JMIR Publications

ABSTRACT

Background:

Accurately identifying patients with hypoglycemia is key in preventing adverse events and mortality. Natural language processing (NLP), a form of artificial intelligence, uses computational algorithms to extract information from text data. NLP is a scalable, efficient, and quick method to extract hypoglycemia related information when using electronic health records resources (EHRs) from a large population.

Objective:

The objective of this systematic review was to synthesize the literature on the application of NLP to extract hypoglycemia from EHR clinical notes.

Methods:

Literature searches were conducted electronically from PubMed, Web of Science Core Collection, CINAHL (EBSCO), PsycINFO (Ovid), IEEEXplore, GoogleScholar, and ACL Anthology. Keywords included hypoglycemia, low blood glucose, NLP, and machine learning. Inclusion criteria included studies that applied NLP to identify hypoglycemia, reported the outcomes related to hypoglycemia, and were published in English as full papers.

Results:

This review (N=8 studies) revealed heterogeneity of the reported results related to hypoglycemia. Four studies reported that the prevalence of any level of hypoglycemia was 3.4 - 46.2%. The use of NLP to analyze clinical notes improved the capture of undocumented or missed hypoglycemic events using ICD-9/10 codes or lab testing. The combination of NLP and ICD-9/10 codes significantly increased the identification of hypoglycemic events compared with individual methods, for example the prevalence of hypoglycemia was 12.4% for ICD codes, 25.1% for NLP algorithm, and 32.2% for combined algorithms. All 8 reviewed studies applied rule-based NLP algorithms to identify hypoglycemia.

Conclusions:

The findings provided evidence that the application of NLP to analyze clinical notes improved the capture of hypoglycemic events, particularly when combined with the ICD-9/10 codes and lab testing.