the status of the entire emergency department and each patient within it through information visualizations will better support clinical decision-making in-the-moment and provide for rapid intervention to improve ED flow. This notion is based on previous work where we found that clinicians' workflow decisions were often based on an in-the-moment local perspective, rather than a global perspective. Here, we discuss the challenges of designing and implementing visualizations for ED through a discussion of the development of our prototype Throughput Dashboard and the potential it holds for supporting real-time decision-making. Copyright © 2017. Published by Elsevier Inc.

Bayesian Decision Support

NASA Astrophysics Data System (ADS)

Berliner, M.

2017-12-01

Bayesian statistical decision theory offers a natural framework for decision-policy making in the presence of uncertainty. Key advantages of the approach include efficient incorporation of information and observations. However, in complicated settings it is very difficult, perhaps essentially impossible, to formalize the mathematical inputs needed in the approach. Nevertheless, using the approach as a template is useful for decision support; that is, organizing and communicating our analyses. Bayesian hierarchical modeling is valuable in quantifying and managing uncertainty such cases. I review some aspects of the idea emphasizing statistical model development and use in the context of sea-level rise.

Mobile Clinical Decision Support System for Acid-base Balance Diagnosis and Treatment Recommendation.

PubMed

Mandzuka, Mensur; Begic, Edin; Boskovic, Dusanka; Begic, Zijo; Masic, Izet

2017-06-01

This paper presents mobile application implementing a decision support system for acid-base disorder diagnosis and treatment recommendation. The application was developed using the official integrated development environment for the Android platform (to maximize availability and minimize investments in specialized hardware) called Android Studio. The application identifies disorder, based on the blood gas analysis, evaluates whether the disorder has been compensated, and based on additional input related to electrolyte imbalance, provides recommendations for treatment. The application is a tool in the hands of the user, which provides assistance during acid-base disorders treatment. The application will assist the physician in clinical practice and is focused on the treatment in intensive care.

Development of a SNOMED CT based national medication decision support system.

PubMed

Greibe, Kell

2013-01-01

Physicians often lack the time to familiarize themselves with the details of particular allergies or other drug restrictions. Clinical Decision Support (CDS), based on a structured terminology as SNOMED CT (SCT), can help physicians get an overview, by automatically alerting allergy, interactions and other important information. The centralized CDS platform based on SCT, controls Allergy, Interactions, Risk Situation Drugs and Max Dose restrictions by the help of databases developed for these specific purposes. The CDS will respond to automatic web service requests from the hospital or GP electronic medication system (EMS) during prescription, and return alerts and information. The CDS also contains a Physicians Preference Database where the physicians individually can set which kind of alerts they want to see. The result is clinically useful information physicians can use as a base for a more effective and safer treatment, without developing alert fatigue.

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

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.

A rule-based smart automated fertilization and irrigation systems

NASA Astrophysics Data System (ADS)

Yousif, Musab El-Rashid; Ghafar, Khairuddin; Zahari, Rahimi; Lim, Tiong Hoo

2018-04-01

Smart automation in industries has become very important as it can improve the reliability and efficiency of the systems. The use of smart technologies in agriculture have increased over the year to ensure and control the production of crop and address food security. However, it is important to use proper irrigation systems avoid water wastage and overfeeding of the plant. In this paper, a Smart Rule-based Automated Fertilization and Irrigation System is proposed and evaluated. We propose a rule based decision making algorithm to monitor and control the food supply to the plant and the soil quality. A build-in alert system is also used to update the farmer using a text message. The system is developed and evaluated using a real hardware.

Development and field testing of a decision support tool to facilitate shared decision making in contraceptive counseling.

PubMed

Dehlendorf, Christine; Fitzpatrick, Judith; Steinauer, Jody; Swiader, Lawrence; Grumbach, Kevin; Hall, Cara; Kuppermann, Miriam

2017-07-01

We developed and formatively evaluated a tablet-based decision support tool for use by women prior to a contraceptive counseling visit to help them engage in shared decision making regarding method selection. Drawing upon formative work around women's preferences for contraceptive counseling and conceptual understanding of health care decision making, we iteratively developed a storyboard and then digital prototypes, based on best practices for decision support tool development. Pilot testing using both quantitative and qualitative data and cognitive testing was conducted. We obtained feedback from patient and provider advisory groups throughout the development process. Ninety-six percent of women who used the tool in pilot testing reported that it helped them choose a method, and qualitative interviews indicated acceptability of the tool's content and presentation. Compared to the control group, women who used the tool demonstrated trends toward increased likelihood of complete satisfaction with their method. Participant responses to cognitive testing were used in tool refinement. Our decision support tool appears acceptable to women in the family planning setting. Formative evaluation of the tool supports its utility among patients making contraceptive decisions, which can be further evaluated in a randomized controlled trial. Copyright © 2017 Elsevier B.V. All rights reserved.

Clinic-Based Mobile Health Decision Support to Enhance Adult Epilepsy Self-Management: An Intervention Mapping Approach.

