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

Doctor AI: Predicting Clinical Events via Recurrent Neural Networks