  Clinical event sequences consist of thousands of clinical events that
represent records of patient care in time. Developing accurate prediction
models for such sequences is of a great importance for defining representations
of a patient state and for improving patient care. One important challenge of
learning a good predictive model of clinical sequences is patient-specific
variability. Based on underlying clinical complications, each patient's
sequence may consist of different sets of clinical events. However,
population-based models learned from such sequences may not accurately predict
patient-specific dynamics of event sequences. To address the problem, we
develop a new adaptive event sequence prediction framework that learns to
adjust its prediction for individual patients through an online model update.
