Abstract
Exponential growth in mobile health devices and electronic health records has resulted in a surge of large-scale time series data, which demands effective and fast machine learning models for analysis and discovery. In this chapter, we discuss a novel framework based on deep learning which automatically performs feature learning from heterogeneous time series data. It is well-suited for healthcare applications, where available data have many sparse outputs (e.g., rare diagnoses) and exploitable structures (e.g., temporal order and relationships between labels). Furthermore, we introduce a simple yet effective knowledge-distillation approach to learn an interpretable model while achieving the prediction performance of deep models. We conduct experiments on several real-world datasets and show the empirical efficacy of our framework and the interpretability of the mimic models.
Zhengping Che
X-Humanoid
Sanjay Purushotham
University of Maryland Baltimore County
