Building a Risk Model for the Patient-centred Care of Multiple Chronic Diseases

Stephane Deparis; Pierpaolo Tommasi; Alessandra Pascale; Hicham Rifai; Julie Doyle; John Dinsmore

doi:10.1109/BIBM47256.2019.8983235

Building a Risk Model for the Patient-centred Care of Multiple Chronic Diseases

Stephane Deparis
, Pierpaolo Tommasi
, Alessandra Pascale
, Hicham Rifai
, Julie Doyle
, John Dinsmore

Technological University Dublin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Editors	Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1078-1082
Number of pages	5
ISBN (Electronic)	9781728118673
DOIs	https://doi.org/10.1109/BIBM47256.2019.8983235
Publication status	Published - Nov 2019
Event	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States Duration: 18 Nov 2019 → 21 Nov 2019

Publication series

Name	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

Conference

Conference	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Country/Territory	United States
City	San Diego
Period	18/11/19 → 21/11/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Bayesian network
care management
multimorbidity
risk model

Access to Document

10.1109/BIBM47256.2019.8983235

https://arrow.tudublin.ie/engscheleart/341Licence: CC BY-NC-SA

Cite this

Deparis, S., Tommasi, P., Pascale, A., Rifai, H., Doyle, J., & Dinsmore, J. (2019). Building a Risk Model for the Patient-centred Care of Multiple Chronic Diseases. In I. Yoo, J. Bi, & X. T. Hu (Eds.), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 (pp. 1078-1082). Article 8983235 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM47256.2019.8983235

Deparis, Stephane ; Tommasi, Pierpaolo ; Pascale, Alessandra et al. / Building a Risk Model for the Patient-centred Care of Multiple Chronic Diseases. Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. editor / Illhoi Yoo ; Jinbo Bi ; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1078-1082 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

@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",

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Deparis, S, Tommasi, P, Pascale, A, Rifai, H, Doyle, J & Dinsmore, J 2019, Building a Risk Model for the Patient-centred Care of Multiple Chronic Diseases. in I Yoo, J Bi & XT Hu (eds), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019., 8983235, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, Institute of Electrical and Electronics Engineers Inc., pp. 1078-1082, 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, San Diego, United States, 18/11/19. https://doi.org/10.1109/BIBM47256.2019.8983235

Building a Risk Model for the Patient-centred Care of Multiple Chronic Diseases. / Deparis, Stephane; Tommasi, Pierpaolo; Pascale, Alessandra et al.
Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. ed. / Illhoi Yoo; Jinbo Bi; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1078-1082 8983235 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Building a Risk Model for the Patient-centred Care of Multiple Chronic Diseases

AU - Deparis, Stephane

AU - Tommasi, Pierpaolo

AU - Pascale, Alessandra

AU - Rifai, Hicham

AU - Doyle, Julie

AU - Dinsmore, John

PY - 2019/11

Y1 - 2019/11

N2 - 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.

AB - 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.

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M3 - Conference contribution

AN - SCOPUS:85084338171

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BT - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

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ER -

Deparis S, Tommasi P, Pascale A, Rifai H, Doyle J, Dinsmore J. Building a Risk Model for the Patient-centred Care of Multiple Chronic Diseases. In Yoo I, Bi J, Hu XT, editors, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1078-1082. 8983235. (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). doi: 10.1109/BIBM47256.2019.8983235

Building a Risk Model for the Patient-centred Care of Multiple Chronic Diseases

Abstract

Publication series

Conference

UN SDGs

Keywords

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