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arXiv · 1611.05146

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

Ahmed M. Alaa·Jinsung Yoon·Scott Hu·Mihaela van der Schaar
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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.

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Ahmed M. Alaa, Jinsung Yoon, Scott Hu, Mihaela van der Schaar. 2016-11-16. A Semi-Markov Switching Linear Gaussian Model for Censored Physiological Data. https://arxiv.org/abs/1611.05146

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