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Case-based Reasoning for Diagnosis of Stress using Enhanced Cosine and Fuzzy Similarity
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0002-1212-7637
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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2008 (English)In: Case-based Reasoning for Diagnosis of Stress using Enhanced Cosine and Fuzzy Similarity, ISSN 1867-366X, Vol. 1, p. 3-19Article 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 envi-ronments. This is particularly the case in stress medicine where digital patient re-cords 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 di-agnosis 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 crite-ria. For evaluation of similarity on the textual dimension we propose an enhanced cosine matching function augmented with related domain knowledge. This is im-plemented 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 tex-tual 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 wide-spread experience reuse and transfer in the area of stress diagnosis and treatment.

Place, publisher, year, edition, pages
2008. Vol. 1, p. 3-19
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-7199OAI: oai:DiVA.org:mdh-7199DiVA, id: diva2:237209
Conference
8th Industrial Conference, ICDM 2008, Leipzig, Germany
Available from: 2009-09-25 Created: 2009-09-25 Last updated: 2017-01-25Bibliographically approved

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http://www.ibai-publishing.org/journal/issue_cbr/2008_october/cbrmd_1_1_3-19.pdf

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Ahmed, Mobyen UddinBegum, ShahinaFunk, PeterXiong, Ning

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