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Case-Based Reasoning Systems in the Health Sciences: A Survey of Recent Trends and Developments
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0002-1212-7637
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0002-5562-1424
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0001-9857-4317
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2011 (English)In: IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews, ISSN 1094-6977, Vol. 41, no 4, 421-434 p.Article in journal (Refereed) Published
Abstract [en]

The Health Sciences are, nowadays, one of the major application areas for case-based reasoning (CBR). The paper presents a survey of recent medical CBR systems based on a literature review and an e-mail questionnaire sent to the corresponding authors of the papers where these systems are presented. Some clear trends have been identified, such as multipurpose systems: more than half of the current medical CBR systems address more than one task. Research on CBR in the area is growing, but most of the systems are still prototypes and not available on the market as commercial products. However, many of the projects/systems are intended to be commercialized.

Place, publisher, year, edition, pages
2011. Vol. 41, no 4, 421-434 p.
Identifiers
URN: urn:nbn:se:mdh:diva-10845DOI: 10.1109/TSMCC.2010.2071862ISI: 000291823300001Scopus ID: 2-s2.0-79959617723OAI: oai:DiVA.org:mdh-10845DiVA: diva2:369149
Available from: 2010-11-10 Created: 2010-11-10 Last updated: 2017-01-25Bibliographically approved
In thesis
1. A Personalised Case-Based Stress Diagnosis System Using Physiological Sensor Signals
Open this publication in new window or tab >>A Personalised Case-Based Stress Diagnosis System Using Physiological Sensor Signals
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Stress is an increasing problem in our present world. It is recognised that increased exposure to stress may cause serious health problems if undiagnosed and untreated. In stress medicine, clinicians’ measure blood pressure, Electrocardiogram (ECG), finger temperature and respiration rate etc. during a number of exercises to diagnose stress-related disorders. However, in practice, it is difficult and tedious for a clinician to understand, interpret and analyze complex, lengthy sequential sensor signals. There are few experts who are able to diagnose and predict stress-related problems. Therefore, a system that can help clinicians in diagnosing stress is important.

This research work has investigated Artificial Intelligence techniques for developing an intelligent, integrated sensor system to establish diagnosis and treatment plans in the psychophysiological domain. This research uses physiological parameters i.e., finger temperature (FT) and heart rate variability (HRV) for quantifying stress levels.  Large individual variations in physiological parameters are one reason why case-based reasoning is applied as a core technique to facilitate experience reuse by retrieving previous similar cases. Feature extraction methods to represent important features of original signals for case indexing are investigated. Furthermore, fuzzy techniques are also employed and incorporated into the case-based reasoning system to handle vagueness and uncertainty inherently existing in clinicians’ reasoning.

The evaluation of the approach is based on close collaboration with experts and measurements of FT and HRV from ECG data. The approach has been evaluated with clinicians and trial measurements on subjects (24+46 persons). An expert has ranked and estimated the similarity for all the subjects during classification. The result shows that the system reaches a level of performance close to an expert in both the cases. The proposed system could be used as an expert for a less experienced clinician or as a second opinion for an experienced clinician to supplement their decision making tasks in stress diagnosis.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2011
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 103
National Category
Computer Science
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-12257 (URN)978-91-7485-018-5 (ISBN)
Public defence
2011-06-20, Pi, Mälardalens högskola, Västerås, 13:35 (English)
Opponent
Supervisors
Available from: 2011-05-16 Created: 2011-05-15 Last updated: 2013-12-03Bibliographically approved
2. 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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