Case-based retrieval of similar diabetic patients

Damien Zufferey; Stefano Bromuri; Michael Schumacher

Workshop on Personal Health Systems for Chronic Diseases

Research Article

Case-based retrieval of similar diabetic patients

Cite: BibTeX Plain Text

@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

Damien Zufferey¹^,*, Stefano Bromuri¹, Michael Schumacher¹

1: University of Applied Sciences Western Switzerland

*Contact email: damien.zufferey@hevs.ch

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.

Keywords: diabetes comorbidities health records case-based retrieval clustering

Published: 2015-08-03
Publisher: IEEE

: http://dx.doi.org/10.4108/icst.pervasivehealth.2015.259184