Detecting Hypoglycemia Incidents Reported in Patients' Secure Messages: Using Cost-Sensitive Learning and Oversampling to Reduce Data Imbalance.

ABSTRACT:

Background

Improper dosing of medications such as insulin can cause hypoglycemic episodes, which may lead to severe morbidity or even death. Although secure messaging was designed for exchanging nonurgent messages, patients sometimes report hypoglycemia events through secure messaging. Detecting these patient-reported adverse events may help alert clinical teams and enable early corrective actions to improve patient safety.

Objective

We aimed to develop a natural language processing system, called HypoDetect (Hypoglycemia Detector), to automatically identify hypoglycemia incidents reported in patients' secure messages.

Methods

An expert in public health annotated 3000 secure message threads between patients with diabetes and US Department of Veterans Affairs clinical

SUBMITTER: Chen J 

PROVIDER: S-EPMC6431826 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature