ArticleDiabetes care2020
Natural Language Processing Improves Detection of Nonsevere 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.
Article in Diabetes care, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it, 12 citations in OpenAlex.
- Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice.Frontiers in public health · 2023Pooled it
- Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2021Pooled it
- Artificial Intelligence-Assisted Clinical Decision-Making: A Perspective on Advancing Personalized Precision Medicine for Elderly Diabetes Patients.Journal of multidisciplinary healthcare · 2025Article
- Identifying Patients With Hypoglycemia Using Natural Language Processing: Systematic Literature Review.JMIR diabetes · 2022Review
- Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients.NPJ digital medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveTo determine if natural language processing (NLP) improves detection of nonsevere 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
methodsFrom 2005 to 2017, we identified NSH events by diagnosis codes and NLP. We then built an SH prediction model.
resultsThere were 204,517 patients with type 2 diabetes and no diagnosis codes for NSH. Evidence of NSH was found in 7,035 (3.4%) of patients using NLP. We reviewed 1,200 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;
conclusionsDetection of NSH improved with NLP in patients with type 2 diabetes without improving SH prediction.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.