ArticleJournal of diabetes science and technology2025
Identifying Diabetes Related-Complications in a Real-World Free-Text Electronic Medical Records in Hebrew Using Natural Language Processing Techniques.
Article in Journal of diabetes science and technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- Personalised Approach to the Management of Older People with Type 2 Diabetes Mellitus-A Comprehensive Narrative Review.Journal of personalized medicine · 2026Review
- Using natural language processing to identify patterns associated with depression, anxiety, and stress symptoms during the COVID-19 pandemic.Journal of affective disorders · 2025Article
- Artificial Intelligence-Assisted Clinical Decision-Making: A Perspective on Advancing Personalized Precision Medicine for Elderly Diabetes Patients.Journal of multidisciplinary healthcare · 2025Article
- Artificial Intelligence to Diagnose Complications of Diabetes.Journal of diabetes science and technology · 2025Review
- DR-GPT: A large language model for medical report analysis of diabetic retinopathy patients.PloS one · 2024Article
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Authors and funding
12 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStudies have demonstrated that 50% to 80% of patients do not receive an International Classification of Diseases (ICD) code assigned to their medical encounter or condition. For these patients, their clinical information is mostly recorded as unstructured free-text narrative data in the medical record without standardized coding or extraction of structured data elements. Leumit Health Services (LHS) in collaboration with the Israeli Ministry of Health (MoH) conducted this study using electronic medical records (EMRs) to systematically extract meaningful clinical information about people with diabetes from the unstructured free-text notes.
objectivesTo develop and validate natural language processing (NLP) algorithms to identify diabetes-related complications in the free-text medical records of patients who have LHS membership.
methodsThe study data included 2.3 million records of 41 469 patients with diabetes aged 35 or older between the years 2012 and 2017. The diabetes related complications included cardiovascular disease, diabetic neuropathy, nephropathy, retinopathy, diabetic foot, cognitive impairments, mood disorders and hypoglycemia. A vocabulary list of terms was determined and adjudicated by two physicians who are experienced in diabetes care board certified diabetes specialist in endocrinology or family medicine. Two independent registered nurses with PhDs reviewed the free-text medical records. Both rule-based and machine learning techniques were used for the NLP algorithm development. Precision, recall, and
resultsThe NLP algorithm versus the reviewers (gold standard) achieved an overall good performance with a mean
conclusionNLP algorithms and machine learning processes may enable more accurate identification of diabetes complications in EMR data.
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