@inproceedings{0e6bc6920d344de38d4e91c69f90efb6,
title = "Building a Risk Model for the Patient-centred Care of Multiple Chronic Diseases",
abstract = "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.",
keywords = "Bayesian network, care management, multimorbidity, risk model",
author = "Stephane Deparis and Pierpaolo Tommasi and Alessandra Pascale and Hicham Rifai and Julie Doyle and John Dinsmore",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 ; Conference date: 18-11-2019 Through 21-11-2019",
year = "2019",
month = nov,
doi = "10.1109/BIBM47256.2019.8983235",
language = "English",
series = "Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1078--1082",
editor = "Illhoi Yoo and Jinbo Bi and Hu, {Xiaohua Tony}",
booktitle = "Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019",
address = "United States",
}