A Semi-Markov Switching Linear Gaussian Model for Censored Physiological Data

TL;DR

This paper introduces a Semi-Markov Switching Linear Gaussian Model (SSLGM) for monitoring critically ill patients, effectively capturing latent states and improving risk assessment for timely ICU transfer using censored electronic health record data.

Contribution

The paper presents a novel SSLGM and an efficient unsupervised learning algorithm tailored for censored EHR data, enhancing patient risk prediction accuracy.

Findings

SSLGM outperforms existing risk scores like Rothman index, MEWS, SOFA, and APACHE.

Model effectively captures latent clinical states and patient dynamics.

Improves timely ICU admission decisions.

Abstract

Critically ill patients in regular wards are vulnerable to unanticipated clinical dete- rioration which requires timely transfer to the intensive care unit (ICU). To allow for risk scoring and patient monitoring in such a setting, we develop a novel Semi- Markov Switching Linear Gaussian Model (SSLGM) for the inpatients' physiol- ogy. The model captures the patients' latent clinical states and their corresponding observable lab tests and vital signs. We present an efficient unsupervised learn- ing algorithm that capitalizes on the informatively censored data in the electronic health records (EHR) to learn the parameters of the SSLGM; the learned model is then used to assess the new inpatients' risk for clinical deterioration in an online fashion, allowing for timely ICU admission. Experiments conducted on a het- erogeneous cohort of 6,094 patients admitted to a large academic medical center show that the proposed model significantly outperforms the currently deployed risk scores such as Rothman index, MEWS, SOFA and APACHE.