{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:19:58Z","timestamp":1782951598810,"version":"3.54.5"},"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>The ability to fine-tune pre-trained deep learning models to learn how to process a downstream task using a large training set allow to significantly improve performances of named entity recognition. Large language models are recent models based on the Transformers architecture that may be conditioned on a new task with in-context learning, by providing a series of instructions or prompt. These models only require few examples and such approach is defined as few shot learning. Our objective was to compare performances of named entity recognition of adverse drug events between state of the art deep learning models fine-tuned on Pubmed abstracts and a large language model using few-shot learning. Hussain et al\u2019s state of the art model (PMID: 34422092) significantly outperformed the ChatGPT-3.5 model (F1-Score: 97.6% vs 86.0%). Few-shot learning is a convenient way to perform named entity recognition when training examples are rare, but performances are still inferior to those of a deep learning model fine-tuned with several training examples. Perspectives are to evaluate few-shot prompting with GPT-4 and perform fine-tuning on GPT-3.5.<\/jats:p>","DOI":"10.3233\/shti240528","type":"book-chapter","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T09:41:17Z","timestamp":1724406077000},"source":"Crossref","is-referenced-by-count":2,"title":["Comparing a Large Language Model with Previous Deep Learning Models on Named Entity Recognition of Adverse Drug Events"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2975-0553","authenticated-orcid":false,"given":"Th\u00e9ophile","family":"Tiffet","sequence":"first","affiliation":[{"name":"Public health and medical information unit, Saint Etienne University Hospital, France"},{"name":"Laboratoire Inserm, SAINBIOSE, U1059, dysfonction vasculaire et h\u00e9mostase, universit\u00e9 Jean-Monnet, Saint-\u00c9tienne, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8419-5785","authenticated-orcid":false,"given":"Alexis","family":"Pikaar","sequence":"additional","affiliation":[{"name":"Sorbonne Universit\u00e9, INSERM, Univ Paris 13, Laboratoire d\u2019Informatique M\u00e9dicale et d\u2019Ing\u00e9nierie des Connaissances pour la e-Sant\u00e9, LIMICS, 75006, Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1710-5568","authenticated-orcid":false,"given":"B\u00e9atrice","family":"Trombert-Paviot","sequence":"additional","affiliation":[{"name":"Public health and medical information unit, Saint Etienne University Hospital, France"},{"name":"Laboratoire Inserm, SAINBIOSE, U1059, dysfonction vasculaire et h\u00e9mostase, universit\u00e9 Jean-Monnet, Saint-\u00c9tienne, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4445-7494","authenticated-orcid":false,"given":"Marie-Christine","family":"Jaulent","sequence":"additional","affiliation":[{"name":"Sorbonne Universit\u00e9, INSERM, Univ Paris 13, Laboratoire d\u2019Informatique M\u00e9dicale et d\u2019Ing\u00e9nierie des Connaissances pour la e-Sant\u00e9, LIMICS, 75006, Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9775-2476","authenticated-orcid":false,"given":"C\u00e9dric","family":"Bousquet","sequence":"additional","affiliation":[{"name":"Public health and medical information unit, Saint Etienne University Hospital, France"},{"name":"Sorbonne Universit\u00e9, INSERM, Univ Paris 13, Laboratoire d\u2019Informatique M\u00e9dicale et d\u2019Ing\u00e9nierie des Connaissances pour la e-Sant\u00e9, LIMICS, 75006, Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"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\/SHTI240528","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T09:41:18Z","timestamp":1724406078000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI240528"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,22]]},"ISBN":["9781643685335"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti240528","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]]}}}