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Prediction of physiologic states are important in medical practice becauseinterventions are guided by predicted impacts of interventions. But predictionis difficult in medicine because the generating system is complex and difficultto understand from data alone, and the data are sparse relative to thecomplexity of the generating processes due to human costs of data collection.Computational machinery can potentially make prediction more accurate, but,working within the constraints of realistic clinical data makes robustinference difficult because the data are sparse, noisy and nonstationary. Thispaper focuses on prediction given sparse, non-stationary, electronic healthrecord data in the intensive care unit (ICU) using data assimilation, a broadcollection of methods that pairs mechanistic models with inference machinerysuch as the Kalman filter. We find that to make inference with sparse clinicaldata accurate and robust requires advancements beyond standard DA methodscombined with additional machine learning methods. Specifically, we show thatcombining the newly developed constrained ensemble Kalman filter with machinelearning methods can produce substantial gains in robustness and accuracy whileminimizing the data requirements. We also identify limitations of Kalmanfiltering methods that lead to new problems to be overcome to make inferencefeasible in clinical settings using realistic clinical data.

Interpretable Forecasting of Physiology in the ICU Using Constrained Data Assimilation and Electronic Health Record Data