SynthesisJournal of diabetes science and technology2021
Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review.
Synthesis in Journal of diabetes science and technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review.JMIR medical informatics · 2025Pooled it
- Developing a triage predictive model for access to a spinal surgeon using clinical variables and natural language processing of radiology reports.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Identifying Diabetes Related-Complications in a Real-World Free-Text Electronic Medical Records in Hebrew Using Natural Language Processing Techniques.Journal of diabetes science and technology · 2025Article
- Impact of Statin Nonacceptance on Cardiovascular Outcomes in Patients With Diabetes.Journal of the American Heart Association · 2025Article
- Patient-reported outcomes and treatment adherence in type 2 diabetes using natural language processing: Wave 8 of the Observational International Diabetes Management Practices Study.Journal of diabetes investigation · 2024Observational
- Natural language processing in the intensive care unit: A scoping review.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2024Article
- Collaborative and privacy-enhancing workflows on a clinical data warehouse: an example developing natural language processing pipelines to detect medical conditions.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Exploring the reliability of inpatient EMR algorithms for diabetes identification.BMJ health & care informatics · 2023Article
- Impact of possible errors in natural language processing-derived data on downstream epidemiologic analysis.JAMIA open · 2023Article
- Use of Natural Language Processing of Patient-Initiated Electronic Health Record Messages to Identify Patients With COVID-19 Infection.JAMA network open · 2023Article
- An automated method for developing search strategies for systematic review using Natural Language Processing (NLP).MethodsX · 2023Article
- A Comparative Study of Natural Language Processing Algorithms Based on Cities Changing Diabetes Vulnerability Data.Healthcare (Basel, Switzerland) · 2022Article
- Natural language processing for the assessment of cardiovascular disease comorbidities: The cardio-Canary comorbidity project.Clinical cardiology · 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
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundReal-world evidence research plays an increasingly important role in diabetes care. However, a large fraction of real-world data are "locked" in narrative format. Natural language processing (NLP) technology offers a solution for analysis of narrative electronic data.
methodsWe conducted a systematic review of studies of NLP technology focused on diabetes. Articles published prior to June 2020 were included.
resultsWe included 38 studies in the analysis. The majority (24; 63.2%) described only development of NLP tools; the remainder used NLP tools to conduct clinical research. A large fraction (17; 44.7%) of studies focused on identification of patients with diabetes; the rest covered a broad range of subjects that included hypoglycemia, lifestyle counseling, diabetic kidney disease, insulin therapy and others. The mean F
conclusionResearch in NLP technology to study diabetes is growing quickly, although challenges (e.g. in analysis of more linguistically complex concepts) remain. Its potential to deliver evidence on treatment and improving quality of diabetes care is demonstrated by a number of studies. Further growth in this area would be aided by deeper collaboration between developers and end-users of natural language processing tools as well as by broader sharing of the tools themselves and related resources.
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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.