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A Multimodal Approach for Clinical Diagnosis and Treatment
Mälardalen University, School of Innovation, Design and Engineering. (Intelligent Systems)ORCID iD: 0000-0003-3802-4721
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: urn:nbn:se:mdh:diva-13166ISBN: 978-91-7485-043-7 (print)OAI: oai:DiVA.org:mdh-13166DiVA: diva2:450659
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
List of papers
1. Case-Based Reasoning Systems in the Health Sciences: A Survey of Recent Trends and Developments
Open this publication in new window or tab >>Case-Based Reasoning Systems in the Health Sciences: A Survey of Recent Trends and Developments
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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.

Identifiers
urn:nbn:se:mdh:diva-10845 (URN)10.1109/TSMCC.2010.2071862 (DOI)000291823300001 ()2-s2.0-79959617723 (Scopus ID)
Available from: 2010-11-10 Created: 2010-11-10 Last updated: 2017-01-25Bibliographically approved
2. A Hybrid Case-Based System in Stress Diagnosis and Treatment
Open this publication in new window or tab >>A Hybrid Case-Based System in Stress Diagnosis and Treatment
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.

Keyword
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:nbn:se:mdh:diva-13161 (URN)
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
3. Case-based Reasoning for Diagnosis of Stress using Enhanced Cosine and Fuzzy Similarity
Open this publication in new window or tab >>Case-based Reasoning for Diagnosis of Stress using Enhanced Cosine and Fuzzy Similarity
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2008 (English)In: Transactions on Case-Based Reasoning on Multimedia Data, ISSN 1867-366X, Vol. 1, no 1, 3-19 p.Article in journal (Refereed) Published
Abstract [en]

Intelligent analysis of heterogeneous data and information sources for efficient decision support presents an interesting yet challenging task in clinical environments. This is particularly the case in stress medicine where digital patient records are becoming popular which contain not only lengthy time series measurements but also unstructured textual documents expressed in form of natural languages. This paper develops a hybrid case-based reasoning system for stress diagnosis which is capable of coping with both numerical signals and textual data at the same time. The total case index consists of two sub-parts corresponding to signal and textual data respectively. For matching of cases on the signal aspect we present a fuzzy similarity matching metric to accommodate and tackle the imprecision and uncertainty in sensor measurements. Preliminary evaluations have revealed that this fuzzy matching algorithm leads to more accurate similarity estimates for improved case ranking and retrieval compared with traditional distance-based matching criteria. For evaluation of similarity on the textual dimension we propose an enhanced cosine matching function augmented with related domain knowledge. This is implemented by incorporating Wordnet and domain specific ontology into the textual case-based reasoning process for refining weights of terms according to available knowledge encoded therein. Such knowledge-based reasoning for matching of textual cases has empirically shown its merit in improving both precision and recall of retrieved cases with our initial medical databases. Experts in the domain are very positive to our system and they deem that it will be a valuable tool to foster widespread experience reuse and transfer in the area of stress diagnosis and treatment.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:mdh:diva-7240 (URN)
Available from: 2009-09-25 Created: 2009-09-25 Last updated: 2017-01-25Bibliographically approved
4. A Multi-Module Case Based Biofeedback System for Stress Treatment
Open this publication in new window or tab >>A Multi-Module Case Based Biofeedback System for Stress Treatment
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2011 (English)In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 51, no 2, 107-115 p.Article in journal (Refereed) Published
Abstract [en]

Biofeedback is today a recognized treatment method for a number of physical and psychological problems. Experienced clinicians often achieve good results in these areas and their success largely builds on many years of experience and often thousands of treated patients. Unfortunately many of the areas where biofeedback is used are very complex, e.g. diagnosis and treatment of stress. Less experienced clinicians may even have difficulties to initially classify the patient correctly. Often there are only a few experts available to assist less experienced clinicians. To reduce this problem we propose a computer assisted biofeedback system helping in classification, parameter setting and biofeedback training. By adopting a case based approach in a computer-based biofeedback system, decision support can be offered to less experienced clinicians and provide a second opinion to experts. We explore how such a system may be designed and validate the approach in the area of stress where the system assists in the classification, parameter setting and finally in the training. In a case study we show that the case based biofeedback system outperforms novice clinicians based on a case library of cases authorized by an expert.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:mdh:diva-8950 (URN)10.1016/j.artmed.2010.09.003 (DOI)000289331100004 ()2-s2.0-79952512335 (Scopus ID)
Available from: 2010-03-03 Created: 2010-03-03 Last updated: 2017-01-25Bibliographically approved
5. FUZZY RULE-BASED CLASSIFICATION TO BUILD INITIAL CASE LIBRARY FOR CASE-BASED STRESS DIAGNOSIS
Open this publication in new window or tab >>FUZZY RULE-BASED CLASSIFICATION TO BUILD INITIAL CASE LIBRARY FOR CASE-BASED STRESS DIAGNOSIS
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, (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.

Keyword
Case-based reasoning, fuzzy rule-based reasoning, stress
National Category
Computer and Information Science
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-13162 (URN)9780889867802 (ISBN)
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

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