Mixture of Coupled HMMs for Robust Modeling of Multivariate Healthcare Time Series
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2023-12-04
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Mcode
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en
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18
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Proceedings of the 3rd Machine Learning for Health Symposium, pp. 461-479, Proceedings of Machine Learning Research ; Volume 225
Abstract
Analysis of multivariate healthcare time series data is inherently challenging: irregular sampling, noisy and missing values, and heterogeneous patient groups with different dynamics violating exchangeability. In addition, interpretability and quantification of uncertainty are critically important. Here, we propose a novel class of models, a mixture of coupled hidden Markov models (M-CHMM), and demonstrate how it elegantly overcomes these challenges. To make the model learning feasible, we derive two algorithms to sample the sequences of the latent variables in the CHMM: samplers based on (i) particle filtering and (ii) factorized approximation. Compared to existing inference methods, our algorithms are computationally tractable, improve mixing, and allow for likelihood estimation, which is necessary to learn the mixture model. Experiments on challenging real-world epidemiological and semi-synthetic data demonstrate the advantages of the M-CHMM: improved data fit, capacity to efficiently handle missing and noisy measurements, improved prediction accuracy, and ability to identify interpretable subsets in the data.Description
| openaire: EC/H2020/101016775/EU//INTERVENE
Keywords
Markov Chain Monte Carlo (MCMC), Multivariate Time Series, Probabilis- tic Graphical Models, Robustness
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Poyraz, O & Marttinen, P 2023, Mixture of Coupled HMMs for Robust Modeling of Multivariate Healthcare Time Series . in Proceedings of the 3rd Machine Learning for Health Symposium . Proceedings of Machine Learning Research, vol. 225, JMLR, pp. 461-479, Machine Learning for Health Workshop, New Orleans, Louisiana, United States, 10/12/2023 . < https://proceedings.mlr.press/v225/poyraz23a/poyraz23a.pdf >