Determining pattern similarity in a medical recommender system

Komkhao, M; Lu, J; Zhang, L

Determining pattern similarity in a medical recommender system

Komkhao, M Lu, J

Zhang, L

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7696 pp. 103 - 114
Issue Date:: 2012-01-01

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Field	Value	Language
dc.contributor.author	Komkhao, M	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Zhang, L	en_US
dc.date.issued	2012-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7696 pp. 103 - 114	en_US
dc.identifier.isbn	9783642346781	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120998
dc.description.abstract	© Springer-Verlag Berlin Heidelberg 2012. As recommender systems have proven their effectiveness in other areas, it is aimed to transfer this approach for use in medicine. Particularly, the diagnoses of physicians made in rural hospitals of developing countries, in remote areas or in situations of uncertainty are to be complemented by machine recommendations drawing on large bases of expert knowledge in order to reduce the risk to patients. Recommendation is mainly based on finding known patterns similar to a case under consideration. To search for such patterns in rather large databases, a weighted similarity distance is employed, which is specially derived for medical knowledge. For collaborative filtering an incremental algorithm, called W-InCF, is used working with the Mahalanobis distance and fuzzy membership. W-InCF consists of a learning phase, in which a cluster model of patients’ medical history is constructed incrementally, and a prediction phase, in which the medical pattern of each patient considered is compared with the model to determine the most similar cluster. Fuzzy sets are employed to cope with possible confusion of decision making on overlapping clusters. The degrees of membership to these fuzzy sets is expressed by a weighted Mahalanobis radial basis function, and the weights are derived from risk factors identified by experts. The algorithm is validated using data on cephalopelvic disproportion.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Determining pattern similarity in a medical recommender system	en_US
dc.type	Conference Proceeding
utslib.citation.volume	7696	en_US
utslib.for	080105 Expert Systems	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	080605 Decision Support and Group Support Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	7696	en_US

Abstract:

© Springer-Verlag Berlin Heidelberg 2012. As recommender systems have proven their effectiveness in other areas, it is aimed to transfer this approach for use in medicine. Particularly, the diagnoses of physicians made in rural hospitals of developing countries, in remote areas or in situations of uncertainty are to be complemented by machine recommendations drawing on large bases of expert knowledge in order to reduce the risk to patients. Recommendation is mainly based on finding known patterns similar to a case under consideration. To search for such patterns in rather large databases, a weighted similarity distance is employed, which is specially derived for medical knowledge. For collaborative filtering an incremental algorithm, called W-InCF, is used working with the Mahalanobis distance and fuzzy membership. W-InCF consists of a learning phase, in which a cluster model of patients’ medical history is constructed incrementally, and a prediction phase, in which the medical pattern of each patient considered is compared with the model to determine the most similar cluster. Fuzzy sets are employed to cope with possible confusion of decision making on overlapping clusters. The degrees of membership to these fuzzy sets is expressed by a weighted Mahalanobis radial basis function, and the weights are derived from risk factors identified by experts. The algorithm is validated using data on cephalopelvic disproportion.

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http://hdl.handle.net/10453/120998