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
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.