PubMed

Shegog, Ross; Begley, Charles E

2017-01-01

Epilepsy is a neurological disorder involving recurrent seizures. It affects approximately 5 million people in the U.S. To optimize their quality of life people with epilepsy are encouraged to engage in self-management (S-M) behaviors. These include managing their treatment (e.g., adhering to anti-seizure medication and clinical visit schedules), managing their seizures (e.g., responding to seizure episodes), managing their safety (e.g., monitoring and avoiding environmental seizure triggers), and managing their co-morbid conditions (e.g., anxiety, depression). The clinic-based Management Information Decision Support Epilepsy Tool (MINDSET) is a decision-support system founded on theory and empirical evidence. It is designed to increase awareness by adult patients (≥18 years) and their health-care provider regarding the patient's epilepsy S-M behaviors, facilitate communication during the clinic visit to prioritize S-M goals and strategies commensurate with the patient's needs, and increase the patient's self-efficacy to achieve those goals. The purpose of this paper is to describe the application of intervention mapping (IM) to develop, implement, and formatively evaluate the clinic-based MINDSET prototype and in developing implementation and evaluation plans. Deliverables comprised a logic model of the problem (IM Step 1); matrices of program objectives (IM Step 2); a program planning document comprising scope, sequence, theory-based methods, and practical strategies (IM Step 3); a functional MINDSET program prototype (IM Step 4); plans for implementation (IM Step 5); and evaluation (IM Step 6). IM provided a logical and systematic approach to developing and evaluating clinic-based decision support toward epilepsy S-M.

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

Development of Asset Management Decision Support Tools for Power Equipment

NASA Astrophysics Data System (ADS)

Okamoto, Tatsuki; Takahashi, Tsuguhiro

Development of asset management decision support tools become very intensive in order to reduce maintenance cost of power equipment due to the liberalization of power business. This article reviews some aspects of present status of asset management decision support tools development for power equipment based on the papers published in international conferences, domestic conventions, and several journals.

Designing Tools for Supporting User Decision-Making in e-Commerce

NASA Astrophysics Data System (ADS)

Sutcliffe, Alistair; Al-Qaed, Faisal

The paper describes a set of tools designed to support a variety of user decision-making strategies. The tools are complemented by an online advisor so they can be adapted to different domains and users can be guided to adopt appropriate tools for different choices in e-commerce, e.g. purchasing high-value products, exploring product fit to users’ needs, or selecting products which satisfy requirements. The tools range from simple recommenders to decision support by interactive querying and comparison matrices. They were evaluated in a scenario-based experiment which varied the users’ task and motivation, with and without an advisor agent. The results show the tools and advisor were effective in supporting users and agreed with the predictions of ADM (adaptive decision making) theory, on which the design of the tools was based.

Decision Support System Based on Computational Collective Intelligence in Campus Information Systems

NASA Astrophysics Data System (ADS)

Saito, Yoshihito; Matsuo, Tokuro

Education institutions such as universities have a lot of information including book information, equipment administrative information, student information, and several others. The institutions also have multiple information in time series. As collective intelligence in campus, integrating and reusing these preserved information regarding career and taking a class, university can effectively support students' decision making of their getting jobs and subjects choice. Our purpose of support is to increase student's motivation. In this paper, we focus on course record and job information included in students' information, and propose the method to analyze correlation between a pattern of taking class and job lined up. Afterwards, we propose a support system regarding getting a job and taking class by using our proposed method. For a student who has his/her favorite job to get, the system supports his/her decision making of lecture choice by recommending a set of appropriate lecture groups. On another hand, for a student who does not have favorite job to get, the system supports his/her decision making of getting job by presenting appropriate job families related with lecture group in which he/she has ever taken. The contribution of this paper is showing a concrete method to reuse the campus collective information, implementing a system, and user perspectives.

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.

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

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.

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.

Simulation of large-scale rule-based models

PubMed Central

Colvin, Joshua; Monine, Michael I.; Faeder, James R.; Hlavacek, William S.; Von Hoff, Daniel D.; Posner, Richard G.

2009-01-01

Motivation: Interactions of molecules, such as signaling proteins, with multiple binding sites and/or multiple sites of post-translational covalent modification can be modeled using reaction rules. Rules comprehensively, but implicitly, define the individual chemical species and reactions that molecular interactions can potentially generate. Although rules can be automatically processed to define a biochemical reaction network, the network implied by a set of rules is often too large to generate completely or to simulate using conventional procedures. To address this problem, we present DYNSTOC, a general-purpose tool for simulating rule-based models. Results: DYNSTOC implements a null-event algorithm for simulating chemical reactions in a homogenous reaction compartment. The simulation method does not require that a reaction network be specified explicitly in advance, but rather takes advantage of the availability of the reaction rules in a rule-based specification of a network to determine if a randomly selected set of molecular components participates in a reaction during a time step. DYNSTOC reads reaction rules written in the BioNetGen language which is useful for modeling protein–protein interactions involved in signal transduction. The method of DYNSTOC is closely related to that of StochSim. DYNSTOC differs from StochSim by allowing for model specification in terms of BNGL, which extends the range of protein complexes that can be considered in a model. DYNSTOC enables the simulation of rule-based models that cannot be simulated by conventional methods. We demonstrate the ability of DYNSTOC to simulate models accounting for multisite phosphorylation and multivalent binding processes that are characterized by large numbers of reactions. Availability: DYNSTOC is free for non-commercial use. The C source code, supporting documentation and example input files are available at http://public.tgen.org/dynstoc/. Contact: dynstoc@tgen.org Supplementary information

Measurement-based care for refractory depression: a clinical decision support model for clinical research and practice.

