{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T12:39:11Z","timestamp":1766579951030,"version":"3.37.3"},"reference-count":63,"publisher":"Oxford University Press (OUP)","issue":"18","license":[{"start":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T00:00:00Z","timestamp":1658707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Department of Veterans Affairs, VHA Office of Mental Health and Suicide Prevention","award":["DE- AC05-00OR22725"],"award-info":[{"award-number":["DE- AC05-00OR22725"]}]},{"DOI":"10.13039\/100000015","name":"US Department of Energy","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,15]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Biomedical machine reading comprehension (biomedical-MRC) aims to comprehend complex biomedical narratives and assist healthcare professionals in retrieving information from them. The high performance of modern neural network-based MRC systems depends on high-quality, large-scale, human-annotated training datasets. In the biomedical domain, a crucial challenge in creating such datasets is the requirement for domain knowledge, inducing the scarcity of labeled data and the need for transfer learning from the labeled general-purpose (source) domain to the biomedical (target) domain. However, there is a discrepancy in marginal distributions between the general-purpose and biomedical domains due to the variances in topics. Therefore, direct-transferring of learned representations from a model trained on a general-purpose domain to the biomedical domain can hurt the model\u2019s performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We present an adversarial learning-based domain adaptation framework for the biomedical machine reading comprehension task (BioADAPT-MRC), a neural network-based method to address the discrepancies in the marginal distributions between the general and biomedical domain datasets. BioADAPT-MRC relaxes the need for generating pseudo labels for training a well-performing biomedical-MRC model. We extensively evaluate the performance of BioADAPT-MRC by comparing it with the best existing methods on three widely used benchmark biomedical-MRC datasets\u2014BioASQ-7b, BioASQ-8b and BioASQ-9b. Our results suggest that without using any synthetic or human-annotated data from the biomedical domain, BioADAPT-MRC can achieve state-of-the-art performance on these datasets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>BioADAPT-MRC is freely available as an open-source project at https:\/\/github.com\/mmahbub\/BioADAPT-MRC.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac508","type":"journal-article","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T14:51:05Z","timestamp":1658760665000},"page":"4369-4379","source":"Crossref","is-referenced-by-count":12,"title":["BioADAPT-MRC: adversarial learning-based domain adaptation improves biomedical machine reading comprehension task"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3422-9650","authenticated-orcid":false,"given":"Maria","family":"Mahbub","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Tennessee , Knoxville, TN 37996, USA"},{"name":"Cyber Resilience and Intelligence Division, Oak Ridge National Laboratory , Oak Ridge, TN 37830, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sudarshan","family":"Srinivasan","sequence":"additional","affiliation":[{"name":"Cyber Resilience and Intelligence Division, Oak Ridge National Laboratory , Oak Ridge, TN 37830, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edmon","family":"Begoli","sequence":"additional","affiliation":[{"name":"Cyber Resilience and Intelligence Division, Oak Ridge National Laboratory , Oak Ridge, TN 37830, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gregory D","family":"Peterson","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Tennessee , Knoxville, TN 37996, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,7,25]]},"reference":[{"key":"2023041408234642000_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-021-01633-4","article-title":"Identification of asthma control factor in clinical notes using a hybrid deep learning model","volume":"21","author":"Agnikula Kshatriya","year":"2021","journal-title":"BMC Med. 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