mmVAE: multimorbidity clustering using Relaxed Bernoulli $β$-Variational Autoencoders

Charles Gadd, Krishnarajah Nirantharakumar, Christopher Yau
Proceedings of the 2nd Machine Learning for Health symposium, PMLR 193:88-102, 2022.

Abstract

The prevalence of chronic disease multimorbidity is a significant and increasing challenge for health systems. In many cases, the occurrence of one chronic disease leads to the development of one or more other chronic conditions. This exerts a significant challenge in improving patient outcomes and is a growing challenge globally as average population age increases. Using electronic health record information to identify patterns of co-occurring conditions is seen as an unbiased means of understanding multimorbidity but most studies have adopted off-the-shelf algorithmic techniques that are not tailored for the application. We present a novel bespoke approach for multimorbidity clustering based on a highly customised version of a $\beta$-variational autoencoder. We incorporate the use of minimum entropy clustering to identify sparse, low-dimensional factored representations that link at a feature-level to the observed patient-level multimorbidity profiles. We demonstrate how the approach can be used to explore complex structure in a population-scale health data sets by examining data from a UK population of nearly 300,000 women in pregnancy suffering from multimorbidity.

Cite this Paper

BibTeX
@InProceedings{pmlr-v193-gadd22a, title = {mmVAE: multimorbidity clustering using Relaxed Bernoulli $β$-Variational Autoencoders}, author = {Gadd, Charles and Nirantharakumar, Krishnarajah and Yau, Christopher}, booktitle = {Proceedings of the 2nd Machine Learning for Health symposium}, pages = {88--102}, 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/gadd22a/gadd22a.pdf}, url = {https://proceedings.mlr.press/v193/gadd22a.html}, abstract = {The prevalence of chronic disease multimorbidity is a significant and increasing challenge for health systems. In many cases, the occurrence of one chronic disease leads to the development of one or more other chronic conditions. This exerts a significant challenge in improving patient outcomes and is a growing challenge globally as average population age increases. Using electronic health record information to identify patterns of co-occurring conditions is seen as an unbiased means of understanding multimorbidity but most studies have adopted off-the-shelf algorithmic techniques that are not tailored for the application. We present a novel bespoke approach for multimorbidity clustering based on a highly customised version of a $\beta$-variational autoencoder. We incorporate the use of minimum entropy clustering to identify sparse, low-dimensional factored representations that link at a feature-level to the observed patient-level multimorbidity profiles. We demonstrate how the approach can be used to explore complex structure in a population-scale health data sets by examining data from a UK population of nearly 300,000 women in pregnancy suffering from multimorbidity.} }
Endnote
%0 Conference Paper %T mmVAE: multimorbidity clustering using Relaxed Bernoulli $β$-Variational Autoencoders %A Charles Gadd %A Krishnarajah Nirantharakumar %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-gadd22a %I PMLR %P 88--102 %U https://proceedings.mlr.press/v193/gadd22a.html %V 193 %X The prevalence of chronic disease multimorbidity is a significant and increasing challenge for health systems. In many cases, the occurrence of one chronic disease leads to the development of one or more other chronic conditions. This exerts a significant challenge in improving patient outcomes and is a growing challenge globally as average population age increases. Using electronic health record information to identify patterns of co-occurring conditions is seen as an unbiased means of understanding multimorbidity but most studies have adopted off-the-shelf algorithmic techniques that are not tailored for the application. We present a novel bespoke approach for multimorbidity clustering based on a highly customised version of a $\beta$-variational autoencoder. We incorporate the use of minimum entropy clustering to identify sparse, low-dimensional factored representations that link at a feature-level to the observed patient-level multimorbidity profiles. We demonstrate how the approach can be used to explore complex structure in a population-scale health data sets by examining data from a UK population of nearly 300,000 women in pregnancy suffering from multimorbidity.
APA
Gadd, C., Nirantharakumar, K. & Yau, C.. (2022). mmVAE: multimorbidity clustering using Relaxed Bernoulli $β$-Variational Autoencoders. Proceedings of the 2nd Machine Learning for Health symposium, in Proceedings of Machine Learning Research 193:88-102 Available from https://proceedings.mlr.press/v193/gadd22a.html.

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