PubMed

Trivedi, Madhukar H; Daly, Ella J

2007-05-01

Despite years of antidepressant drug development and patient and provider education, suboptimal medication dosing and duration of exposure resulting in incomplete remission of symptoms remains the norm in the treatment of depression. Additionally, since no one treatment is effective for all patients, optimal implementation focusing on the measurement of symptoms, side effects, and function is essential to determine effective sequential treatment approaches. There is a need for a paradigm shift in how clinical decision making is incorporated into clinical practice and for a move away from the trial-and-error approach that currently determines the "next best" treatment. This paper describes how our experience with the Texas Medication Algorithm Project (TMAP) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial has confirmed the need for easy-to-use clinical support systems to ensure fidelity to guidelines. To further enhance guideline fidelity, we have developed an electronic decision support system that provides critical feedback and guidance at the point of patient care. We believe that a measurement-based care (MBC) approach is essential to any decision support system, allowing physicians to individualize and adapt decisions about patient care based on symptom progress, tolerability of medication, and dose optimization. We also believe that successful integration of sequential algorithms with MBC into real-world clinics will facilitate change that will endure and improve patient outcomes. Although we use major depression to illustrate our approach, the issues addressed are applicable to other chronic psychiatric conditions including comorbid depression and substance use disorder as well as other medical illnesses.

Measurement-Based Care for Refractory Depression: A Clinical Decision Support Model for Clinical Research and Practice

PubMed Central

Trivedi, Madhukar H.; Daly, Ella J.

2009-01-01

Despite years of antidepressant drug development and patient and provider education, suboptimal medication dosing and duration of exposure resulting in incomplete remission of symptoms remains the norm in the treatment of depression. Additionally, since no one treatment is effective for all patients, optimal implementation focusing on the measurement of symptoms, side effects, and function is essential to determine effective sequential treatment approaches. There is a need for a paradigm shift in how clinical decision making is incorporated into clinical practice and for a move away from the trial-and-error approach that currently determines the “next best” treatment. This paper describes how our experience with the Texas Medication Algorithm Project (TMAP) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial has confirmed the need for easy-to-use clinical support systems to ensure fidelity to guidelines. To further enhance guideline fidelity, we have developed an electronic decision support system that provides critical feedback and guidance at the point of patient care. We believe that a measurement-based care (MBC) approach is essential to any decision support system, allowing physicians to individualize and adapt decisions about patient care based on symptom progress, tolerability of medication, and dose optimization. We also believe that successful integration of sequential algorithms with MBC into real-world clinics will facilitate change that will endure and improve patient outcomes. Although we use major depression to illustrate our approach, the issues addressed are applicable to other chronic psychiatric conditions including comorbid depression and substance use disorder as well as other medical illnesses. PMID:17320312

Oxytocin conditions trait-based rule adherence.

PubMed

Gross, Jörg; De Dreu, Carsten K W

2017-03-01

Rules, whether in the form of norms, taboos or laws, regulate and coordinate human life. Some rules, however, are arbitrary and adhering to them can be personally costly. Rigidly sticking to such rules can be considered maladaptive. Here, we test whether, at the neurobiological level, (mal)adaptive rule adherence is reduced by oxytocin-a hypothalamic neuropeptide that biases the biobehavioural approach-avoidance system. Participants (N = 139) self-administered oxytocin or placebo intranasally, and reported their need for structure and approach-avoidance sensitivity. Next, participants made binary decisions and were given an arbitrary rule that demanded to forgo financial benefits. Under oxytocin, participants violated the rule more often, especially when they had high need for structure and high approach sensitivity. Possibly, oxytocin dampens the need for a highly structured environment and enables individuals to flexibly trade-off internal desires against external restrictions. Implications for the treatment of clinical disorders marked by maladaptive rule adherence are discussed. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Decision-support systems for natural-hazards and land-management issues

USGS Publications Warehouse

Dinitz, Laura; Forney, William; Byrd, Kristin

2012-01-01

Scientists at the USGS Western Geographic Science Center are developing decision-support systems (DSSs) for natural-hazards and land-management issues. DSSs are interactive computer-based tools that use data and models to help identify and solve problems. These systems can provide crucial support to policymakers, planners, and communities for making better decisions about long-term natural hazards mitigation and land-use planning.

A decision-supported outpatient practice system.

PubMed Central

Barrows, R. C.; Allen, B. A.; Smith, K. C.; Arni, V. V.; Sherman, E.

1996-01-01

We describe a Decision-supported Outpatient Practice (DOP) system developed and now in use at the Columbia-Presbyterian Medical Center. DOP is an automated ambulatory medical record system that integrates in-patient and ambulatory care data, and incorporates active and passive decision support mechanisms with a view towards improving the quality of primary care. Active decision support occurs in the form of event-driven reminders created within a remote clinical information system with its central data repository and decision support system (DSS). Novel features of DOP include patient specific health maintenance task lists calculated by the remote DSS. uses of a semantically structured controlled medical vocabulary to support clinical results review and provider data entry, and exploitation of an underlying ambulatory data model that provides for an explicit record of evolution of insight regarding patient management. Benefits, challenges, and plans are discussed. PMID:8947774

The conceptual foundation of environmental decision support.

