3 research outputs found
Scalable and accurate deep learning for electronic health records
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.Comment: Published version from
https://www.nature.com/articles/s41746-018-0029-
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Scalable and accurate deep learning with electronic health records.
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 US academic medical centers with 216,221 adult patients hospitalized for at least 24 h. 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 (area under the receiver operator curve [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 traditional, clinically-used predictive models in all cases. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios. In a case study of a particular prediction, we demonstrate that neural networks can be used to identify relevant information from the patient's chart