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FUZZY RULE-BASED CLASSIFICATION TO BUILD INITIAL CASE LIBRARY FOR CASE-BASED STRESS DIAGNOSIS
Mälardalen University, School of Innovation, Design and Engineering. (Intelligent Systems)ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering. (Intelligent Systems)ORCID iD: 0000-0002-1212-7637
Mälardalen University, School of Innovation, Design and Engineering. (Intelligent Systems)ORCID iD: 0000-0002-5562-1424
Mälardalen University, School of Innovation, Design and Engineering. (Intelligent Systems)ORCID iD: 0000-0001-9857-4317
2009 (English)In: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2009 / [ed] M.H. Hamza, 2009, 225-230 p.Conference paper, Published paper (Refereed)
Abstract [en]

Case-Based Reasoning (CBR) is receiving increasedinterest for applications in medical decision support.Clinicians appreciate the fact that the system reasons withfull medical cases, symptoms, diagnosis, actions takenand outcomes. Also for experts it is often appreciated toget a second opinion. In the initial phase of a CBR systemthere are often a limited number of cases available whichreduces the performance of the system. If past cases aremissing or very sparse in some areas the accuracy isreduced. This paper presents a fuzzy rule-basedclassification scheme which is introduced into the CBRsystem to initiate the case library, providing improvedperformance in the stress diagnosis task. Theexperimental results showed that the CBR system usingthe enhanced case library can correctly classify 83% ofthe cases, whereas previously the correctness of theclassification was 61%. Consequently the proposedsystem has an improved performance with 22% in termsof accuracy. In terms of the discrepancy in classificationcompared to the expert, the goodness-of-fit value of thetest results is on average 87%. Thus by employing thefuzzy rule-based classification, the new hybrid system cangenerate artificial cases to enhance the case library.Furthermore, it can classify new problem cases previouslynot classified by the system.

Place, publisher, year, edition, pages
2009. 225-230 p.
Keyword [en]
Case-based reasoning, fuzzy rule-based reasoning, stress
National Category
Computer and Information Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:mdh:diva-13162ISBN: 9780889867802 (print)OAI: oai:DiVA.org:mdh-13162DiVA: diva2:450606
Conference
IASTED International Conference on Artificial Intelligence and Applications, AIA 2009; Innsbruck; Austria; 16 February 2009 through 18 February 2009
Projects
IPOS
Available from: 2011-10-21 Created: 2011-10-21 Last updated: 2017-01-25Bibliographically approved
In thesis
1. A Multimodal Approach for Clinical Diagnosis and Treatment
Open this publication in new window or tab >>A Multimodal Approach for Clinical Diagnosis and Treatment
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A computer-aided Clinical Decision Support System (CDSS) for diagnosis and treatment often plays a vital role and brings essential benefits for clinicians. Such a CDSS could function as an expert for a less experienced clinician or as a second option/opinion of an experienced clinician to their decision making task. Nevertheless, it has been a real challenge to design and develop such a functional system where accuracy of the system performance is an important issue.

This research work focuses on development of intelligent CDSS based on a multimodal approach for diagnosis, classification and treatment in medical domains i.e. stress and post-operative pain management domains. Several Artificial Intelligence (AI) techniques such as Case-Based Reasoning (CBR), textual Information Retrieval (IR), Rule-Based Reasoning (RBR), Fuzzy Logic and clustering approaches have been investigated in this thesis work.

Patient’s data i.e. their stress and pain related information are collected from complex data sources for instance, finger temperature measurements through sensor signals, pain measurements using a Numerical Visual Analogue Scale (NVAS), patient’s information from text and multiple choice questionnaires. The proposed approach considers multimedia data management to be able to use them in CDSSs for both the domains.

The functionalities and performance of the systems have been evaluated based on close collaboration with experts and clinicians of the domains. In stress management, 68 measurements from 46 subjects and 1572 patients’ cases out of ≈4000 in post-operative pain have been used to design, develop and validate the systems. In the stress management domain, besides the 68 measurement cases, three trainees and one senior clinician also have been involved in order to conduct the experimental work. The result from the evaluation shows that the system reaches a level of performance close to the expert and better than the senior and trainee clinicians. Thus, the proposed CDSS could be used as an expert for a less experienced clinician (i.e. trainee) or as a second option/opinion for an experienced clinician (i.e. senior) to their decision making process in stress management. In post-operative pain treatment, the CDSS retrieves and presents most similar cases (e.g. both rare and regular) with their outcomes to assist physicians. Moreover, an automatic approach is presented in order to identify rare cases and 18% of cases from the whole cases library i.e. 276 out of 1572 are identified as rare cases by the approach. Again, among the rare cases (i.e. 276), around 57.25% of the cases are classified as ‘unusually bad’ i.e. the average pain outcome value is greater or equal to 5 on the NVAS scale 0 to 10. Identification of rear cases is an important part of the PAIN OUT project and can be used to improve the quality of individual pain treatment.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2011
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 113
National Category
Computer and Information Science
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-13166 (URN)978-91-7485-043-7 (ISBN)
Public defence
2011-11-22, Paros, Mälardalens högskola, Västerås, 13:15 (English)
Opponent
Supervisors
Available from: 2011-10-21 Created: 2011-10-21 Last updated: 2017-01-25Bibliographically approved

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Citation style
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