PubMed

Reichert, Peter; Langhans, Simone D; Lienert, Judit; Schuwirth, Nele

2015-05-01

Environmental decision support intends to use the best available scientific knowledge to help decision makers find and evaluate management alternatives. The goal of this process is to achieve the best fulfillment of societal objectives. This requires a careful analysis of (i) how scientific knowledge can be represented and quantified, (ii) how societal preferences can be described and elicited, and (iii) how these concepts can best be used to support communication with authorities, politicians, and the public in environmental management. The goal of this paper is to discuss key requirements for a conceptual framework to address these issues and to suggest how these can best be met. We argue that a combination of probability theory and scenario planning with multi-attribute utility theory fulfills these requirements, and discuss adaptations and extensions of these theories to improve their application for supporting environmental decision making. With respect to (i) we suggest the use of intersubjective probabilities, if required extended to imprecise probabilities, to describe the current state of scientific knowledge. To address (ii), we emphasize the importance of value functions, in addition to utilities, to support decisions under risk. We discuss the need for testing "non-standard" value aggregation techniques, the usefulness of flexibility of value functions regarding attribute data availability, the elicitation of value functions for sub-objectives from experts, and the consideration of uncertainty in value and utility elicitation. With respect to (iii), we outline a well-structured procedure for transparent environmental decision support that is based on a clear separation of scientific prediction and societal valuation. We illustrate aspects of the suggested methodology by its application to river management in general and with a small, didactical case study on spatial river rehabilitation prioritization. Copyright © 2015 The Authors. Published by

Temporal reasoning for decision support in medicine.

PubMed

Augusto, Juan Carlos

2005-01-01

Handling time-related concepts is essential in medicine. During diagnosis it can make a substantial difference to know the temporal order in which some symptoms occurred or for how long they lasted. During prognosis the potential evolutions of a disease are conceived as a description of events unfolding in time. In therapy planning the different steps of treatment must be applied in a precise order, with a given frequency and for a certain span of time in order to be effective. This article offers a survey on the use of temporal reasoning for decision support-related tasks in medicine. Key publications of the area, mainly circumscribed to the latest two decades, are reviewed and classified according to three important stages of patient treatment requiring decision support: diagnosis, prognosis and therapy planning/management. Other complementary publications, like those on time-centered information storage and retrieval, are also considered as they provide valuable support to the above mentioned three stages. Key areas are highlighted and used to organize the latest contributions. The survey of previous research is followed by an analysis of what can still be improved and what is needed to make the next generation of decision support systems for medicine more effective. It can be observed that although the area has been considerably developed, there are still areas where more research is needed to make time-based systems of widespread use in decision support-related areas of medicine. Several suggestions for further exploration are proposed as a result of the survey.

Mobile Clinical Decision Support System for Acid-base Balance Diagnosis and Treatment Recommendation

PubMed Central

Mandzuka, Mensur; Begic, Edin; Boskovic, Dusanka; Begic, Zijo; Masic, Izet

2017-01-01

Introduction: This paper presents mobile application implementing a decision support system for acid-base disorder diagnosis and treatment recommendation. Material and methods: The application was developed using the official integrated development environment for the Android platform (to maximize availability and minimize investments in specialized hardware) called Android Studio. Results: The application identifies disorder, based on the blood gas analysis, evaluates whether the disorder has been compensated, and based on additional input related to electrolyte imbalance, provides recommendations for treatment. Conclusion: The application is a tool in the hands of the user, which provides assistance during acid-base disorders treatment. The application will assist the physician in clinical practice and is focused on the treatment in intensive care. PMID:28883678

“Smart Forms” in an Electronic Medical Record: Documentation-based Clinical Decision Support to Improve Disease Management

PubMed Central

Schnipper, Jeffrey L.; Linder, Jeffrey A.; Palchuk, Matvey B.; Einbinder, Jonathan S.; Li, Qi; Postilnik, Anatoly; Middleton, Blackford

2008-01-01

Clinical decision support systems (CDSS) integrated within Electronic Medical Records (EMR) hold the promise of improving healthcare quality. To date the effectiveness of CDSS has been less than expected, especially concerning the ambulatory management of chronic diseases. This is due, in part, to the fact that clinicians do not use CDSS fully. Barriers to clinicians' use of CDSS have included lack of integration into workflow, software usability issues, and relevance of the content to the patient at hand. At Partners HealthCare, we are developing “Smart Forms” to facilitate documentation-based clinical decision support. Rather than being interruptive in nature, the Smart Form enables writing a multi-problem visit note while capturing coded information and providing sophisticated decision support in the form of tailored recommendations for care. The current version of the Smart Form is designed around two chronic diseases: coronary artery disease and diabetes mellitus. The Smart Form has potential to improve the care of patients with both acute and chronic conditions. PMID:18436911

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.

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.

Understanding Decision-Making, Communication Rules, and Communication Satisfaction as Culture: Implications for Organizational Effectiveness.

ERIC Educational Resources Information Center

Shockley-Zalabak, Pamela

A study of decision making processes and communication rules, in a corporate setting undergoing change as a result of organizational ineffectiveness, examined whether (1) decisions about formal communication reporting systems were linked to management assumptions about technical creativity/effectiveness, (2) assumptions about…

Parameter selection for and implementation of a web-based decision-support tool to predict extubation outcome in premature infants.

PubMed

Mueller, Martina; Wagner, Carol L; Annibale, David J; Knapp, Rebecca G; Hulsey, Thomas C; Almeida, Jonas S

