{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:50:04Z","timestamp":1747216204015,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643685335"}],"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>There is a rapid growth in the volume of data in the cancer field and fine-grained classification is in high demand especially for interdisciplinary and collaborative research. There is thus a need to establish a multi-label classifier with higher resolution to reduce the burden of screening articles for clinical relevance. This research trains a multi-label classifier with scalability for classifying literature on cancer research directly at the publication level. Firstly, a corpus was divided into a training set and a testing set at a ratio of 7:3. Secondly, we compared the performance of classifiers developed by \u201cPubMedBERT + TextRNN\u201d and \u201cBioBERT + TextRNN\u201d with ICRP CT. Finally, the classifier was obtained based on the optimal combination \u201cPubMedBERT + TextRNN\u201d, with P= 0.952014, R=0.936696, F1=0.931664. The quantitative comparisons demonstrate that the resulting classifier is fit for high-resolution classification of cancer literature at the publication level to support accurate retrieving and academic statistics.<\/jats:p>","DOI":"10.3233\/shti240425","type":"book-chapter","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T09:12:36Z","timestamp":1724404356000},"source":"Crossref","is-referenced-by-count":0,"title":["A Multi-Label Text Classifier at Publication Level Based on \u201cPubMedBERT + TextRNN\u201d for Cancer Literature"],"prefix":"10.3233","author":[{"given":"Zhang","family":"Ying","sequence":"first","affiliation":[{"name":"Institute of Medical Information, Chinese Academy of Medical Sciences, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4587-0344","authenticated-orcid":false,"given":"Xia","family":"Guanghui","sequence":"additional","affiliation":[{"name":"Institute of Medical Information, Chinese Academy of Medical Sciences, China"}]},{"given":"Li","family":"Xiaoying","sequence":"additional","affiliation":[{"name":"Institute of Medical Information, Chinese Academy of Medical Sciences, China"}]},{"given":"Tang","family":"Shishi","sequence":"additional","affiliation":[{"name":"Institute of Medical Information, Chinese Academy of Medical Sciences, China"}]}],"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\/SHTI240425","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T09:12:37Z","timestamp":1724404357000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI240425"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,22]]},"ISBN":["9781643685335"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti240425","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2024,8,22]]}}}