{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:03:58Z","timestamp":1755219838086,"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>Information extraction tasks, such as Named Entity Recognition (NER) and Relation Extraction (RE), are essential for advancing clinical research and applications. However, these tasks are hindered by the scarcity of labeled clinical documents due to privacy concerns and high annotation costs. This study introduces a novel framework combining Large Language Models (LLMs) for data augmentation with an adapted BERT model for clinical information extraction. The framework encodes entity and relational information within clinical note segments, enabling LLMs to generate diverse and contextually accurate augmentations while preserving structural integrity. Augmented data is used to train a segmentation-based BERT model, overcoming sequence length limitations and integrating global context via BiLSTM. Evaluations on public and proprietary datasets demonstrate significant performance improvements, highlighting the approach\u2019s potential to address data scarcity in clinical information extraction tasks.<\/jats:p>","DOI":"10.3233\/shti250984","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:37:14Z","timestamp":1754566634000},"source":"Crossref","is-referenced-by-count":0,"title":["Structured LLM Augmentation for Clinical Information Extraction"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7403-3495","authenticated-orcid":false,"given":"Ying","family":"Wei","sequence":"first","affiliation":[{"name":"Iowa State University"},{"name":"Truveta"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3136-2157","authenticated-orcid":false,"given":"Qi","family":"Li","sequence":"additional","affiliation":[{"name":"Iowa State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jay","family":"Pillai","sequence":"additional","affiliation":[{"name":"Truveta"}],"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\/SHTI250984","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:37:15Z","timestamp":1754566635000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250984"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250984","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]]}}}