2006-03-01

Approximately 30% of intubated preterm infants with respiratory distress syndrome (RDS) will fail attempted extubation, requiring reintubation and mechanical ventilation. Although ventilator technology and monitoring of premature infants have improved over time, optimal extubation remains challenging. Furthermore, extubation decisions for premature infants require complex informational processing, techniques implicitly learned through clinical practice. Computer-aided decision-support tools would benefit inexperienced clinicians, especially during peak neonatal intensive care unit (NICU) census. A five-step procedure was developed to identify predictive variables. Clinical expert (CE) thought processes comprised one model. Variables from that model were used to develop two mathematical models for the decision-support tool: an artificial neural network (ANN) and a multivariate logistic regression model (MLR). The ranking of the variables in the three models was compared using the Wilcoxon Signed Rank Test. The best performing model was used in a web-based decision-support tool with a user interface implemented in Hypertext Markup Language (HTML) and the mathematical model employing the ANN. CEs identified 51 potentially predictive variables for extubation decisions for an infant on mechanical ventilation. Comparisons of the three models showed a significant difference between the ANN and the CE (p = 0.0006). Of the original 51 potentially predictive variables, the 13 most predictive variables were used to develop an ANN as a web-based decision-tool. The ANN processes user-provided data and returns the prediction 0-1 score and a novelty index. The user then selects the most appropriate threshold for categorizing the prediction as a success or failure. Furthermore, the novelty index, indicating the similarity of the test case to the training case, allows the user to assess the confidence level of the prediction with regard to how much the new data differ from the data

Decision Support for Ecosystem Management (Chapter 28)

Treesearch

Keith Reynolds; Jennifer Bjork; Rachel Riemann Hershey; Dan Schmoldt; John Payne; Susan King; Lee DeCola; Mark J. Twery; Pat Cunningham

1999-01-01

This chapter presents a management perspective on decision support for ecosystem management.The Introduction provides a brief historical overview of decision support technology as it has been used in natural resource management, discusses the role of decision support in ecosystem management as we see it, and summarizes the current state of the technology.

Rule-Based Event Processing and Reaction Rules

NASA Astrophysics Data System (ADS)

Paschke, Adrian; Kozlenkov, Alexander

Reaction rules and event processing technologies play a key role in making business and IT / Internet infrastructures more agile and active. While event processing is concerned with detecting events from large event clouds or streams in almost real-time, reaction rules are concerned with the invocation of actions in response to events and actionable situations. They state the conditions under which actions must be taken. In the last decades various reaction rule and event processing approaches have been developed, which for the most part have been advanced separately. In this paper we survey reaction rule approaches and rule-based event processing systems and languages.

Evolving rule-based systems in two medical domains using genetic programming.

PubMed

Tsakonas, Athanasios; Dounias, Georgios; Jantzen, Jan; Axer, Hubertus; Bjerregaard, Beth; von Keyserlingk, Diedrich Graf

2004-11-01

To demonstrate and compare the application of different genetic programming (GP) based intelligent methodologies for the construction of rule-based systems in two medical domains: the diagnosis of aphasia's subtypes and the classification of pap-smear examinations. Past data representing (a) successful diagnosis of aphasia's subtypes from collaborating medical experts through a free interview per patient, and (b) correctly classified smears (images of cells) by cyto-technologists, previously stained using the Papanicolaou method. Initially a hybrid approach is proposed, which combines standard genetic programming and heuristic hierarchical crisp rule-base construction. Then, genetic programming for the production of crisp rule based systems is attempted. Finally, another hybrid intelligent model is composed by a grammar driven genetic programming system for the generation of fuzzy rule-based systems. Results denote the effectiveness of the proposed systems, while they are also compared for their efficiency, accuracy and comprehensibility, to those of an inductive machine learning approach as well as to those of a standard genetic programming symbolic expression approach. The proposed GP-based intelligent methodologies are able to produce accurate and comprehensible results for medical experts performing competitive to other intelligent approaches. The aim of the authors was the production of accurate but also sensible decision rules that could potentially help medical doctors to extract conclusions, even at the expense of a higher classification score achievement.

A Web-Based Decision Support System for Assessing Regional Water-Quality Conditions and Management Actions

NASA Astrophysics Data System (ADS)

Booth, N. L.; Everman, E.; Kuo, I.; Sprague, L.; Murphy, L.

2011-12-01

A new web-based decision support system has been developed as part of the U.S. Geological Survey (USGS) National Water Quality Assessment Program's (NAWQA) effort to provide ready access to Spatially Referenced Regressions On Watershed attributes (SPARROW) results of stream water-quality conditions and to offer sophisticated scenario testing capabilities for research and water-quality planning via an intuitive graphical user interface with a map-based display. The SPARROW Decision Support System (DSS) is delivered through a web browser over an Internet connection, making it widely accessible to the public in a format that allows users to easily display water-quality conditions, distribution of nutrient sources, nutrient delivery to downstream waterbodies, and simulations of altered nutrient inputs including atmospheric and agricultural sources. The DSS offers other features for analysis including various background map layers, model output exports, and the ability to save and share prediction scenarios. SPARROW models currently supported by the DSS are based on the modified digital versions of the 1:500,000-scale River Reach File (RF1) and 1:100,000-scale National Hydrography Dataset (medium-resolution, NHDPlus) stream networks. The underlying modeling framework and server infrastructure illustrate innovations in the information technology and geosciences fields for delivering SPARROW model predictions over the web by performing intensive model computations and map visualizations of the predicted conditions within the stream network.

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.

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

Rules based process window OPC

NASA Astrophysics Data System (ADS)

O'Brien, Sean; Soper, Robert; Best, Shane; Mason, Mark

2008-03-01

As a preliminary step towards Model-Based Process Window OPC we have analyzed the impact of correcting post-OPC layouts using rules based methods. Image processing on the Brion Tachyon was used to identify sites where the OPC model/recipe failed to generate an acceptable solution. A set of rules for 65nm active and poly were generated by classifying these failure sites. The rules were based upon segment runlengths, figure spaces, and adjacent figure widths. 2.1 million sites for active were corrected in a small chip (comparing the pre and post rules based operations), and 59 million were found at poly. Tachyon analysis of the final reticle layout found weak margin sites distinct from those sites repaired by rules-based corrections. For the active layer more than 75% of the sites corrected by rules would have printed without a defect indicating that most rulesbased cleanups degrade the lithographic pattern. Some sites were missed by the rules based cleanups due to either bugs in the DRC software or gaps in the rules table. In the end dramatic changes to the reticle prevented catastrophic lithography errors, but this method is far too blunt. A more subtle model-based procedure is needed changing only those sites which have unsatisfactory lithographic margin.

