Misra_Hebert_NLP_Hypo_Model_Brief_Report_online_appendix_rev3.docx (215.2 kB)

Natural Language Processing Improves Detection of Non-Severe Hypoglycemia in Medical Records versus Coding Alone in Patients with Type 2 Diabetes but does not Improve Prediction of Severe Hypoglycemia Events: An Analysis Using the Electronic Medical Record in a Large Health System

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posted on 15.05.2020 by Anita D. Misra-Hebert, Alex Milinovich, Alex Zajichek, Xinge Ji, Todd D. Hobbs, Wayne Weng, Paul Petraro, Sheldon X. Kong, Michelle Mocarski, Rahul Ganguly, Janine M. Bauman, Kevin M. Pantalone, Robert S. Zimmerman, Michael W. Kattan
Objective: To determine if natural language processing (NLP) improves detection of non-severe hypoglycemia (NSH) in patients with type 2 diabetes and no NSH documentation by diagnosis codes, and to measure if NLP detection improves the prediction of future severe hypoglycemia (SH).

Research Design and Methods: From 2005-2017, we identified NSH events by diagnosis codes and NLP. We then built an SH prediction model.

Results: There were 204,517 patients with type 2 diabetes and no diagnosis codes for NSH. Evidence of of NSH was found in 7035 (3.4%) using NLP. We reviewed 1200 of the NLP-detected NSH notes and confirmed 93% to have NSH. The SH prediction model (C-statistic 0.806) showed increased risk with NSH (Hazard Ratio=4.44, p<0.001). However the model with NLP did not improve SH prediction compared to diagnosis code-only NSH.

Conclusions: Detection of NSH improved with NLP in patients with type 2 diabetes without improving SH prediction.


This study was funded by Novo Nordisk, Inc.