Naslov
Machine Learning for Family Doctors: A Case of Cluster Analysis for Studying Aging Associated Comorbidities and Frailty

Autori
Babič, František ; Trtica Majnarić, Ljiljana ; Bekić, Sanja ; Holzinger, Andreas

Izvornik
Lecture Notes in Computer Science (0302-9743) LNCS 11713 (2019); 178-194

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Aging comorbidities, Complexity, Data-driven clustering

Sažetak
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 pre-diction 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 anthro-pometric and laboratory health indicators, routinely collected in electronic health records. Differences found among clusters in proportions of prefrail and frailversus 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

Izvorni jezik
Engleski

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