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Electrical and electronic engineering, 3. MEDICAL AND HEALTH SCIENCES
With the increase of multimorbidity due to population ageing, managing multiple chronic health conditions is a rising challenge. Machine-learning can contribute to a better understanding of persons with multimorbidity (PwMs) and how to design an effective framework of care and support for them. We present a risk model of older PwMs that was derived from the TILDA dataset, a longitudinal study of the ageing Irish population. This model is based on a 26-nodes Bayesian network that represents patients possibly having one or more chronic conditions among diabetes, chronic obstructive pulmonary disease and arthritis, through a joint probability distribution of demographic, symptomatic and behavioral dimensions. We describe our method, give an exploratory analysis of the risk model, and assess its prediction accuracy in a cross-validation experiment. Finally we discuss its use in supporting management of care for PwMs, drawing on comments from health practitioners on the model.
S. Deparis, P. Tommasi, A. Pascale, H. Rifai, J. Doyle and J. Dinsmore, "Building a Risk Model for the Patient-centred Care of Multiple Chronic Diseases," 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2019, pp. 1078-1082, doi: 10.1109/BIBM47256.2019.8983235.