%0 Conference Proceedings
%T Machine Learning for Family Doctors: A Case of Cluster Analysis for Studying Aging Associated Comorbidities and Frailty
%+ Department of Cybernetics and Artificial Intelligence
%+ Josip Juraj Strossmayer University of Osijek
%+ Medical University Graz
%A Babič, František
%A Trtica Majnarić, Ljiljana
%A Bekić, Sanja
%A Holzinger, Andreas
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 3rd International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE)
%C Canterbury, United Kingdom
%Y Andreas Holzinger
%Y Peter Kieseberg
%Y A Min Tjoa
%Y Edgar Weippl
%I Springer International Publishing
%3 Machine Learning and Knowledge Extraction
%V LNCS-11713
%P 178-194
%8 2019-08-26
%D 2019
%R 10.1007/978-3-030-29726-8_12
%K Aging comorbidities
%K Complexity
%K Data-driven clustering
%Z Computer Science [cs]Conference papers
%X Many problems in clinical medicine are characterized by high complexity and non-linearity. Particularly, this is the case with aging diseases, chronic medical conditions that are known to tend to accumulate in the same person. This phenomenon is known as multimorbidity. In addition to the number of chronic diseases, the presence of integrated geriatric conditions and functional deficits, such as walking difficulties, of frailty (a general weakness associated with weight and muscle loss and low functioning) are important for the prediction of negative health outcomes of older people, such as hospitalization, dependency on others or pre-term mortality. In this work, we identified how frailty is associated with clinical phenotypes, which most reliably characterize the group of older patients from our local environment: the general practice attenders. We have performed cluster analysis, based on using a set of anthropometric and laboratory health indicators, routinely collected in electronic health records. Differences found among clusters in proportions of prefrail and frail versus non-frail patients have been explained with differences in the central values of the parameters used for clustering. Distribution patterns of chronic diseases and other geriatric conditions, found by the assessment of differences, were very useful in determining the clinical phenotypes derived by the clusters. Once more, this study demonstrates the most important aspect of any machine learning task: the quality of the data!
%G English
%Z TC 5
%Z TC 12
%Z WG 8.4
%Z WG 8.9
%Z WG 12.9
%2 https://inria.hal.science/hal-02520059/document
%2 https://inria.hal.science/hal-02520059/file/485369_1_En_12_Chapter.pdf
%L hal-02520059
%U https://inria.hal.science/hal-02520059
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-WG8-4
%~ IFIP-WG8-9
%~ IFIP-CD-MAKE
%~ IFIP-WG12-9
%~ IFIP-LNCS-11713