A study on Fuzzy Cognitive Map structures for Medical Decision Support Systems

Bourgani, Evangelia; Stylios, Chrysostomos; Georgopoulos, Voula C.; Manis, George

doi:10.2991/eusflat.2013.111

Cited by 12 publications

(9 citation statements)

References 20 publications

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“…In many medical applications, time has not taken into consideration [13] despite the fact that time is a crucial factor which can lead to different decision. T-FCMs are essential for problems of differential diagnosis [14], [15]} or in obstetrics [16], where time plays a significant role for the evolution of a problem.…”

Section: B Possible Applications Of Timed-fcmmentioning

confidence: 99%

Timed Fuzzy Cognitive Maps

Bourgani

Stylios

Manis

et al. 2015

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Taking time into consideration in the FCM model and the subsequent numerical calculation is a hot FCM issue. This work investigates an approach for introducing the time in Fuzzy Cognitive Maps (FCM) framework and it proposes the Timed-FCM (T-FCM) that incorporates time steps in their structure and constitutes an extension of FCMs. The proposed TFCMs take into consideration the time evolution and the fact that the parameters of any system change with the time and it accepts intermediate states and results of the modeled system. In any problem, its factors change differently over time and so their influence as is presented on the interconnections between the concepts are time dependent.

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Section: B Possible Applications Of Timed-fcmmentioning

confidence: 99%

Timed Fuzzy Cognitive Maps

Bourgani

Stylios

Manis

et al. 2015

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

Self Cite

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“…An edge is created whenever the diagnostic factor and diagnostic hypothesis are increase-related or decrease-related according to the binary analysis. The graph is automatically converted into a cognitive fuzzy map [28,33] that displays an associated numerical weight for each node and edge. The student can modify the weight of diagnostic factor-diagnostic hypothesis edges according to their estimated importance of a specific diagnostic factor supporting the likelihood of a certain disease.…”

Section: Pattern Analysismentioning

confidence: 99%

Learning Analytics Applied to Clinical Diagnostic Reasoning Using a Natural Language Processing–Based Virtual Patient Simulator: Case Study

Furlan¹,

Gatti²,

Menè³

et al. 2022

JMIR Med Educ

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Background Virtual patient simulators (VPSs) log all users’ actions, thereby enabling the creation of a multidimensional representation of students’ medical knowledge. This representation can be used to create metrics providing teachers with valuable learning information. Objective The aim of this study is to describe the metrics we developed to analyze the clinical diagnostic reasoning of medical students, provide examples of their application, and preliminarily validate these metrics on a class of undergraduate medical students. The metrics are computed from the data obtained through a novel VPS embedding natural language processing techniques. Methods A total of 2 clinical case simulations (tests) were created to test our metrics. During each simulation, the students’ step-by-step actions were logged into the program database for offline analysis. The students’ performance was divided into seven dimensions: the identification of relevant information in the given clinical scenario, history taking, physical examination, medical test ordering, diagnostic hypothesis setting, binary analysis fulfillment, and final diagnosis setting. Sensitivity (percentage of relevant information found) and precision (percentage of correct actions performed) metrics were computed for each issue and combined into a harmonic mean (F1), thereby obtaining a single score evaluating the students’ performance. The 7 metrics were further grouped to reflect the students’ capability to collect and to analyze information to obtain an overall performance score. A methodological score was computed based on the discordance between the diagnostic pathway followed by students and the reference one previously defined by the teacher. In total, 25 students attending the fifth year of the School of Medicine at Humanitas University underwent test 1, which simulated a patient with dyspnea. Test 2 dealt with abdominal pain and was attended by 36 students on a different day. For validation, we assessed the Spearman rank correlation between the performance on these scores and the score obtained by each student in the hematology curricular examination. Results The mean overall scores were consistent between test 1 (mean 0.59, SD 0.05) and test 2 (mean 0.54, SD 0.12). For each student, the overall performance was achieved through a different contribution in collecting and analyzing information. Methodological scores highlighted discordances between the reference diagnostic pattern previously set by the teacher and the one pursued by the student. No significant correlation was found between the VPS scores and hematology examination scores. Conclusions Different components of the students’ diagnostic process may be disentangled and quantified by appropriate metrics applied to students’ actions recorded while addressing a virtual case. Such an approach may help teachers provide students with individualized feedback aimed at filling competence drawbacks and methodological inconsistencies. There was no correlation between the hematology curricular examination score and any of the proposed scores as these scores address different aspects of students’ medical knowledge.

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“…The reasoning mechanism behind Fuzzy Cognitive Maps (FCMs) [12] combines elements of fuzzy logic, neural networks and causal modeling. Fuzzy cognitive mapping allows modeling a real world system as a collection of concepts and causal relations [2]. One of the most attractive features attached to these knowledgebased networks lies in their graphical nature, their transparency and adaptability and their ability to perform WHAT-IF simulations.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Cognitive Maps Reasoning with Words Based on Triangular Fuzzy Numbers

Frias

Filiberto

Nápoles

et al. 2018

Advances in Soft Computing

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A pivotal difference between Artificial Neural Networks and Fuzzy Cognitive Maps (FCMs) is that the latter allow modeling a physical system in terms of concepts and causal relations, thus equipping the network with interpretability features. However, such components are normally described by quantitative terms, which may be difficult to handle by domain experts. In this paper, we explore a reasoning mechanism for FCMs based on the Computing with Words paradigm where numerical concepts and relations are replaced with linguistic terms. More explicitly, we include triangular fuzzy numbers into the qualitative reasoning process attached to our model, thus proving further interpretability and transparency. The simulations show the potential behind the symbolic reasoning mechanism proposed in this study.

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A study on Fuzzy Cognitive Map structures for Medical Decision Support Systems

Cited by 12 publications

References 20 publications

Timed Fuzzy Cognitive Maps

Timed Fuzzy Cognitive Maps

Learning Analytics Applied to Clinical Diagnostic Reasoning Using a Natural Language Processing–Based Virtual Patient Simulator: Case Study

Fuzzy Cognitive Maps Reasoning with Words Based on Triangular Fuzzy Numbers

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