Dynamic Time Varying Models for Predicting Patient Deterioration

Steorts, Rebecca C

McCreanor, Reuben Knowles

2017-08-16T18:26:15Z

2019-05-23T08:17:10Z

2017

Statistical and Economic Modeling

Streaming data are becoming more common in a variety of fields. One common data stream in clinical medicine is electronic health records (EHRs) which have been used to develop risk prediction models. Our motivating application considers the risk of patient deterioration, which is defined as in-hospital mortality or transfer to the Intensive Care Unit (ICU). Duke University Hospital recently implemented an alert risk score for acute care wards: the National Early Warning Score (NEWS). However, NEWS was designed to be hand-calculable from patient vital data rather than to optimize prediction. Our approach considers three further methods to use on real-time EHR data to predict patient deterioration. We propose a Cox model, a joint modeling approach, and a Gaussian process. By evaluating the implementation of these models on clinical EHR data from more than 51,000 patients, we are able to provide a comparison of the methods on real EHR data for patient deterioration. We evaluate the results on both performance and scalability and consider the feasibility of implementing each approach in a clinical environment. While the more complicated models may potentially offer a small gain in predictive performance, they do not scale to a full patient data set. Thus, within a clinical setting, the Cox model is clearly the best approach.

https://hdl.handle.net/10161/15271

Statistics

Health care management

Big data

Hospital

Machine learning

Patient

Prediction

Risk

Dynamic Time Varying Models for Predicting Patient Deterioration

Master's thesis

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