Unfolding physiological state: mortality modelling in intensive care units

Abstract

Accurate knowledge of a patient’s disease state and trajec-tory is critical in a clinical setting. Modern electronic health-care records contain an increasingly large amount of data, and the ability to automatically identify the factors that influence patient outcomes stand to greatly improve the ef-ficiency and quality of care. We examined the use of latent variable models (viz. La-tent Dirichlet Allocation) to decompose free-text hospital notes into meaningful features, and the predictive power of these features for patient mortality. We considered three prediction regimes: (1) baseline prediction, (2) dynamic (time-varying) outcome prediction, and (3) retrospective outcome prediction. In each, our prediction task differs from the familiar time-varying situation whereby data accumulates;