Exploration of SWRL Rule Bases through Visualization, Paraphrasing, and Categorization of Rules

NASA Astrophysics Data System (ADS)

Hassanpour, Saeed; O'Connor, Martin J.; Das, Amar K.

Rule bases are increasingly being used as repositories of knowledge content on the Semantic Web. As the size and complexity of these rule bases increases, developers and end users need methods of rule abstraction to facilitate rule management. In this paper, we describe a rule abstraction method for Semantic Web Rule Language (SWRL) rules that is based on lexical analysis and a set of heuristics. Our method results in a tree data structure that we exploit in creating techniques to visualize, paraphrase, and categorize SWRL rules. We evaluate our approach by applying it to several biomedical ontologies that contain SWRL rules, and show how the results reveal rule patterns within the rule base. We have implemented our method as a plug-in tool for Protégé-OWL, the most widely used ontology modeling software for the Semantic Web. Our tool can allow users to rapidly explore content and patterns in SWRL rule bases, enabling their acquisition and management.

Development of the Supported Decision Making Inventory System.

PubMed

Shogren, Karrie A; Wehmeyer, Michael L; Uyanik, Hatice; Heidrich, Megan

2017-12-01

Supported decision making has received increased attention as an alternative to guardianship and a means to enable people with intellectual and developmental disabilities to exercise their right to legal capacity. Assessments are needed that can used by people with disabilities and their systems of supports to identify and plan for needed supports to enable decision making. This article describes the steps taken to develop such an assessment tool, the Supported Decision Making Inventory System (SDMIS), and initial feedback received from self-advocates with intellectual disability. The three sections of the SDMIS (Supported Decision Making Personal Factors Inventory, Supported Decision Making Environmental Demands Inventory, and Decision Making Autonomy Inventory) are described and implications for future research, policy, and practice are discussed.

Building Better Decision-Support by Using Knowledge Discovery.

ERIC Educational Resources Information Center

Jurisica, Igor

2000-01-01

Discusses knowledge-based decision-support systems that use artificial intelligence approaches. Addresses the issue of how to create an effective case-based reasoning system for complex and evolving domains, focusing on automated methods for system optimization and domain knowledge evolution that can supplement knowledge acquired from domain…

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

Designing Real-time Decision Support for Trauma Resuscitations

PubMed Central

Yadav, Kabir; Chamberlain, James M.; Lewis, Vicki R.; Abts, Natalie; Chawla, Shawn; Hernandez, Angie; Johnson, Justin; Tuveson, Genevieve; Burd, Randall S.

2016-01-01

Background Use of electronic clinical decision support (eCDS) has been recommended to improve implementation of clinical decision rules. Many eCDS tools, however, are designed and implemented without taking into account the context in which clinical work is performed. Implementation of the pediatric traumatic brain injury (TBI) clinical decision rule at one Level I pediatric emergency department includes an electronic questionnaire triggered when ordering a head computed tomography using computerized physician order entry (CPOE). Providers use this CPOE tool in less than 20% of trauma resuscitation cases. A human factors engineering approach could identify the implementation barriers that are limiting the use of this tool. Objectives The objective was to design a pediatric TBI eCDS tool for trauma resuscitation using a human factors approach. The hypothesis was that clinical experts will rate a usability-enhanced eCDS tool better than the existing CPOE tool for user interface design and suitability for clinical use. Methods This mixed-methods study followed usability evaluation principles. Pediatric emergency physicians were surveyed to identify barriers to using the existing eCDS tool. Using standard trauma resuscitation protocols, a hierarchical task analysis of pediatric TBI evaluation was developed. Five clinical experts, all board-certified pediatric emergency medicine faculty members, then iteratively modified the hierarchical task analysis until reaching consensus. The software team developed a prototype eCDS display using the hierarchical task analysis. Three human factors engineers provided feedback on the prototype through a heuristic evaluation, and the software team refined the eCDS tool using a rapid prototyping process. The eCDS tool then underwent iterative usability evaluations by the five clinical experts using video review of 50 trauma resuscitation cases. A final eCDS tool was created based on their feedback, with content analysis of the

Technology Infusion Challenges from a Decision Support Perspective

NASA Technical Reports Server (NTRS)

Adumitroaie, V.; Weisbin, C. R.

2009-01-01

In a restricted science budget environment and increasingly numerous required technology developments, the technology investment decisions within NASA are objectively more and more difficult to make such that the end results are satisfying the technical objectives and all the organizational constraints. Under these conditions it is rationally desirable to build an investment portfolio, which has the highest possible technology infusion rate. Arguably the path to infusion is subject to many influencing factors, but here only the challenges associated with the very initial stages are addressed: defining the needs and the subsequent investment decision-support process. It is conceivable that decision consistency and possibly its quality suffer when the decision-making process has limited or no traceability. This paper presents a structured decision-support framework aiming to provide traceable, auditable, infusion- driven recommendations towards a selection process in which these recommendations are used as reference points in further discussions among stakeholders. In this framework addressing well-defined requirements, different measures of success can be defined based on traceability to specific selection criteria. As a direct result, even by using simplified decision models the likelihood of infusion can be probed and consequently improved.

