  In the realm of big data and digital healthcare, Electronic Health Records
(EHR) have become a rich source of information with the potential to improve
patient care and medical research. In recent years, machine learning models
have proliferated for analyzing EHR data to predict patients future health
conditions. Among them, some studies advocate for multi-task learning (MTL) to
jointly predict multiple target diseases for improving the prediction
performance over single task learning. Nevertheless, current MTL frameworks for
EHR data have significant limitations due to their heavy reliance on human
experts to identify task groups for joint training and design model
architectures. To reduce human intervention and improve the framework design,
we propose an automated approach named AutoDP, which can search for the optimal
configuration of task grouping and architectures simultaneously. To tackle the
vast joint search space encompassing task combinations and architectures, we
employ surrogate model-based optimization, enabling us to efficiently discover
the optimal solution. Experimental results on real-world EHR data demonstrate
the efficacy of the proposed AutoDP framework. It achieves significant
performance improvements over both hand-crafted and automated state-of-the-art
methods, also maintains a feasible search cost at the same time.
