Leveraging large historical data in electronic health record (EHR), we
developed Doctor AI, a generic predictive model that covers observed medical
conditions and medication uses. Doctor AI is a temporal model using recurrent
neural networks (RNN) and was developed and applied to longitudinal time
stamped EHR data from 260K patients over 8 years. Encounter records (e.g.
diagnosis codes, medication codes or procedure codes) were input to RNN to
predict (all) the diagnosis and medication categories for a subsequent visit.
Doctor AI assesses the history of patients to make multilabel predictions (one
label for each diagnosis or medication category). Based on separate blind test
set evaluation, Doctor AI can perform differential diagnosis with up to 79%
recall@30, significantly higher than several baselines. Moreover, we
demonstrate great generalizability of Doctor AI by adapting the resulting
models from one institution to another without losing substantial accuracy.

使用大量历史数据，Doctor AI 基于循环神经网络（RNN）作为时间模型，从 260k 患者数据中预测多种医学诊断和用药类别，该模型可比几个基线模型更高效地进行差异诊断。