{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T02:47:09Z","timestamp":1776394029497,"version":"3.51.2"},"reference-count":10,"publisher":"Cambridge University Press (CUP)","issue":"9","license":[{"start":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T00:00:00Z","timestamp":1753920000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Infect. Control Hosp. Epidemiol."],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>There is a clinical need to appropriately apply large language model (LLM)-based systems for use in infectious diseases. We sought to use LLM and machine learning for extracting antibiotic susceptibility from clinical microbiology free-text reports, allowing use for outbreak detection, increasing information gathering efficiency, and public health reporting.<\/jats:p>","DOI":"10.1017\/ice.2025.10210","type":"journal-article","created":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T07:43:58Z","timestamp":1753947838000},"page":"941-943","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":2,"title":["Extracting antibiotic susceptibility from free-text microbiology reports using natural language processing"],"prefix":"10.1017","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2520-6959","authenticated-orcid":false,"given":"Andrew","family":"Chou","sequence":"first","affiliation":[{"name":"VA Connecticut Healthcare System"},{"name":"Yale School of Medicine"},{"name":"Yale School of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ronald George","family":"Hauser","sequence":"additional","affiliation":[{"name":"VA Connecticut Healthcare System"},{"name":"Yale School of Medicine"},{"name":"Yale School of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lori A.","family":"Bastian","sequence":"additional","affiliation":[{"name":"VA Connecticut Healthcare System"},{"name":"Yale School of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8179-1796","authenticated-orcid":false,"given":"Cynthia A.","family":"Brandt","sequence":"additional","affiliation":[{"name":"VA Connecticut Healthcare System"},{"name":"Yale School of Medicine"},{"name":"Yale School of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7986-9099","authenticated-orcid":false,"given":"Barbara W.","family":"Trautner","sequence":"additional","affiliation":[{"name":"Michael E. DeBakey VA Medical Center"},{"name":"Baylor College of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2025,7,31]]},"reference":[{"key":"S0899823X25102109_ref2","doi-asserted-by":"publisher","DOI":"10.1093\/infdis\/jiae348"},{"key":"S0899823X25102109_ref3","first-page":"1","article-title":"Performance of a large language model for identifying central line-associated bloodstream infections (CLABSI) using real clinical notes","volume":"46","author":"Rodriguez-Nava","year":"2024","journal-title":"Infect Control Hosp Epidemiol"},{"key":"S0899823X25102109_ref9","doi-asserted-by":"publisher","DOI":"10.21203\/rs.3.rs-4240043\/v1"},{"key":"S0899823X25102109_ref6","doi-asserted-by":"publisher","DOI":"10.1017\/ice.2024.79"},{"key":"S0899823X25102109_ref5","doi-asserted-by":"publisher","DOI":"10.1186\/1472-6947-12-34"},{"key":"S0899823X25102109_ref1","doi-asserted-by":"publisher","DOI":"10.1093\/cid\/ciad635"},{"key":"S0899823X25102109_ref10","doi-asserted-by":"publisher","DOI":"10.1093\/cid\/ciae446"},{"key":"S0899823X25102109_ref4","doi-asserted-by":"publisher","DOI":"10.1093\/jamiaopen\/ooac026"},{"key":"S0899823X25102109_ref8","first-page":"1234","article-title":"BioBERT: a pre-trained biomedical language representation model for biomedical text mining","volume":"36","author":"Lee","year":"2020","journal-title":"Bioinf"},{"key":"S0899823X25102109_ref7","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-54515-w"}],"container-title":["Infection Control &amp; Hospital Epidemiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0899823X25102109","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T02:00:08Z","timestamp":1776391208000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0899823X25102109\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,31]]},"references-count":10,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["S0899823X25102109"],"URL":"https:\/\/doi.org\/10.1017\/ice.2025.10210","relation":{},"ISSN":["0899-823X","1559-6834"],"issn-type":[{"value":"0899-823X","type":"print"},{"value":"1559-6834","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,31]]},"assertion":[{"value":"\u00a9 US Department of Veterans Affairs, 2025","name":"copyright","label":"Copyright","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This is a work of the US Government and is not subject to copyright protection within the United States. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America.","name":"license","label":"License","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http:\/\/creativecommons.org\/licenses\/by\/4.0\/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.","name":"license","label":"License","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}