{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T09:01:13Z","timestamp":1771059673180,"version":"3.50.1"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"11","funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>In multi-label text classification, each textual document is assigned 1 or more labels. As an important task that has broad applications in biomedicine, a number of different computational methods have been proposed. Many of these methods, however, have only modest accuracy or efficiency and limited success in practical use. We propose ML-Net, a novel end-to-end deep learning framework, for multi-label classification of biomedical texts.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>ML-Net combines a label prediction network with an automated label count prediction mechanism to provide an optimal set of labels. This is accomplished by leveraging both the predicted confidence score of each label and the deep contextual information (modeled by ELMo) in the target document. We evaluate ML-Net on 3 independent corpora in 2 text genres: biomedical literature and clinical notes. For evaluation, we use example-based measures, such as precision, recall, and the F measure. We also compare ML-Net with several competitive machine learning and deep learning baseline models.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Our benchmarking results show that ML-Net compares favorably to state-of-the-art methods in multi-label classification of biomedical text. ML-Net is also shown to be robust when evaluated on different text genres in biomedicine.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>ML-Net is able to accuractely represent biomedical document context and dynamically estimate the label count in a more systematic and accurate manner. Unlike traditional machine learning methods, ML-Net does not require human effort for feature engineering and is a highly efficient and scalable approach to tasks with a large set of labels, so there is no need to build individual classifiers for each separate label.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocz085","type":"journal-article","created":{"date-parts":[[2019,5,8]],"date-time":"2019-05-08T19:23:23Z","timestamp":1557343403000},"page":"1279-1285","source":"Crossref","is-referenced-by-count":102,"title":["ML-Net: multi-label classification of biomedical texts with deep neural networks"],"prefix":"10.1093","volume":"26","author":[{"given":"Jingcheng","family":"Du","sequence":"first","affiliation":[{"name":"National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland, USA"},{"name":"The University of Texas School of Biomedical Informatics, Houston, Texas, USA"}]},{"given":"Qingyu","family":"Chen","sequence":"additional","affiliation":[{"name":"National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9309-8331","authenticated-orcid":false,"given":"Yifan","family":"Peng","sequence":"additional","affiliation":[{"name":"National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland, USA"}]},{"given":"Yang","family":"Xiang","sequence":"additional","affiliation":[{"name":"The University of Texas School of Biomedical Informatics, Houston, Texas, USA"}]},{"given":"Cui","family":"Tao","sequence":"additional","affiliation":[{"name":"The University of Texas School of Biomedical Informatics, Houston, Texas, USA"}]},{"given":"Zhiyong","family":"Lu","sequence":"additional","affiliation":[{"name":"National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland, USA"}]}],"member":"286","published-online":{"date-parts":[[2019,6,24]]},"reference":[{"key":"2021012411200098600_ocz085-B1","volume-title":"Speech and Language Processing","author":"Jurafsky","year":"2014"},{"issue":"5","key":"2021012411200098600_ocz085-B2","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1136\/amiajnl-2010-000055","article-title":"Recommending MeSH terms for annotating biomedical articles","volume":"18","author":"Huang","year":"2011","journal-title":"J Am Med Inform Assoc"},{"issue":"12","key":"2021012411200098600_ocz085-B3","doi-asserted-by":"crossref","first-page":"i70","DOI":"10.1093\/bioinformatics\/btw294","article-title":"DeepMeSH: Deep semantic representation for improving large-scale MeSH indexing","volume":"32","author":"Peng","year":"2016","journal-title":"Bioinformatics"},{"issue":"2","key":"2021012411200098600_ocz085-B4","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1136\/amiajnl-2013-002159","article-title":"Diagnosis code assignment: models and evaluation metrics","volume":"21","author":"Perotte","year":"2014","journal-title":"J Am Med Inform Assoc"},{"key":"2021012411200098600_ocz085-B5","author":"Baumel"},{"issue":"7","key":"2021012411200098600_ocz085-B6","doi-asserted-by":"crossref","first-page":"e236","DOI":"10.2196\/jmir.9413","article-title":"Public perception analysis of tweets during the 2015 measles outbreak: comparative study using convolutional neural network models","volume":"20","author":"Du","year":"2018","journal-title":"J Med Internet Res"},{"key":"2021012411200098600_ocz085-B7","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1186\/s12911-018-0632-8","article-title":"Extracting psychiatric stressors for suicide from social media using deep learning","volume":"18 (Suppl 2)","author":"Du","year":"2018","journal-title":"BMC Med Inform Decis Mak"},{"issue":"12","key":"2021012411200098600_ocz085-B8","doi-asserted-by":"crossref","first-page":"e414","DOI":"10.2196\/jmir.9266","article-title":"Using social media data to understand the impact of promotional information on laypeople\u2019s discussions: a case study of Lynch