{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T06:02:05Z","timestamp":1772690525601,"version":"3.50.1"},"reference-count":50,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T00:00:00Z","timestamp":1753660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01GM140012"],"award-info":[{"award-number":["R01GM140012"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01DE030656"],"award-info":[{"award-number":["R01DE030656"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01GM115473"],"award-info":[{"award-number":["R01GM115473"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01CA249245"],"award-info":[{"award-number":["U01CA249245"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01AI169298"],"award-info":[{"award-number":["U01AI169298"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R35GM136375"],"award-info":[{"award-number":["R35GM136375"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Cancer Prevention and Research Institute","award":["RP180805"],"award-info":[{"award-number":["RP180805"]}]},{"name":"Cancer Prevention and Research Institute","award":["RP240521"],"award-info":[{"award-number":["RP240521"]}]},{"name":"Cancer Prevention and Research Institute","award":["RP230330"],"award-info":[{"award-number":["RP230330"]}]},{"name":"Health Resources Clinical Scholars Program"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>Social and behavioral determinants of health (SBDH) are increasingly recognized as essential for prognostication and informing targeted interventions. Clinical notes often contain details about SBDH in unstructured format. Conventional extraction methods for these data tend to be labor intensive, inaccurate, and\/or unscalable. In this study, we aim to develop and validate a large language model (LLM)-powered method to extract structured SBDH data from clinical notes through prompt engineering.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>We developed SBDH-Reader to extract 6 categories of granular SBDH data by prompting GPT-4o, including employment, housing, marital status, and substance use including alcohol, tobacco, and drug use. SBDH-Reader was developed using 7225 notes from 6382 patients in the MIMIC-III database (2001\u20132012) and externally validated using 971 notes from 437 patients at The University of Texas Southwestern Medical Center (UTSW; 2022\u20132023). We evaluated SBDH-Reader\u2019s performance against human-annotated ground truths based on precision, recall, F1, and confusion matrix.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>When tested on the UTSW validation set, SBDH-Reader achieved a macro-average F1 ranging from 0.94 to 0.98 across 6 SBDH categories. For clinically relevant adverse attributes, F1 ranged from 0.96 (employment; housing) to 0.99 (tobacco use). When extracting any adverse attributes across all SBDH categories, SBDH-Reader achieved an F1 of 0.97, recall of 0.97, and precision of 0.98 in the independent validation set.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion<\/jats:title>\n                  <jats:p>SBDH-Reader demonstrated strong performance in extracting structured SBDH data through effective prompt engineering of a general-purpose LLM, without the need for task-specific fine-tuning. Its modular design and adaptability to diverse datasets and documentation patterns support its applicability in real-world clinical settings.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>SBDH-Reader has the potential to serve as a scalable and effective method for collecting real-time, patient-level SBDH data to support clinical research and care.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocaf124","type":"journal-article","created":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T14:11:16Z","timestamp":1752502276000},"page":"1570-1580","source":"Crossref","is-referenced-by-count":3,"title":["SBDH-Reader: a large language model-powered method for extracting social and behavioral determinants of health from clinical notes"],"prefix":"10.1093","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8024-2629","authenticated-orcid":false,"given":"Zifan","family":"Gu","sequence":"first","affiliation":[{"name":"Quantitative Biomedical Research Center, Department of Health Data Science and Biostatistics, Peter O\u2019Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center , Dallas, TX 75390,","place":["United States"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5732-4220","authenticated-orcid":false,"given":"Lesi","family":"He","sequence":"additional","affiliation":[{"name":"Department of Health Data Science and Biostatistics, Peter O\u2019Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center , Dallas, TX 75390,","place":["United States"]}]},{"given":"Awais","family":"Naeem","sequence":"additional","affiliation":[{"name":"School of Information, University of Texas at Austin , Austin, TX 78712,","place":["United 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Public Health, The University of Texas Southwestern Medical Center , Dallas, TX 75390,","place":["United States"]},{"name":"Department of Bioinformatics, The University of Texas Southwestern Medical Center , Dallas, TX 75390,","place":["United States"]},{"name":"Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center , Dallas, TX 75390,","place":["United States"]}]},{"given":"Eric D","family":"Peterson","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, The University of Texas Southwestern Medical Center , Dallas, TX 75390,","place":["United States"]}]},{"given":"Yang","family":"Xie","sequence":"additional","affiliation":[{"name":"Quantitative Biomedical Research Center, Department of Health Data Science and Biostatistics, Peter O\u2019Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center , Dallas, TX 75390,","place":["United States"]},{"name":"Department of Bioinformatics, The University 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