{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T20:41:52Z","timestamp":1789591312792,"version":"build-2803163510"},"reference-count":54,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:00:00Z","timestamp":1775692800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:00:00Z","timestamp":1775692800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS\u20102021\u2010NR064239"],"award-info":[{"award-number":["RS\u20102021\u2010NR064239"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Public Health Nursing"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study aimed to identify high\u2010risk factors for type 2 diabetes and develop a machine learning (ML)\u2013based diabetes prediction model using nursing notes from home health care.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>Retrospective cohort study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Sample<\/jats:title>\n                    <jats:p>A total 7896 medical records from 1747 patients aged \u2265\u00a020 years who received home health care at a university hospital in South Korea over 10 years.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Measurements<\/jats:title>\n                    <jats:p>The patient's sociodemographic characteristics, diagnosis codes, and clinical history as well as narrative nursing notes detailing nursing assessments and descriptions of the patient's condition were recorded. Logistic regression was used to identify risk factors, and five machine learning models were constructed using 10\u2010fold cross\u2010validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Female sex, depression, hypertension, hyperlipidemia, nursing services such as dressing and nutritional care, and dysuria were identified as prediabetes\u2010related symptoms extracted from nursing notes using natural language processing (NLP). The Random Forest model demonstrated the highest predictive performance (AUC\u00a0=\u00a00.985).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Nursing documentation is a valuable resource for early diabetes risk screening. High predictive performance was achieved by integrating structured and unstructured data, prediabetes\u2010related symptoms extracted from nursing notes. This approach enhances nurses\u2019 ability to deliver timely, personalized interventions and improve preventive care outcomes, with significant benefits for patients receiving home care who require continuous support.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/phn.70122","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T03:51:52Z","timestamp":1775793112000},"page":"1043-1055","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Community\u2010Based Nursing Decision Support: Machine Learning Models for Diabetes Risk Prediction Using Home Health Nursing Notes"],"prefix":"10.1111","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1904-8783","authenticated-orcid":false,"given":"Doyeon","family":"LIM","sequence":"first","affiliation":[{"name":"College of Nursing Seoul National University  Seoul South Korea"},{"name":"Fresenius Medical Care Korea Ltd.  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