The availability of a large amount of electronic health records (EHR)
provides huge opportunities to improve health care service by mining these
data. One important application is clinical endpoint prediction, which aims to
predict whether a disease, a symptom or an abnormal lab test will happen in the
future according to patients' history records. This paper develops deep
learning techniques for clinical endpoint prediction, which are effective in
many practical applications. However, the problem is very challenging since
patients' history records contain multiple heterogeneous temporal events such
as lab tests, diagnosis, and drug administrations. The visiting patterns of
different types of events vary significantly, and there exist complex nonlinear
relationships between different events. In this paper, we propose a novel model
for learning the joint representation of heterogeneous temporal events. The
model adds a new gate to control the visiting rates of different events which
effectively models the irregular patterns of different events and their
nonlinear correlations. Experiment results with real-world clinical data on the
tasks of predicting death and abnormal lab tests prove the effectiveness of our
proposed approach over competitive baselines.

本文研究了基于深度学习技术的临床终点预测方法，提出了一种用于学习不同类型时间事件的联合表示的新模型，并在真实世界的临床数据上对死亡和异常实验室检测的预测任务进行了实验，证明了我们提出的方法的有效性。