Back to Search Start Over

Scalable and accurate deep learning for electronic health records

Authors :
Rajkomar, Alvin
Oren, Eyal
Chen, Kai
Dai, Andrew M.
Hajaj, Nissan
Liu, Peter J.
Liu, Xiaobing
Sun, Mimi
Sundberg, Patrik
Yee, Hector
Zhang, Kun
Duggan, Gavin E.
Flores, Gerardo
Hardt, Michaela
Irvine, Jamie
Le, Quoc
Litsch, Kurt
Marcus, Jake
Mossin, Alexander
Tansuwan, Justin
Wang, De
Wexler, James
Wilson, Jimbo
Ludwig, Dana
Volchenboum, Samuel L.
Chou, Katherine
Pearson, Michael
Madabushi, Srinivasan
Shah, Nigam H.
Butte, Atul J.
Howell, Michael
Cui, Claire
Corrado, Greg
Dean, Jeff
Source :
npj Digital Medicine 1:18 (2018)
Publication Year :
2018

Abstract

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. We validated our approach using de-identified EHR data from two U.S. academic medical centers with 216,221 adult patients hospitalized for at least 24 hours. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting in-hospital mortality (AUROC across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed state-of-the-art traditional predictive models in all cases. We also present a case-study of a neural-network attribution system, which illustrates how clinicians can gain some transparency into the predictions. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios, complete with explanations that directly highlight evidence in the patient's chart.<br />Comment: Published version from https://www.nature.com/articles/s41746-018-0029-1

Details

Database :
arXiv
Journal :
npj Digital Medicine 1:18 (2018)
Publication Type :
Report
Accession number :
edsarx.1801.07860
Document Type :
Working Paper
Full Text :
https://doi.org/10.1038/s41746-018-0029-1