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Feature allocation approach for multimorbidity trajectory modelling
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Kim, Woojung, Jenkins, Paul A. and Yau, Christopher (2022) Feature allocation approach for multimorbidity trajectory modelling. In: 2nd Machine Learning for Health symposium (ML4H 2022), New Orleans, USA and virtual, 28 Nov 2022. Published in: Proceedings of Machine Learning Research, 193 pp. 103-119. ISSN 2640-3498.
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Official URL: https://proceedings.mlr.press/v193/kim22a.html
Abstract
A multimorbidity trajectory charts the time-dependent acquisition of disease conditions in an individual. This is important for understanding and managing patients who have a complex array of multiple chronic conditions, particularly later in life. We construct a novel probabilistic generative model for multimorbidity acquisition within a Bayesian framework of latent feature allocation, which allows an individual’s morbidity profile to be driven by multiple latent factors and allows the modelling of age-dependent multimorbidity trajectories. We demonstrate the utility of our model in applications to both simulated data and disease event data from patient electronic health records. In each setting, we show our model can reconstruct clinically meaningful latent multimorbidity patterns and their age-dependent prevalence trajectories.
Item Type: | Conference Item (Paper) | |||||||||||||||||||||
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Subjects: | Q Science > QA Mathematics R Medicine > RC Internal medicine |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | |||||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Comorbidity, Resource allocation -- Mathematical models, Bayesian statistical decision theory, Monte Carlo method, Markov processes, Medicine -- Data processing | |||||||||||||||||||||
Series Name: | Proceedings of Machine Learning Research | |||||||||||||||||||||
Journal or Publication Title: | Proceedings of Machine Learning Research | |||||||||||||||||||||
Publisher: | ML Research Press | |||||||||||||||||||||
ISSN: | 2640-3498 | |||||||||||||||||||||
Official Date: | 2022 | |||||||||||||||||||||
Dates: |
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Volume: | 193 | |||||||||||||||||||||
Page Range: | pp. 103-119 | |||||||||||||||||||||
Status: | Peer Reviewed | |||||||||||||||||||||
Publication Status: | Published | |||||||||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||||||||
Copyright Holders: | © 2022 W. Kim, P. A. Jenkins & C. Yau | |||||||||||||||||||||
Date of first compliant deposit: | 30 November 2022 | |||||||||||||||||||||
Date of first compliant Open Access: | 30 November 2022 | |||||||||||||||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | |||||||||||||||||||||
Title of Event: | 2nd Machine Learning for Health symposium (ML4H 2022) | |||||||||||||||||||||
Type of Event: | Other | |||||||||||||||||||||
Location of Event: | New Orleans, USA and virtual | |||||||||||||||||||||
Date(s) of Event: | 28 Nov 2022 | |||||||||||||||||||||
Open Access Version: |
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