Doctor AI: Predicting Clinical Events via Recurrent Neural Networks

Edward Choi, Mohammad Taha Bahadori, Andy Schuetz, Walter F. Stewart, Joshua C. Denny, Bradley A. Malin, Jimeng Sun

Abstract:

Large amount of Electronic Health Record (EHR) data have been collected over millions of patients over multiple years. The rich longitudinal EHR data documented the collective experiences of physicians including diagnosis, medication prescription and procedures. We argue it is possible now to leverage the EHR data to model how physicians behave, and we call our model Doctor AI.
Towards this direction of modeling clinical behavior of physicians, we develop a successful application of Recurrent Neural Networks (RNN) to jointly forecast the future disease diagnosis and medication prescription along with their timing. Unlike traditional classification models where a single target is of interest, our model can assess the entire history of patients and make continuous and multilabel predictions based on patients' historical data. We evaluate the performance of the proposed method on a large real-world EHR data over 260K patients over 8 years.
We observed Doctor AI can perform differential diagnosis with similar accuracy to physicians. In particular, Doctor AI achieves up to 79% recall@30, significantly higher than several baselines. Moreover, we demonstrate great generalizability of Doctor AI by applying the resulting models on data from a completely different medication institution achieving comparable performance.

Conflicts: usc.edu, gatech.edu, vanderbilt.edu, sutterhealth.org