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.

Rule-based simulation models

NASA Technical Reports Server (NTRS)

Nieten, Joseph L.; Seraphine, Kathleen M.

1991-01-01

Procedural modeling systems, rule based modeling systems, and a method for converting a procedural model to a rule based model are described. Simulation models are used to represent real time engineering systems. A real time system can be represented by a set of equations or functions connected so that they perform in the same manner as the actual system. Most modeling system languages are based on FORTRAN or some other procedural language. Therefore, they must be enhanced with a reaction capability. Rule based systems are reactive by definition. Once the engineering system has been decomposed into a set of calculations using only basic algebraic unary operations, a knowledge network of calculations and functions can be constructed. The knowledge network required by a rule based system can be generated by a knowledge acquisition tool or a source level compiler. The compiler would take an existing model source file, a syntax template, and a symbol table and generate the knowledge network. Thus, existing procedural models can be translated and executed by a rule based system. Neural models can be provide the high capacity data manipulation required by the most complex real time models.

The politics of end-of-life decision-making: computerised decision-support tools, physicians' jurisdiction and morality.

PubMed

Jennings, Beth

2006-04-01

With the increasing corporate and governmental rationalisation of medical care, the mandate of efficiency has caused many to fear that concern for the individual patient will be replaced with impersonal, rule-governed allocation of medical resources. Largely ignored is the role of moral principles in medical decision-making. This analysis comes from an ethnographic study conducted from 1999-2001 in three US Intensive Care Units, two of which were using the computerised decision-support tool, APACHE III (Acute Physiological and Chronic Health Evaluation III), which notably predicts the probability that a patient will die. It was found that the use of APACHE presents a paradox regarding concern for the individual patient. To maintain jurisdiction over the care of patients, physicians share the data with the payers and regulators of care to prove they are using resources effectively and efficiently, yet they use the system in conjunction with moral principles to justify treating each patient as unique. Thus, concern for the individual patient is not lessened with the use of this system. However, physicians do not share the data with patients or surrogate decision-makers because they fear they will be viewed as more interested in profits than patients.

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

77 FR 52977 - Regulatory Capital Rules: Advanced Approaches Risk-Based Capital Rule; Market Risk Capital Rule

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-30

... Corporation 12 CFR Parts 324, 325 Regulatory Capital Rules: Advanced Approaches Risk-Based Capital Rule... 325 RIN 3064-AD97 Regulatory Capital Rules: Advanced Approaches Risk-Based Capital Rule; Market Risk... the agencies' current capital rules. In this NPR (Advanced Approaches and Market Risk NPR) the...

Pheromone-based decision support tools for management of Halyomorpha halys in apple orchards: development of a trap-based treatment threshold

USDA-ARS?s Scientific Manuscript database

The invasive brown marmorated stink bug, Halyomorpha halys (Stål), has become a serious pest in mid-Atlantic apple orchards. Because no decision support tools exist for H. halys management, calendar-based insecticide applications have been the only successful technique for mitigating H. halys injur...

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

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.

Patient-oriented Computerized Clinical Guidelines for Mobile Decision Support in Gestational Diabetes.

PubMed

García-Sáez, Gema; Rigla, Mercedes; Martínez-Sarriegui, Iñaki; Shalom, Erez; Peleg, Mor; Broens, Tom; Pons, Belén; Caballero-Ruíz, Estefanía; Gómez, Enrique J; Hernando, M Elena

2014-03-01

The risks associated with gestational diabetes (GD) can be reduced with an active treatment able to improve glycemic control. Advances in mobile health can provide new patient-centric models for GD to create personalized health care services, increase patient independence and improve patients' self-management capabilities, and potentially improve their treatment compliance. In these models, decision-support functions play an essential role. The telemedicine system MobiGuide provides personalized medical decision support for GD patients that is based on computerized clinical guidelines and adapted to a mobile environment. The patient's access to the system is supported by a smartphone-based application that enhances the efficiency and ease of use of the system. We formalized the GD guideline into a computer-interpretable guideline (CIG). We identified several workflows that provide decision-support functionalities to patients and 4 types of personalized advice to be delivered through a mobile application at home, which is a preliminary step to providing decision-support tools in a telemedicine system: (1) therapy, to help patients to comply with medical prescriptions; (2) monitoring, to help patients to comply with monitoring instructions; (3) clinical assessment, to inform patients about their health conditions; and (4) upcoming events, to deal with patients' personal context or special events. The whole process to specify patient-oriented decision support functionalities ensures that it is based on the knowledge contained in the GD clinical guideline and thus follows evidence-based recommendations but at the same time is patient-oriented, which could enhance clinical outcomes and patients' acceptance of the whole system. © 2014 Diabetes Technology Society.

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.

A risk-based decision support framework for selection of appropriate safety measure system for underground coal mines.

PubMed

Samantra, Chitrasen; Datta, Saurav; Mahapatra, Siba Sankar

2017-03-01

In the context of underground coal mining industry, the increased economic issues regarding implementation of additional safety measure systems, along with growing public awareness to ensure high level of workers safety, have put great pressure on the managers towards finding the best solution to ensure safe as well as economically viable alternative selection. Risk-based decision support system plays an important role in finding such solutions amongst candidate alternatives with respect to multiple decision criteria. Therefore, in this paper, a unified risk-based decision-making methodology has been proposed for selecting an appropriate safety measure system in relation to an underground coal mining industry with respect to multiple risk criteria such as financial risk, operating risk, and maintenance risk. The proposed methodology uses interval-valued fuzzy set theory for modelling vagueness and subjectivity in the estimates of fuzzy risk ratings for making appropriate decision. The methodology is based on the aggregative fuzzy risk analysis and multi-criteria decision making. The selection decisions are made within the context of understanding the total integrated risk that is likely to incur while adapting the particular safety system alternative. Effectiveness of the proposed methodology has been validated through a real-time case study. The result in the context of final priority ranking is seemed fairly consistent.

