{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:03:47Z","timestamp":1755219827585,"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>Medication errors significantly challenge healthcare, necessitating innovative analytical methods. This study explored generative pre-trained language models (LLMs) for Named Entity Recognition (NER) in Japanese medical incident reports. We assessed four LLMs\u2014Llama-3-ELYZA, BioMistral-7B, GPT-4.0 mini, and GPT-4.0\u2014using a national open-source dataset, comparing their NER performance with a published annotated version of the data and offering a prompt-based framework approach to address the clinical NER problem. Although GPT-4.0 outperformed the others, it does not exceed the fine-tuned BERT model previously reported. Few-shot prompts achieved high accuracy for number-related entities (e.g., \u2018Strength_rate\u2019, F1-score: 0.951) matching the previous study, but clinically specific types underperformed due to language complexities. Despite these challenges, providing entity type definitions and a few examples improved GPT-4.0\u2019s performance, highlighting LLMs\u2019 potential without extensive training and the necessity of considering the linguistic challenges in the clinical NER problem.<\/jats:p>","DOI":"10.3233\/shti250938","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:35:44Z","timestamp":1754566544000},"source":"Crossref","is-referenced-by-count":0,"title":["Exploring Prompt-Based Large Language Model (LLM) Approach for Medication Error-Related Named Entity Recognition in Medical Incident Reports"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3096-5850","authenticated-orcid":false,"given":"Mizue","family":"Ogi","sequence":"first","affiliation":[{"name":"Graduate School of Public Health, St.Luke\u2019s International University, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shin","family":"Ushiro","sequence":"additional","affiliation":[{"name":"Division of Patient Safety, Kyushu University Hospital, Fukuoka, Japan"},{"name":"Japan Council for Quality Health Care (JQ), Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4499-9779","authenticated-orcid":false,"given":"Zoie Shui-Yee","family":"Wong","sequence":"additional","affiliation":[{"name":"Graduate School of Public Health, St.Luke\u2019s International University, Tokyo, Japan"}],"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\/SHTI250938","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:35:44Z","timestamp":1754566544000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250938"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250938","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]]}}}