A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications

Amirkhani, Abdollah; Papageorgiou, Elpiniki I.; Mohseni, Akram; Mosavi, Mohammad Reza

doi:10.1016/j.cmpb.2017.02.021

Cited by 125 publications

(59 citation statements)

References 73 publications

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“…Our approach to manage complex patients is based on producing a series of interactive pathways where any combination can be selected, the conflict between them automatically identified, and suggestions for resolution generated to inform the subsequent decisions. There are, however, other approaches and recently the use of fuzzy logic has gained in popularity 46. This also offers the opportunity to integrate complex human knowledge with computer-aided techniques through tools such as CCTool 47.…”

Section: Discussionmentioning

confidence: 99%

Automated conflict resolution between multiple clinical pathways: a technology report

Litchfield

Turner

Backman

et al. 2018

BMJ Health Care Inform

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BackgroundThe number of people in the UK with three or more long-term conditions continues to grow and the management of patients with co-morbidities is complex. In treating patients with multimorbidities, a fundamental problem is understanding and detecting points of conflict between different guidelines which to date has relied on individual clinicians collating disparate information.ObjectiveWe will develop a framework for modelling a diverse set of care pathways, and investigate how conflicts can be detected and resolved automatically. We will use this knowledge to develop a software tool for use by clinicians that can map guidelines, highlight root causes of conflict between these guidelines and suggest ways they might be resolved.MethodOur work consists of three phases. First, we will accurately model clinical pathways for six of the most common chronic diseases; second, we will automatically identify and detect sources of conflict across the pathways and how they might be resolved. Third, we will present a case study to prove the validity of our approach using a team of clinicians to detect and resolve the conflicts in the treatment of a fictional patient with multiple common morbidities and compare their findings and recommendations with those derived automatically using our novel software.DiscussionThis paper describes the development of an important software-based method for identifying a conflict between clinical guidelines. Our findings will support clinicians treating patients with multimorbidity in both primary and secondary care settings.

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Section: Discussionmentioning

confidence: 99%

Automated conflict resolution between multiple clinical pathways: a technology report

Litchfield

Turner

Backman

et al. 2018

BMJ Health Care Inform

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“…The design of ESs using a conceptual approach has been reported in the past (Amirkhani et al, ; Clancey, ; Swaffield & Knight, ; Zarikas, Papageorgiou, & Regner, ). Some of the most important issues that have arisen in this article were already discussed in the literature, including the development of structured reusable libraries.…”

Section: Discussionmentioning

confidence: 99%

“…The KA methods in ES are mainly aimed at the development of algorithms for the selection of rules and inferences. The task of selecting variables prior to submission to the “Inference Engine” was considered in several ESs proposed in Akinnuwesi et al (), Akinnuwesi et al (), and Amirkhani, Papageorgiou, Mohseni, and Mosavi (), Lee and Lee (). The characteristics of the approaches used in these studies are described above.…”

Section: Related Workmentioning

confidence: 99%

Knowledge discovery using maximization of the spread of influence in an expert system

Tselykh

Vasilev

et al. 2018

Expert Systems

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The problem of knowledge acquisition (KA) and the automation of this process is a bottleneck of expert systems (ESs) and remains a topical issue. The present study is an attempt to develop an ES in which inference attributes are formed on the basis of automated discovery of tacit knowledge. The structure of the KA module provides an opportunity to discover new knowledge from the cognitive representations of domain models. The algorithmic processing mechanism consists of three blocks: cluster analysis, influence analysis, and modelling of the equivalent graph. In the proposed expert system based on effective controls, a method of finding influences that highlights the main characteristics of the system is presented. These parameters are used as attributes for the further development of the rule base, expertise, or independent use in decision‐making. A qualitative decrease in user subjectivity is achieved using this algorithm and through elimination of the knowledge engineer from the cycle of KA, which transforms it into a discovery knowledge cycle. Quality assurance of the knowledge base is achieved using three approaches: system presentation, mathematical support, and automation.

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“…These FCM techniques are quite general and have led to numerous applications [3]- [6]. A recent application developed an FCM representation of how 'Brexit' scenarios could affect energy demand in the United Kingdom [7].…”

Section: Fuzzy Cognitive Maps For Causal Modelingmentioning

confidence: 99%

“…Numerous FCM applications have appeared in recent years. They range from control engineering and medicine to policy analysis and social modeling [3]- [6].…”

Section: Fuzzy Cognitive Maps As Models Of Causal Feedbackmentioning

confidence: 99%

Causal Modeling with Feedback Fuzzy Cognitive Maps

Osoba

Kosko

2019

Social‐Behavioral Modeling for Complex Systems

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Fuzzy cognitive maps (FCMs) model feedback causal relations in interwoven webs of causality and policy variables. FCMs are fuzzy signed directed graphs that allow degrees of causal influence and event occurrence. Such causal models can simulate a wide range of policy scenarios and decision processes. Their directed loops or cycles directly model causal feedback. Their nonlinear dynamics permit forward-chaining inference from input causes and policy options to output effects. Users can add detailed dynamics and feedback links directly to the causal model or infer them with statistical learning laws. Users can fuse or combine FCMs from multiple experts by weighting and adding the underlying fuzzy edge matrices and do so recursively if needed. The combined FCM tends to better represent domain knowledge as the expert sample size increases if the expert sample approximates a random sample. Many causal models use more restrictive directed acyclic graphs (DAGs) and Bayesian probabilities. DAGs do not model causal feedback because they do not contain closed loops. Combining DAGs also tends to produce cycles and thus tends not to produce a new DAG. Combining DAGs tends to produce a FCM. FCM causal influence is also transitive whereas probabilistic causal influence is not transitive in general. Overall: FCMs trade the numerical precision of probabilistic DAGs for pattern prediction, faster and scalable computation, ease of combination, and richer feedback representation. We show how FCMs can apply to problems of public support for insurgency and terrorism and to US-China conflict relations in Graham Allison's Thucydides-trap framework. The Thucydides-trap FCM predicts war-like conflict for nearly 80% of input scenarios where we treat all such scenarios

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A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications

Cited by 125 publications

References 73 publications

Automated conflict resolution between multiple clinical pathways: a technology report

Automated conflict resolution between multiple clinical pathways: a technology report

Knowledge discovery using maximization of the spread of influence in an expert system

Causal Modeling with Feedback Fuzzy Cognitive Maps

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