{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T10:58:50Z","timestamp":1790852330569,"version":"4.1.0"},"reference-count":17,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01HG011181"],"award-info":[{"award-number":["U01HG011181"]}],"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":["R01HL171809"],"award-info":[{"award-number":["R01HL171809"]}],"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":["P50HD106446"],"award-info":[{"award-number":["P50HD106446"]}],"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":["R01AG084550"],"award-info":[{"award-number":["R01AG084550"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Precision medicine requires delivery and tracking of genome-informed risk assessments (GIRAs), often documented in unstructured electronic health record (EHR) notes, making large-scale evaluation reliant on labor-intensive manual chart review. Large language models (LLMs) offer a promising approach to automated extraction, but their performance in genome-informed clinical contexts remains incompletely characterized.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To evaluate guideline-grounded LLM approaches for extracting genome-informed clinical recommendations from EHR notes in the eMERGE study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>We developed an LLM-based pipeline to identify clinical recommendations associated with GIRA reports. Three LLMs (GPT-4o, LLaMA-3.3-70B, and LLaMA-3.1-8B) were evaluated using baseline prompting, guideline-aware prompting, and retrieval-augmented generation (RAG), validated against manual chart review (N\u2009=\u200918 participants; N\u2009=\u200934 documents).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Baseline prompting showed limited performance (F1\u2009=\u20090.50-0.54). Incorporating guideline knowledge improved F1 by 0.14-0.31 across models, with LLaMA-3.1-8B using RAG achieving the highest performance (F1\u2009=\u20090.81).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Domain-grounded LLM approaches can support scalable extraction of genome-informed clinical recommendations from EHR data. Larger studies are needed to assess generalizability in real-world workflows.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocag171","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T12:04:25Z","timestamp":1789473865000},"source":"Crossref","is-referenced-by-count":0,"title":["Guideline-grounded large language models for extracting genome-informed clinical recommendations from electronic health records"],"prefix":"10.1093","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7983-8277","authenticated-orcid":false,"given":"Yi","family":"Xin","sequence":"first","affiliation":[{"name":"Department of Computer Science, Vanderbilt University , Nashville, TN 37235,","place":["United States"]},{"name":"Department of Biomedical Informatics, Vanderbilt University Medical Center , Nashville, TN 37203,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyle 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