{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T12:18:17Z","timestamp":1771330697902,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686080","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>This study explored the potential of LLMs, such as ClinicalBERT and GPT-4, to identify potential diagnoses using early clinical notes from the MIMIC-III dataset. We compared these models across four conditions: circulatory system diseases, respiratory system diseases, septicemia, and pneumonia. ClinicalBERT consistently outperformed the GPT models, with its highest F1-score of 0.952 for respiratory system diseases. The GPT models, while showing high recall, had lower precision, with the highest F1-score of 0.784 achieved by the GPT binary voting method. ClinicalBERT demonstrated strong precision and F1-scores, while GPT-4 excelled in recall.<\/jats:p>","DOI":"10.3233\/shti251241","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:45:42Z","timestamp":1754567142000},"source":"Crossref","is-referenced-by-count":2,"title":["Leveraging LLMs for Early Diagnosis in the Emergency Department: Comparing ClinicalBERT and GPT-4"],"prefix":"10.3233","author":[{"given":"Wanting","family":"Cui","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, University of Utah, Salt Lake City, UT"}]},{"given":"Joseph","family":"Finkelstein","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, University of Utah, Salt Lake City, UT"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI251241","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:45:43Z","timestamp":1754567143000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI251241"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti251241","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}