Mixture of Coupled HMMs for Robust Modeling of Multivariate Healthcare Time Series

TL;DR

This paper introduces a mixture of coupled hidden Markov models (M-CHMM) designed to effectively model complex multivariate healthcare time series data, addressing challenges like irregular sampling, noise, and heterogeneity while providing interpretability and uncertainty quantification.

Contribution

The paper proposes a novel M-CHMM framework with new inference algorithms based on particle filtering and factorized approximation for scalable, interpretable modeling of healthcare time series.

Findings

Improved data fit and prediction accuracy on real-world epidemiological data

Enhanced handling of missing and noisy measurements

Ability to identify interpretable patient data subsets

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.