Switching State Space Modeling via Constrained Inference for Clinical Outcome Prediction

Abstract

In clinical settings, timely and accurate prediction of adverse patient outcomes can help guide treatment decisions. While deep learning models such as LSTMs have demonstrated strong predictive performance on multivariate clinical time series, they often lack interpretability. To address this gap, this thesis proposes a framework that combines the predictive strength of neural networks with the interpretability of latent variable models. Specifically, we develop a constrained inference approach to train a switching state space model—an autoregressive hidden Markov model (AR-HMM)—for outcome prediction. Our method leverages knowledge distillation: a high-capacity LSTM "teacher" model is first trained to predict a target clinical outcome of interest, and its predictive behavior is then transferred to an interpretable AR-HMM "student" model through a similarity constraint during inference. We implement a constrained variational inference approach to estimate the parameters of the student model while aligning its latent representations with that of the teacher model’s. We evaluated our approach using two real-world clinical datasets. Our approach demonstrates predictive performance comparable to state-of-the-art deep learning models, while producing interpretable latent trajectories that reflect clinically meaningful patient states.