Authors:
Aleksandar Jeremic
1
;
Natasa Radosavljevic
2
;
Dejan Nikolic
3
and
Milica Lazovic
4
Affiliations:
1
McMaster University, Canada
;
2
Institute for Rehabilitation, Serbia
;
3
University Childrens Hospital, Serbia
;
4
University of Belgrade, Serbia
Keyword(s):
Fuzzy clustering, Hip fracture, Clinical decision making.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Biomedical Signal Processing
;
Detection and Identification
;
Fuzzy Systems and Signals
;
Monitoring and Telemetry
;
Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics
Abstract:
Hip fractures are most frequent cause of hospitalization after the fall in older population and consequently have been subject of great interest in medicine and biomedical engineering. It has been observed that the incidence of hip fractures is rising at the approximate rate of 1-3% per year, with subsequent mortality rates at approximately 33% in first year after the fracture. In this paper we propose to classify patients at the time of admission into several clusters with respect to their ability for successful recovery. To this purpose we first evaluate the efficacy of rehabilitation program based on the balance function improvement measured by Berg Balance Scale (BBS) in elderly (in the remainder of the paper defined as above 65 years of life) after hip fractures, and evaluate influence of gender, age and comorbidity on balance function improvement in these patients. Then we design clustering procedure in which the patients are clustered according to BBS improvement using statist
ically most significant parameters. We then evaluate the proposed clustering procedure on a data sample consisting of 203 patients that have been admitted to the Institute for Rehabilitation, Belgrade, Serbia.
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