Mixed Type Multimorbidity Variational Autoencoder: A Deep Generative Model for Multimorbidity Analysis

Woojung Kim, Paul A. Jenkins, Christopher Yau
Proceedings of the 9th Machine Learning for Healthcare Conference, PMLR 252, 2024.

Abstract

This paper introduces the Mixed Type Multimorbidity Variational Autoencoder ($\text{M}^{3}$VAE), a deep probabilistic generative model developed for supervised dimensionality reduction in the context of multimorbidity analysis. The model is designed to overcome the limitations of purely supervised or unsupervised approaches in this field. $\text{M}^{3}$VAE focuses on identifying latent representations of mixed-type health-related attributes essential for predicting patient survival outcomes. It integrates datasets with multiple modalities (by which we mean data of multiple types), encompassing health measurements, demographic details, and (potentially censored) survival outcomes. A key feature of $\text{M}^{3}$VAE is its ability to reconstruct latent representations that exhibit clustering patterns, thereby revealing important patterns in disease co-occurrence. This functionality provides insights for understanding and predicting health outcomes. The efficacy of $\text{M}^{3}$VAE has been demonstrated through experiments with both synthetic and real-world electronic health record data, showing its capability in identifying interpretable morbidity groupings related to future survival outcomes.

Cite this Paper

BibTeX
@InProceedings{pmlr-v252-kim24b, title = {Mixed Type Multimorbidity Variational Autoencoder: A Deep Generative Model for Multimorbidity Analysis}, author = {Kim, Woojung and Jenkins, Paul A. and Yau, Christopher}, booktitle = {Proceedings of the 9th Machine Learning for Healthcare Conference}, year = {2024}, editor = {Deshpande, Kaivalya and Fiterau, Madalina and Joshi, Shalmali and Lipton, Zachary and Ranganath, Rajesh and Urteaga, Iñigo}, volume = {252}, series = {Proceedings of Machine Learning Research}, month = {16--17 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v252/main/assets/kim24b/kim24b.pdf}, url = {https://proceedings.mlr.press/v252/kim24b.html}, abstract = {This paper introduces the Mixed Type Multimorbidity Variational Autoencoder ($\text{M}^{3}$VAE), a deep probabilistic generative model developed for supervised dimensionality reduction in the context of multimorbidity analysis. The model is designed to overcome the limitations of purely supervised or unsupervised approaches in this field. $\text{M}^{3}$VAE focuses on identifying latent representations of mixed-type health-related attributes essential for predicting patient survival outcomes. It integrates datasets with multiple modalities (by which we mean data of multiple types), encompassing health measurements, demographic details, and (potentially censored) survival outcomes. A key feature of $\text{M}^{3}$VAE is its ability to reconstruct latent representations that exhibit clustering patterns, thereby revealing important patterns in disease co-occurrence. This functionality provides insights for understanding and predicting health outcomes. The efficacy of $\text{M}^{3}$VAE has been demonstrated through experiments with both synthetic and real-world electronic health record data, showing its capability in identifying interpretable morbidity groupings related to future survival outcomes.} }
Endnote
%0 Conference Paper %T Mixed Type Multimorbidity Variational Autoencoder: A Deep Generative Model for Multimorbidity Analysis %A Woojung Kim %A Paul A. Jenkins %A Christopher Yau %B Proceedings of the 9th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2024 %E Kaivalya Deshpande %E Madalina Fiterau %E Shalmali Joshi %E Zachary Lipton %E Rajesh Ranganath %E Iñigo Urteaga %F pmlr-v252-kim24b %I PMLR %U https://proceedings.mlr.press/v252/kim24b.html %V 252 %X This paper introduces the Mixed Type Multimorbidity Variational Autoencoder ($\text{M}^{3}$VAE), a deep probabilistic generative model developed for supervised dimensionality reduction in the context of multimorbidity analysis. The model is designed to overcome the limitations of purely supervised or unsupervised approaches in this field. $\text{M}^{3}$VAE focuses on identifying latent representations of mixed-type health-related attributes essential for predicting patient survival outcomes. It integrates datasets with multiple modalities (by which we mean data of multiple types), encompassing health measurements, demographic details, and (potentially censored) survival outcomes. A key feature of $\text{M}^{3}$VAE is its ability to reconstruct latent representations that exhibit clustering patterns, thereby revealing important patterns in disease co-occurrence. This functionality provides insights for understanding and predicting health outcomes. The efficacy of $\text{M}^{3}$VAE has been demonstrated through experiments with both synthetic and real-world electronic health record data, showing its capability in identifying interpretable morbidity groupings related to future survival outcomes.
APA
Kim, W., Jenkins, P.A. & Yau, C.. (2024). Mixed Type Multimorbidity Variational Autoencoder: A Deep Generative Model for Multimorbidity Analysis. Proceedings of the 9th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 252 Available from https://proceedings.mlr.press/v252/kim24b.html.

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