Scalable and accurate deep learning for electronic health records

Understand

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality.

• Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record.
• We propose a representation of patients' entire, raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format.
• We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization.