{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:03:43Z","timestamp":1755219823181,"version":"3.43.0"},"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>Catatonia, a complex syndrome with diagnostic challenges, was studied using a novel approach combining LightGBM and GPT-4 to enhance phenotyping from electronic health record (EHR) data. LightGBM, trained on structured data, achieved superior performance (AUROC 0.713) compared to GPT-4 (best AUROC 0.709 with retrieval-augmented generation (RAG) few-shot and ML predictions). While LightGBM excelled in raw metrics, GPT-4 added value through enhanced interpretability, effectively analyzing contextual information like the Bush-Francis Catatonia Rating Scale from clinical notes. This integration demonstrates the potential of combining ML and GPT-4 to improve catatonia identification and phenotyping accuracy.<\/jats:p>","DOI":"10.3233\/shti250915","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:34:57Z","timestamp":1754566497000},"source":"Crossref","is-referenced-by-count":0,"title":["Integrating Large Language Models and Machine Learning for Enhanced Catatonia Phenotyping: A Study on Clinical Data from Electronic Health Records"],"prefix":"10.3233","author":[{"given":"Yubo","family":"Feng","sequence":"first","affiliation":[{"name":"Vanderbilt University, Nashville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruiyan","family":"Ma","sequence":"additional","affiliation":[{"name":"Vanderbilt University, Nashville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinmeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Vanderbilt University, Nashville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"You","family":"Chen","sequence":"additional","affiliation":[{"name":"Vanderbilt University, Nashville, TN, USA"},{"name":"Vanderbilt University Medical Center, Nashville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"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\/SHTI250915","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:34:57Z","timestamp":1754566497000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250915"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250915","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]]}}}