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A Hybrid Case-Based System in Stress Diagnosis and Treatment
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
2012 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Computer-aided decision support systems play anincreasingly important role in clinical diagnosis and treatment.However, they are difficult to build for domains where thedomain theory is weak and where different experts differ indiagnosis. Stress diagnosis and treatment is an example of such adomain. This paper explores several artificial intelligencemethods and techniques and in particular case-based reasoning,textual information retrieval, rule-based reasoning, and fuzzylogic to enable a more reliable diagnosis and treatment of stress.The proposed hybrid case-based approach has been validated byimplementing a prototype in close collaboration with leadingexperts in stress diagnosis. The obtained sensitivity, specificityand overall accuracy compared to an expert are 92%, 86% and88% respectively.

Place, publisher, year, edition, pages
2012.
Keyword [en]
Artificial intelligence, Biofeedback, Case based reasoning, Diagnosis, Information retrieval, Rule based reasoning, Stress measurement.
National Category
Computer and Information Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:mdh:diva-13161OAI: oai:DiVA.org:mdh-13161DiVA: diva2:450594
Conference
IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI2012)
Projects
IModNovaMedTech
Note
Submitted to: IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI2012)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
  • apa
  • harvard1
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  • Other locale
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