{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T09:26:08Z","timestamp":1758273968039,"version":"3.37.3"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"20","license":[{"start":{"date-parts":[[2019,3,18]],"date-time":"2019-03-18T00:00:00Z","timestamp":1552867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61806017"],"award-info":[{"award-number":["61806017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,10,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>With the abundant medical resources, especially literature available online, it is possible for people to understand their own health status and relevant problems autonomously. However, how to obtain the most appropriate answer from the increasingly large-scale database, remains a great challenge. Here, we present a biomedical question answering framework and implement a system, Health Assistant, to enable the search process.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>In Health Assistant, a search engine is firstly designed to rank biomedical documents based on contents. Then various query processing and search techniques are utilized to find the relevant documents. Afterwards, the titles and abstracts of top-N documents are extracted to generate candidate snippets. Finally, our own designed query processing and retrieval approaches for short text are applied to locate the relevant snippets to answer the questions.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Our system is evaluated on the BioASQ benchmark datasets, and experimental results demonstrate the effectiveness and robustness of our system, compared to BioASQ participant systems and some state-of-the-art methods on both document retrieval and snippet retrieval tasks.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>A demo of our system is available at https:\/\/github.com\/jinzanxia\/biomedical-QA.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz195","type":"journal-article","created":{"date-parts":[[2019,3,16]],"date-time":"2019-03-16T12:22:03Z","timestamp":1552738923000},"page":"4129-4139","source":"Crossref","is-referenced-by-count":6,"title":["Health assistant: answering your questions anytime from biomedical literature"],"prefix":"10.1093","volume":"35","author":[{"given":"Zan-Xia","family":"Jin","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing , Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo-Wen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Turing Lab, Alibaba Group, Hangzhou , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing , Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Le-Le","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing , Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu-Cheng","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing , Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2019,3,18]]},"reference":[{"key":"2023013108284231600_btz195-B1","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-39940-9_924","volume-title":"Divergence from Randomness Models","author":"Amati","year":"2009"},{"article-title":"MetaMap: mapping text to the UMLS Metathesaurus","year":"2006","author":"Aronson","key":"2023013108284231600_btz195-B2"},{"key":"2023013108284231600_btz195-B3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cmpb.2009.10.003","article-title":"Biomedical question answering: a survey","volume":"99","author":"Athenikos","year":"2010","journal-title":"Comput. 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