Personalized Event Prediction for Electronic Health Records
Jeong Min Lee Milos Hauskrecht
Clinical event sequences consist of hundreds of clinical events that
represent records of patient care in time. Developing accurate predictive
models of such sequences is of a great importance for supporting a variety of
models for interpreting/classifying the current patient condition, or
predicting adverse clinical events and outcomes, all aimed to improve patient
care. One important challenge of learning predictive models of clinical
sequences is their patient-specific variability. Based on underlying clinical
conditions, each patient's sequence may consist of different sets of clinical
events (observations, lab results, medications, procedures). Hence, simple
population-wide models learned from event sequences for many different patients
may not accurately predict patient-specific dynamics of event sequences and
their differences. To address the problem, we propose and investigate multiple
new event sequence prediction models and methods that let us better adjust the
prediction for individual patients and their specific conditions. The methods
developed in this work pursue refinement of population-wide models to
subpopulations, self-adaptation, and a meta-level model switching that is able
to adaptively select the model with the best chance to support the immediate
prediction. We analyze and test the performance of these models on clinical
event sequences of patients in MIMIC-III database.