Research Article
Case-based retrieval of similar diabetic patients
@INPROCEEDINGS{10.4108/icst.pervasivehealth.2015.259184, author={Damien Zufferey and Stefano Bromuri and Michael Schumacher}, title={Case-based retrieval of similar diabetic patients}, proceedings={Workshop on Personal Health Systems for Chronic Diseases}, publisher={IEEE}, proceedings_a={PHSCD}, year={2015}, month={8}, keywords={diabetes comorbidities health records case-based retrieval clustering}, doi={10.4108/icst.pervasivehealth.2015.259184} }- Damien Zufferey
Stefano Bromuri
Michael Schumacher
Year: 2015
Case-based retrieval of similar diabetic patients
PHSCD
ACM
DOI: 10.4108/icst.pervasivehealth.2015.259184
Abstract
Patients suffering from diabetes often develop several comorbidities such as hypertension and dyslipidemia. The presence of the comorbidities leads to more complex patient profiles associated with specific patient treatments. In this paper we present a novel algorithm to help physicians, given a new case, in retrieving similar past patient cases. This novel algorithm is based on the bag-of-words (BoW) model to encode as features, the occurrence of each pre-computed cluster, for each patient, according to the approach of document classification. We then evaluate the algorithm on a real de-identified dataset of 3201 diabetic patients, demonstrating the advantage of our approach.
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