An Internationally Consented Standard for Nursing Process-Clinical Decision Support Systems in Electronic Health Records.

PubMed

Müller-Staub, Maria; de Graaf-Waar, Helen; Paans, Wolter

2016-11-01

Nurses are accountable to apply the nursing process, which is key for patient care: It is a problem-solving process providing the structure for care plans and documentation. The state-of-the art nursing process is based on classifications that contain standardized concepts, and therefore, it is named Advanced Nursing Process. It contains valid assessments, nursing diagnoses, interventions, and nursing-sensitive patient outcomes. Electronic decision support systems can assist nurses to apply the Advanced Nursing Process. However, nursing decision support systems are missing, and no "gold standard" is available. The study aim is to develop a valid Nursing Process-Clinical Decision Support System Standard to guide future developments of clinical decision support systems. In a multistep approach, a Nursing Process-Clinical Decision Support System Standard with 28 criteria was developed. After pilot testing (N = 29 nurses), the criteria were reduced to 25. The Nursing Process-Clinical Decision Support System Standard was then presented to eight internationally known experts, who performed qualitative interviews according to Mayring. Fourteen categories demonstrate expert consensus on the Nursing Process-Clinical Decision Support System Standard and its content validity. All experts agreed the Advanced Nursing Process should be the centerpiece for the Nursing Process-Clinical Decision Support System and should suggest research-based, predefined nursing diagnoses and correct linkages between diagnoses, evidence-based interventions, and patient outcomes.

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.

Home care decision support using an Arden engine--merging smart home and vital signs data.

PubMed

Marschollek, Michael; Bott, Oliver J; Wolf, Klaus-H; Gietzelt, Matthias; Plischke, Maik; Madiesh, Moaaz; Song, Bianying; Haux, Reinhold

2009-01-01

The demographic change with a rising proportion of very old people and diminishing resources leads to an intensification of the use of telemedicine and home care concepts. To provide individualized decision support, data from different sources, e.g. vital signs sensors and home environmental sensors, need to be combined and analyzed together. Furthermore, a standardized decision support approach is necessary. The aim of our research work is to present a laboratory prototype home care architecture that integrates data from different sources and uses a decision support system based on the HL7 standard Arden Syntax for Medical Logical Modules. Data from environmental sensors connected to a home bus system are stored in a data base along with data from wireless medical sensors. All data are analyzed using an Arden engine with the medical knowledge represented in Medical Logic Modules. Multi-modal data from four different sensors in the home environment are stored in a single data base and are analyzed using an HL7 standard conformant decision support system. Individualized home care decision support must be based on all data available, including context data from smart home systems and medical data from electronic health records. Our prototype implementation shows the feasibility of using an Arden engine for decision support in a home setting. Our future work will include the utilization of medical background knowledge for individualized decision support, as there is no one-size-fits-all knowledge base in medicine.

Data mining for multiagent rules, strategies, and fuzzy decision tree structure

NASA Astrophysics Data System (ADS)

Smith, James F., III; Rhyne, Robert D., II; Fisher, Kristin

2002-03-01

A fuzzy logic based resource manager (RM) has been developed that automatically allocates electronic attack resources in real-time over many dissimilar platforms. Two different data mining algorithms have been developed to determine rules, strategies, and fuzzy decision tree structure. The first data mining algorithm uses a genetic algorithm as a data mining function and is called from an electronic game. The game allows a human expert to play against the resource manager in a simulated battlespace with each of the defending platforms being exclusively directed by the fuzzy resource manager and the attacking platforms being controlled by the human expert or operating autonomously under their own logic. This approach automates the data mining problem. The game automatically creates a database reflecting the domain expert's knowledge. It calls a data mining function, a genetic algorithm, for data mining of the database as required and allows easy evaluation of the information mined in the second step. The criterion for re- optimization is discussed as well as experimental results. Then a second data mining algorithm that uses a genetic program as a data mining function is introduced to automatically discover fuzzy decision tree structures. Finally, a fuzzy decision tree generated through this process is discussed.

Combination Rules for Morse-Based van der Waals Force Fields.

PubMed

Yang, Li; Sun, Lei; Deng, Wei-Qiao

2018-02-15

In traditional force fields (FFs), van der Waals interactions have been usually described by the Lennard-Jones potentials. Conventional combination rules for the parameters of van der Waals (VDW) cross-termed interactions were developed for the Lennard-Jones based FFs. Here, we report that the Morse potentials were a better function to describe VDW interactions calculated by highly precise quantum mechanics methods. A new set of combination rules was developed for Morse-based FFs, in which VDW interactions were described by Morse potentials. The new set of combination rules has been verified by comparing the second virial coefficients of 11 noble gas mixtures. For all of the mixed binaries considered in this work, the combination rules work very well and are superior to all three other existing sets of combination rules reported in the literature. We further used the Morse-based FF by using the combination rules to simulate the adsorption isotherms of CH 4 at 298 K in four covalent-organic frameworks (COFs). The overall agreement is great, which supports the further applications of this new set of combination rules in more realistic simulation systems.