syndrome","volume":"19","author":"Bian","year":"2017","journal-title":"J Med Internet Res"},{"key":"2021012411200098600_ocz085-B9","first-page":"1070","article-title":"Automated classification of multi-labeled patient safety reports: a shift from quantity to quality measure","volume":"245","author":"Liang","year":"2017","journal-title":"Stud Health Technol Inform"},{"key":"2021012411200098600_ocz085-B10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13326-017-0123-3","article-title":"MeSH now: automatic MeSH indexing at PubMed scale via learning to rank","volume":"8","author":"Mao","year":"2017","journal-title":"J Biomed Semantics"},{"key":"2021012411200098600_ocz085-B11","first-page":"641","author":"Gargiulo","year":"2018"},{"key":"2021012411200098600_ocz085-B12","author":"Li","year":"2017"},{"key":"2021012411200098600_ocz085-B13","author":"Nam"},{"issue":"9","key":"2021012411200098600_ocz085-B14","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1016\/j.patcog.2004.03.009","article-title":"Learning multi-label scene classification","volume":"37","author":"Boutell","year":"2004","journal-title":"Pattern Recognit"},{"issue":"3","key":"2021012411200098600_ocz085-B15","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s10994-011-5256-5","article-title":"Classifier chains for multi-label classification","volume":"85","author":"Read","year":"2011","journal-title":"Mach Learn"},{"key":"2021012411200098600_ocz085-B16","doi-asserted-by":"crossref","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","article-title":"A review on multi-label learning algorithms","volume":"26","author":"Min-Ling","year":"2014","journal-title":"Knowl Data Eng IEEE Trans"},{"key":"2021012411200098600_ocz085-B17","doi-asserted-by":"crossref","first-page":"1338","DOI":"10.1109\/TKDE.2006.162","article-title":"Multilabel neural networks with applications to functional genomics and text categorization","volume":"18","author":"Zhang","year":"2006","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2021012411200098600_ocz085-B18","article-title":"Automated ICD-9 coding via a deep learning approach","author":"Li","year":"2018","journal-title":"IEEE\/ACM Trans Comput Biol Bioinforma"},{"key":"2021012411200098600_ocz085-B19","first-page":"307","article-title":"Initializing neural networks for hierarchical multi-label text classification","volume":"BioNLP 2017","author":"Baker"},{"key":"2021012411200098600_ocz085-B20","author":"Lenc"},{"key":"2021012411200098600_ocz085-B21","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.ajog.2010.08.050","article-title":"Adverse events in pregnant women following administration of trivalent inactivated influenza vaccine and live attenuated influenza vaccine in the Vaccine Adverse Event Reporting System, 1990-2009","volume":"204","author":"Moro","year":"2011","journal-title":"Am J Obstet Gynecol"},{"key":"2021012411200098600_ocz085-B22","author":"Nigam"},{"key":"2021012411200098600_ocz085-B23","author":"Peters"},{"key":"2021012411200098600_ocz085-B24","author":"Yang"},{"key":"2021012411200098600_ocz085-B25","author":"Wang","year":"2018,   ,"},{"key":"2021012411200098600_ocz085-B26","first-page":"807","author":"Nair","year":", 2010, ,"},{"issue":"5","key":"2021012411200098600_ocz085-B27","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1016\/j.cell.2011.02.013","article-title":"Hallmarks of cancer: the next generation","volume":"144","author":"Hanahan","year":"2011","journal-title":"Cell"},{"issue":"3","key":"2021012411200098600_ocz085-B28","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1093\/bioinformatics\/btv585","article-title":"Automatic semantic classification of scientific literature according to the hallmarks of cancer","volume":"32","author":"Baker","year":"2016","journal-title":"Bioinformatics"},{"issue":"3","key":"2021012411200098600_ocz085-B29","doi-asserted-by":"crossref","first-page":"e0173132","DOI":"10.1371\/journal.pone.0173132","article-title":"Text mining for improved exposure assessment","volume":"12","author":"Larsson","year":"2017","journal-title":"PLoS One"},{"key":"2021012411200098600_ocz085-B30","author":"Liu","year":", 2017, ,"},{"key":"2021012411200098600_ocz085-B31","author":"Kingma"},{"key":"2021012411200098600_ocz085-B32","author":"Rios","year":"2015"},{"key":"2021012411200098600_ocz085-B33","author":"Du"},{"key":"2021012411200098600_ocz085-B34","author":"Moen"},{"key":"2021012411200098600_ocz085-B35","author":"Wu","year":"., 2015. ,"},{"key":"2021012411200098600_ocz085-B36","first-page":"5998","author":"Vaswani","year":", 2017,  ,"},{"key":"2021012411200098600_ocz085-B37","author":"Radford","year":"2018"},{"key":"2021012411200098600_ocz085-B38","author":"Devlin"}],"container-title":["Journal of the American Medical Informatics Association"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/jamia\/article-pdf\/26\/11\/1279\/36089060\/ocz085.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/jamia\/article-pdf\/26\/11\/1279\/36089060\/ocz085.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,24]],"date-time":"2021-01-24T16:20:16Z","timestamp":1611505216000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jamia\/article\/26\/11\/1279\/5522430"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,24]]},"references-count":38,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2019,6,24]]},"published-print":{"date-parts":[[2019,11,1]]}},"URL":"https:\/\/doi.org\/10.1093\/jamia\/ocz085","relation":{},"ISSN":["1067-5027","1527-974X"],"issn-type":[{"value":"1067-5027","type":"print"},{"value":"1527-974X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2019,11]]},"published":{"date-parts":[[2019,6,24]]}}}