Identifying Patients With Hypoglycemia Using Natural Language Processing: Systematic Literature Review Article Swipe
Yaguang Zheng
,
Victoria Vaughan Dickson
,
Saul Blecker
,
Jason M. Ng
,
Brynne Campbell
,
Gail D’Eramo Melkus
,
Liat Shenkar
,
Marie Claire R Mortejo
,
Stephen B. Johnson
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.2196/34681
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.2196/34681
Background Accurately identifying patients with hypoglycemia is key to 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 record data sources 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 electronic health record clinical notes. Methods Literature searches were conducted electronically in PubMed, Web of Science Core Collection, CINAHL (EBSCO), PsycINFO (Ovid), IEEE Xplore, Google Scholar, 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. Of the 8 included studies, 4 (50%) reported that the prevalence rate of any level of hypoglycemia was 3.4% to 46.2%. The use of NLP to analyze clinical notes improved the capture of undocumented or missed hypoglycemic events using International Classification of Diseases, Ninth Revision (ICD-9), and International Classification of Diseases, Tenth Revision (ICD-10), and laboratory testing. The combination of NLP and ICD-9 or ICD-10 codes significantly increased the identification of hypoglycemic events compared with individual methods; for example, the prevalence rates of hypoglycemia were 12.4% for International Classification of Diseases codes, 25.1% for an NLP algorithm, and 32.2% for combined algorithms. All the 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 or ICD-10 codes and laboratory testing.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.2196/34681
- https://diabetes.jmir.org/2022/2/e34681/PDF
- OA Status
- gold
- Cited By
- 9
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4226069305
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4226069305Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2196/34681Digital Object Identifier
- Title
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Identifying Patients With Hypoglycemia Using Natural Language Processing: Systematic Literature ReviewWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-04-08Full publication date if available
- Authors
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Yaguang Zheng, Victoria Vaughan Dickson, Saul Blecker, Jason M. Ng, Brynne Campbell, Gail D’Eramo Melkus, Liat Shenkar, Marie Claire R Mortejo, Stephen B. JohnsonList of authors in order
- Landing page
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https://doi.org/10.2196/34681Publisher landing page
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https://diabetes.jmir.org/2022/2/e34681/PDFDirect link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://diabetes.jmir.org/2022/2/e34681/PDFDirect OA link when available
- Concepts
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Hypoglycemia, PsycINFO, Medicine, Artificial intelligence, CINAHL, Natural language processing, Machine learning, MEDLINE, Computer science, Pediatrics, Internal medicine, Psychiatry, Law, Insulin, Political science, Psychological interventionTop concepts (fields/topics) attached by OpenAlex
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9Total citation count in OpenAlex
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2025: 1, 2024: 3, 2023: 4, 2022: 1Per-year citation counts (last 5 years)
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39Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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