{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T06:29:15Z","timestamp":1778826555118,"version":"3.51.4"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684567","type":"print"},{"value":"9781643684574","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T00:00:00Z","timestamp":1706140800000},"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":[[2024,1,25]]},"abstract":"<jats:p>Clinical NLP can be applied to extract medication information from free-text notes in EMRs, using NER pipelines. Publicly available annotated data for clinical NLP are scarce, and research annotation budgets are often low. Fine-tuning pre-trained pipelines containing a Transformer layer can produce quality results with relatively small training corpora. We examine the transferability of a publicly available, pre-trained NER pipeline with a Transformer layer for medication targets. The pipeline performs poorly when directly validated but achieves an F1-score of 92% for drug names after fine-tuning with 1,565 annotated samples from a clinical cancer EMR \u2013 highlighting the benefits of the Transformer architecture in this setting. Performance was largely influenced by inconsistent annotation \u2013 reinforcing the need for innovative annotation processes in clinical NLP applications.<\/jats:p>","DOI":"10.3233\/shti231051","type":"book-chapter","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:24:49Z","timestamp":1706178289000},"source":"Crossref","is-referenced-by-count":3,"title":["Applying and Improving a Publicly Available Medication NER Pipeline in a Clinical Cancer EMR"],"prefix":"10.3233","author":[{"given":"Meg","family":"Stevens","sequence":"first","affiliation":[{"name":"University of New South Wales, Sydney Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Georgina","family":"Kennedy","sequence":"additional","affiliation":[{"name":"University of New South Wales, Sydney Australia"},{"name":"Ingham Institute for Applied Medical Research, Sydney Australia"},{"name":"Maridulu Budyari Gumal (SPHERE) Cancer Clinical Academic Group"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Timothy","family":"Churches","sequence":"additional","affiliation":[{"name":"University of New South Wales, Sydney Australia"},{"name":"Ingham Institute for Applied Medical Research, Sydney Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2023 \u2014 The Future Is Accessible"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI231051","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:24:51Z","timestamp":1706178291000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI231051"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,25]]},"ISBN":["9781643684567","9781643684574"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti231051","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,25]]}}}