{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T23:46:48Z","timestamp":1786492008349,"version":"build-2736575974"},"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>Phenotype definitions are crucial for the progression of precision and personalized medicine. Although phenotype knowledge bases such as PheKB and the OHDSI library are available, they rely heavily on manual input. This study introduces PheCatcher, an automated pipeline that integrates BiomedBERT-based Named Entity Recognition (NER) and Relation Extraction (RE) to extract phenotypes and standardized codes from biomedical literature. To complement human annotation, GPT-4 was utilized to generate synthetic data, which improved model performance. The NER model\u2019s F1 score for \u201cphenotype\u201d entities increased from 0.616 to 0.800, and the RE model achieved an F1 score of 0.901. The application of the pipeline to the PubMed Central (PMC) articles resulted in the extraction of 173,283 phenotype definitions, which are now publicly accessible. Our study underscores the potential of synthetic data for information extraction (IE) and offers the first evidence of the feasibility of leveraging synthetic data to build a complete IE system.<\/jats:p>","DOI":"10.3233\/shti250934","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:35:37Z","timestamp":1754566537000},"source":"Crossref","is-referenced-by-count":6,"title":["PheCatcher: Leveraging LLM-Generated Synthetic Data for Automated Phenotype Definition Extraction from Biomedical Literature"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2413-5918","authenticated-orcid":false,"given":"Yan","family":"Hu","sequence":"first","affiliation":[{"name":"McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6798-1761","authenticated-orcid":false,"given":"Na","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8784-1745","authenticated-orcid":false,"given":"Yiming","family":"Li","sequence":"additional","affiliation":[{"name":"McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1484-0622","authenticated-orcid":false,"given":"Xueqing","family":"Peng","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0835-0788","authenticated-orcid":false,"given":"Yong","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5274-4672","authenticated-orcid":false,"given":"Hua","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"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\/SHTI250934","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:35:37Z","timestamp":1754566537000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250934"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250934","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]]}}}