Deep EHR Spotlight: a Framework and Mechanism to Highlight Events in Electronic Health Records for Explainable Predictions

Thanh Nguyen-Duc, Natalia Mulligan, Joao H Bettencourt-Silva

Abstract:

The wide adoption of Electronic Health Records (EHR) has resulted in large amounts of clinical data becoming
available, which promises to support service delivery and advance clinical and informatics research. Deep learning
techniques have demonstrated performance in predictive analytic tasks using EHRs yet they typically lack model result
transparency or explainability functionalities and require cumbersome pre-processing tasks. Moreover, EHRs contain
heterogeneous and multi-modal data points such as text, numbers and time series which further hinder visualisation
and interpretability. This paper proposes a deep learning framework to: 1) encode patient pathways from EHRs into
images, 2) highlight important events within pathway images, and 3) enable more complex predictions with additional
intelligibility. The proposed method relies on a deep attention mechanism for visualisation of the predictions and
allows predicting multiple sequential outcomes