Feature Allocation Approach for Multimorbidity Trajectory Modelling

Woojung Kim, Paul A. Jenkins, Christopher Yau
Proceedings of the 2nd Machine Learning for Health symposium, PMLR 193:103-119, 2022.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v193-kim22a, title = {Feature Allocation Approach for Multimorbidity Trajectory Modelling}, author = {Kim, Woojung and Jenkins, Paul A. and Yau, Christopher}, booktitle = {Proceedings of the 2nd Machine Learning for Health symposium}, pages = {103--119}, year = {2022}, editor = {Parziale, Antonio and Agrawal, Monica and Joshi, Shalmali and Chen, Irene Y. and Tang, Shengpu and Oala, Luis and Subbaswamy, Adarsh}, volume = {193}, series = {Proceedings of Machine Learning Research}, month = {28 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v193/kim22a/kim22a.pdf}, 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.} }
Endnote
%0 Conference Paper %T Feature Allocation Approach for Multimorbidity Trajectory Modelling %A Woojung Kim %A Paul A. Jenkins %A Christopher Yau %B Proceedings of the 2nd Machine Learning for Health symposium %C Proceedings of Machine Learning Research %D 2022 %E Antonio Parziale %E Monica Agrawal %E Shalmali Joshi %E Irene Y. Chen %E Shengpu Tang %E Luis Oala %E Adarsh Subbaswamy %F pmlr-v193-kim22a %I PMLR %P 103--119 %U https://proceedings.mlr.press/v193/kim22a.html %V 193 %X 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.
APA
Kim, W., Jenkins, P.A. & Yau, C.. (2022). Feature Allocation Approach for Multimorbidity Trajectory Modelling. Proceedings of the 2nd Machine Learning for Health symposium, in Proceedings of Machine Learning Research 193:103-119 Available from https://proceedings.mlr.press/v193/kim22a.html.

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