{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T15:15:36Z","timestamp":1761491736805,"version":"3.41.0"},"reference-count":26,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T00:00:00Z","timestamp":1745539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"AIDAVA","award":["Horizon Europe: EU HORIZON-HLTH-2021-TOOL-06-03"],"award-info":[{"award-number":["Horizon Europe: EU HORIZON-HLTH-2021-TOOL-06-03"]}]},{"DOI":"10.13039\/501100001942","name":"CHIST-ERA","doi-asserted-by":"crossref","award":["ANTIDOTE (ERA-NET CHIST-ERA IV FET PROACT JTC 2019)"],"award-info":[{"award-number":["ANTIDOTE (ERA-NET CHIST-ERA IV FET PROACT JTC 2019)"]}],"id":[{"id":"10.13039\/501100001942","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Comput. Healthcare"],"published-print":{"date-parts":[[2025,4,30]]},"abstract":"<jats:p>\n            Medical Question Answering (medical QA) systems play an essential role in assisting healthcare workers in finding answers to their questions. However, it is not sufficient to merely provide answers by medical QA systems because users might want explanations, that is, more analytic statements in natural language that describe the elements and context that support the answer. To do so, we propose a novel approach for generating natural language explanations for answers predicted by medical QA systems. As high-quality medical explanations require additional medical knowledge, so that our system extracts knowledge from medical textbooks to enhance the quality of explanations during the explanation generation process. Concretely, we designed an Expectation-Maximization approach that makes inferences about the evidence found in these texts, offering an efficient way to focus attention on lengthy evidence passages. Experimental results, conducted on two datasets MQAE-diag and MQAE, demonstrate the effectiveness of our framework for reasoning with textual evidence. Our approach outperforms state-of-the-art models, achieving a significant improvement of\n            <jats:bold>6.13<\/jats:bold>\n            and\n            <jats:bold>5.47<\/jats:bold>\n            percentage points on the Rouge-L score;\n            <jats:bold>6.49<\/jats:bold>\n            and\n            <jats:bold>5.28<\/jats:bold>\n            percentage points on the Bleu-4 score on the MQAE-diag and MQAE datasets.\n          <\/jats:p>","DOI":"10.1145\/3712296","type":"journal-article","created":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T17:59:31Z","timestamp":1737655171000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Generating Explanations in Medical Question-Answering by Expectation Maximization Inference over Evidence"],"prefix":"10.1145","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6724-0584","authenticated-orcid":false,"given":"Wei","family":"Sun","sequence":"first","affiliation":[{"name":"CS Department, KU Leuven, Leuven, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4951-6887","authenticated-orcid":false,"given":"Mingxiao","family":"Li","sequence":"additional","affiliation":[{"name":"CS Department, KU Leuven, Leuven, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3274-291X","authenticated-orcid":false,"given":"Damien","family":"Sileo","sequence":"additional","affiliation":[{"name":"Inria Lille, Villeneuve-d\u2019Ascq, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3748-9263","authenticated-orcid":false,"given":"Jesse","family":"Davis","sequence":"additional","affiliation":[{"name":"CS Department, KU Leuven, Leuven, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3732-9323","authenticated-orcid":false,"given":"Marie-Francine","family":"Moens","sequence":"additional","affiliation":[{"name":"CS Department, KU Leuven, Leuven, Belgium"}]}],"member":"320","published-online":{"date-parts":[[2025,4,25]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2015.04.006"},{"key":"e_1_3_2_3_1","first-page":"72","article-title":"Improving medical code prediction from clinical text via incorporating online knowledge sources (WWW \u201919)","author":"Bai Tian","year":"2019","unstructured":"Tian Bai and Slobodan Vucetic. 2019. Improving medical code prediction from clinical text via incorporating online knowledge sources (WWW \u201919). ACM, 72\u201382.","journal-title":"ACM"},{"key":"e_1_3_2_4_1","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown Tom","year":"2020","unstructured":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et\u00a0al. 2020. Language models are few-shot learners. Advances in Neural Information Processing Systems 33 (2020), 1877\u20131901.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_5_1","first-page":"2946","volume-title":"Proceedings of the 29th COLING","author":"Chen Wei-Lin","year":"2022","unstructured":"Wei-Lin Chen, An-Zi Yen, Hen-Hsen Huang, and Hsin-Hsi Chen. 2022. Learning to generate explanation from E-hospital services for medical suggestion. 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A textbook remedy for domain shifts: Knowledge priors for medical image analysis. arXiv:2405.14839. Retrieved from https:\/\/arxiv.org\/abs\/2405.14839"},{"key":"e_1_3_2_25_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.551"},{"key":"e_1_3_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3351033"},{"key":"e_1_3_2_27_1","article-title":"Least-to-most prompting enables complex reasoning in large language models","author":"Zhou Denny","year":"2023","unstructured":"Denny Zhou, Nathanael Sch\u00e4rli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V. Le, and Ed H. Chi. 2023. Least-to-most prompting enables complex reasoning in large language models. 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