ForecastICU: A Prognostic Decision Support System for Timely Prediction of Intensive Care Unit Admission

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Jinsung Yoon, Ahmed Alaa, Scott Hu, Mihaela Schaar ;
Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1680-1689, 2016.

Abstract

We develop ForecastICU: a prognostic decision support system that monitors hospitalized patients and prompts alarms for intensive care unit (ICU) admissions. ForecastICU is first trained in an offline stage by constructing a Bayesian belief system that corresponds to its belief about how trajectories of physiological data streams of the patient map to a clinical status. After that, ForecastICU monitors a new patient in real-time by observing her physiological data stream, updating its belief about her status over time, and prompting an alarm whenever its belief process hits a predefined threshold (confidence). Using a real-world dataset obtained from UCLA Ronald Reagan Medical Center, we show that ForecastICU can predict ICU admissions 9 hours before a physician’s decision (for a sensitivity of 40% and a precision of 50%). Also, ForecastICU performs consistently better than other state-of-the-art machine learning algorithms in terms of sensitivity, precision, and timeliness: it can predict ICU admissions 3 hours earlier, and offers a 7.8% gain in sensitivity and a 5.1% gain in precision compared to the best state-of-the-art algorithm. Moreover, ForecastICU offers an area under curve (AUC) gain of 22.3% compared to the Rothman index, which is the currently deployed technology in most hospital wards.

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