{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:30:25Z","timestamp":1724459425442},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685335","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"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":[[2024,8,22]]},"abstract":"<jats:p>Annotated language resources derived from clinical routine documentation form an intriguing asset for secondary use case scenarios. In this investigation, we report on how such a resource can be leveraged to identify additional term candidates for a chosen set of ICD-10 codes. We conducted a log-likelihood analysis, considering the co-occurrence of approximately 1.9 million de-identified ICD-10 codes alongside corresponding brief textual entries from problem lists in German. This analysis aimed to identify potential candidates with statistical significance set at p &lt; 0.01, which were used as seed terms to harvest additional candidates by interfacing to a large language model in a second step. The proposed approach can identify additional term candidates at suitable performance values: hypernyms MAP@5=0.801, synonyms MAP@5 = 0.723 and hyponyms MAP@5 = 0.507. The re-use of existing annotated clinical datasets, in combination with large language models, presents an interesting strategy to bridge the lexical gap in standardized clinical terminologies and real-world jargon.<\/jats:p>","DOI":"10.3233\/shti240509","type":"book-chapter","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T09:39:38Z","timestamp":1724405978000},"source":"Crossref","is-referenced-by-count":0,"title":["Term Candidate Generation to Enrich Clinical Terminologies with Large Language Models"],"prefix":"10.3233","author":[{"given":"Amila","family":"Kugic","sequence":"first","affiliation":[{"name":"Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria"}]},{"given":"Stefan","family":"Schulz","sequence":"additional","affiliation":[{"name":"Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria"}]},{"given":"Markus","family":"Kreuzthaler","sequence":"additional","affiliation":[{"name":"Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Digital Health and Informatics Innovations for Sustainable Health Care Systems"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI240509","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T09:39:39Z","timestamp":1724405979000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI240509"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,22]]},"ISBN":["9781643685335"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti240509","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,22]]}}}