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.

翻译：临床事件序列由能及时反映病人护理记录的数千个临床事件组成。为这种序列制定准确的预测模型对于确定病人状况的表示方式和改善病人护理非常重要。学习临床序列的良好预测模型的一个重要挑战是病人特有的变异性。根据基本的临床并发症,每个病人的序列可能由不同的临床事件组成。然而,从这些序列中学习的人口模型可能无法准确预测病人特定事件序列的动态。为了解决这个问题,我们开发了新的适应性事件序列预测框架,通过在线模型更新,学会调整个别病人的预测。