Skip to content

Google Research

Catalog entries using this tag (links open the entry card on its page):

Entries

Deep EHR

AI Clinical EHR Deep Learning Google Research Digital Medicine
PUBMED_LINK
31304366
FULL NAME
Scalable and Accurate Deep Learning with Electronic Health Records
DESCRIPTION
Pioneering deep learning framework for EHR data by Google Research, using Fast Healthcare Interoperability Resources (FHIR) format to represent patients' raw EHR records. Trained on 216,221 patients across 2 US medical centers with 46.8 billion data points including clinical notes. Achieved AUROC 0.93-0.94 for in-hospital mortality, 0.75-0.76 for 30-day readmission, and 0.90 for final discharge diagnoses, outperforming traditional clinical predictive models. 2,800+ citations, widely considered the landmark paper for deep learning on EHR data.
TITLE
Scalable and accurate deep learning with electronic health records.
Main citation
Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, Liu PJ, Liu X, Marcus J, Sun M, Sundberg P, Yee H, Zhang K, Zhang Y, Flores G, Duggan GE, Irvine J, Le Q, Litsch K, Mossin A, Tansuwan J, Wang D, Wexler J, Wilson J, Ludwig D, Volchenboum SL, Chou K, Pearson M, Madabushi S, Shah NH, Butte AJ, Howell MD, Cui C, Corrado GS, Dean J. (2018) Scalable and accurate deep learning with electronic health records. npj Digital Medicine, 1:18. doi:10.1038/s41746-018-0029-1. PMID 31304366
ABSTRACT
Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. 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. Deep learning models achieved high accuracy for predicting in-hospital mortality (AUROC 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 (AUROC 0.90).
DOI
10.1038/s41746-018-0029-1