Deep Learning Solutions to Computational Phenotyping in Health Care

Abstract

Exponential growth in electronic health record (EHR) data has resulted in new opportunities and urgent needs to discover meaningful data-driven representations and patterns of diseases, i.e., computational phenotyping. Recent success and development of deep learning provides promising solutions to the problem of prediction and feature discovery tasks, while lots of challenges still remain and prevent people from applying standard deep learning models directly. In this paper, we discussed three key challenges in this field: how to deal with missing data, how to build scalable models, and how to get interpretations of features and models. We proposed novel and effective deep learning solutions to each of them respectively. All proposed solutions are evaluated on several real-world health care datasets and experimental results demonstrated their superiority over existing baselines.

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