{"id":"https://openalex.org/W2913388902","doi":"https://doi.org/10.1109/bibm.2018.8621209","title":"Automatic Acceptance Prediction for Answers in Online Healthcare Community","display_name":"Automatic Acceptance Prediction for Answers in Online Healthcare Community","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2913388902","doi":"https://doi.org/10.1109/bibm.2018.8621209","mag":"2913388902"},"language":"en","primary_location":{"id":"doi:10.1109/bibm.2018.8621209","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2018.8621209","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5060346402","display_name":"Qianlong Liu","orcid":"https://orcid.org/0009-0009-1722-9737"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qianlong Liu","raw_affiliation_strings":["School of Data Science Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Data Science Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009877238","display_name":"Kangenbei Liao","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kangenbei Liao","raw_affiliation_strings":["School of Data Science Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Data Science Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011504177","display_name":"Zhongyu Wei","orcid":"https://orcid.org/0000-0003-3789-8507"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongyu Wei","raw_affiliation_strings":["School of Data Science Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Data Science Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":0.2264,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.56033618,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1262","last_page":"1265"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13274","display_name":"Expert finding and Q&A systems","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.709631085395813},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6759724617004395},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6097045540809631},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.5669186115264893},{"id":"https://openalex.org/keywords/order","display_name":"Order (exchange)","score":0.5605874061584473},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5263368487358093},{"id":"https://openalex.org/keywords/questions-and-answers","display_name":"Questions and answers","score":0.5186809301376343},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4874684810638428},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4307800531387329},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4134824872016907},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.38905906677246094},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34013834595680237}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.709631085395813},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6759724617004395},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6097045540809631},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.5669186115264893},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.5605874061584473},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5263368487358093},{"id":"https://openalex.org/C3019144022","wikidata":"https://www.wikidata.org/wiki/Q4124998","display_name":"Questions and answers","level":2,"score":0.5186809301376343},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4874684810638428},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4307800531387329},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4134824872016907},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.38905906677246094},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34013834595680237},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm.2018.8621209","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2018.8621209","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6299999952316284,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W856962312","https://openalex.org/W1880262756","https://openalex.org/W1947481528","https://openalex.org/W2029522813","https://openalex.org/W2030498706","https://openalex.org/W2041360546","https://openalex.org/W2061929286","https://openalex.org/W2064675550","https://openalex.org/W2115613106","https://openalex.org/W2153579005","https://openalex.org/W2157331557","https://openalex.org/W2231608773","https://openalex.org/W2370336089","https://openalex.org/W2462133661","https://openalex.org/W2552027021","https://openalex.org/W2560627000","https://openalex.org/W2798161840","https://openalex.org/W2798494119","https://openalex.org/W2886305736","https://openalex.org/W2962985038","https://openalex.org/W2963797754","https://openalex.org/W2963871484","https://openalex.org/W2964026924","https://openalex.org/W2964045208","https://openalex.org/W4231510805","https://openalex.org/W4294170691","https://openalex.org/W6608229634","https://openalex.org/W6639619044","https://openalex.org/W6682691769","https://openalex.org/W6689960994","https://openalex.org/W6704493726","https://openalex.org/W6729263887","https://openalex.org/W6730492364","https://openalex.org/W6746041023","https://openalex.org/W6754826874"],"related_works":["https://openalex.org/W4390516098","https://openalex.org/W2181948922","https://openalex.org/W2375873920","https://openalex.org/W2384362569","https://openalex.org/W2146114872","https://openalex.org/W2392060890","https://openalex.org/W4205302943","https://openalex.org/W2392760275","https://openalex.org/W2119949815","https://openalex.org/W2561132942"],"abstract_inverted_index":{"Predicting":[0],"whether":[1],"an":[2,17],"answer":[3],"of":[4,23,35,41,76,98],"doctors":[5],"would":[6],"be":[7],"accepted":[8],"by":[9,54,59],"a":[10,30,64,68,74,81,120],"patient":[11],"on":[12,67,103],"online":[13],"healthcare":[14],"communities":[15],"plays":[16],"important":[18],"role":[19],"in":[20],"the":[21,39,51,96],"development":[22],"e-Health.":[24],"In":[25,43],"this":[26],"paper,":[27],"we":[28,61],"proposed":[29],"framework":[31,109],"combining":[32],"different":[33],"types":[34],"features":[36,48,88,91,115],"to":[37,45,94,112],"predict":[38,95],"acceptance":[40,97],"answers.":[42,99],"order":[44],"extract":[46,113],"textual":[47,90],"from":[49,116],"both":[50,86],"questions":[52,77],"posted":[53,58],"patients":[55],"and":[56,78,84,89,118],"answers":[57,79],"doctors,":[60],"first":[62],"trained":[63],"sentence":[65],"encoder":[66],"held-":[69],"out":[70],"dataset,":[71],"which":[72],"encodes":[73],"pair":[75],"into":[80],"co-dependent":[82],"representation.":[83],"then":[85],"numerical":[87],"are":[92],"combined":[93],"The":[100],"experimental":[101],"results":[102],"our":[104,108],"dataset":[105],"demonstrates":[106],"that":[107],"is":[110],"able":[111],"additional":[114],"text":[117],"make":[119],"better":[121],"prediction.":[122]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
