{"id":"https://openalex.org/W2952778987","doi":"https://doi.org/10.1145/3292500.3330770","title":"Naranjo Question Answering using End-to-End Multi-task Learning Model","display_name":"Naranjo Question Answering using End-to-End Multi-task Learning Model","publication_year":2019,"publication_date":"2019-07-25","ids":{"openalex":"https://openalex.org/W2952778987","doi":"https://doi.org/10.1145/3292500.3330770","mag":"2952778987","pmid":"https://pubmed.ncbi.nlm.nih.gov/31799022"},"language":"en","primary_location":{"id":"doi:10.1145/3292500.3330770","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3292500.3330770","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5086590990","display_name":"Bhanu Pratap Singh Rawat","orcid":"https://orcid.org/0000-0002-7140-2328"},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Bhanu Pratap Singh Rawat","raw_affiliation_strings":["University of Massachusetts Amherst, Amherst, MA, USA"],"affiliations":[{"raw_affiliation_string":"University of Massachusetts Amherst, Amherst, MA, USA","institution_ids":["https://openalex.org/I24603500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100325881","display_name":"Fei Li","orcid":"https://orcid.org/0000-0003-1816-1761"},"institutions":[{"id":"https://openalex.org/I133738476","display_name":"University of Massachusetts Lowell","ror":"https://ror.org/03hamhx47","country_code":"US","type":"education","lineage":["https://openalex.org/I133738476"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fei Li","raw_affiliation_strings":["University of Massachusetts Lowell, Lowell, MA, USA"],"affiliations":[{"raw_affiliation_string":"University of Massachusetts Lowell, Lowell, MA, USA","institution_ids":["https://openalex.org/I133738476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017601806","display_name":"Hong Yu","orcid":"https://orcid.org/0000-0001-9263-5035"},"institutions":[{"id":"https://openalex.org/I133738476","display_name":"University of Massachusetts Lowell","ror":"https://ror.org/03hamhx47","country_code":"US","type":"education","lineage":["https://openalex.org/I133738476"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hong Yu","raw_affiliation_strings":["University of Massachusetts Lowell, Lowell, MA, USA"],"affiliations":[{"raw_affiliation_string":"University of Massachusetts Lowell, Lowell, MA, USA","institution_ids":["https://openalex.org/I133738476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5086590990"],"corresponding_institution_ids":["https://openalex.org/I24603500"],"apc_list":null,"apc_paid":null,"fwci":0.8401,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.7999888,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"2019","issue":null,"first_page":"2547","last_page":"2555"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9937000274658203,"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":0.9937000274658203,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9631999731063843,"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/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.9610999822616577,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.758036732673645},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.7203665375709534},{"id":"https://openalex.org/keywords/end-to-end-principle","display_name":"End-to-end principle","score":0.6912179589271545},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.6010910272598267},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5158107280731201},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.32925987243652344},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09977582097053528},{"id":"https://openalex.org/keywords/systems-engineering","display_name":"Systems engineering","score":0.061956554651260376}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.758036732673645},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7203665375709534},{"id":"https://openalex.org/C74296488","wikidata":"https://www.wikidata.org/wiki/Q2527392","display_name":"End-to-end principle","level":2,"score":0.6912179589271545},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.6010910272598267},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5158107280731201},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.32925987243652344},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09977582097053528},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.061956554651260376}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3292500.3330770","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3292500.3330770","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},{"id":"pmid:31799022","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/31799022","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","raw_type":null},{"id":"pmh:oai:europepmc.org:5848515","is_oa":false,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6887102","pdf_url":null,"source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.5400000214576721}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1152166452","https://openalex.org/W1164166134","https://openalex.org/W1902237438","https://openalex.org/W1970934894","https://openalex.org/W1979496029","https://openalex.org/W1991567209","https://openalex.org/W1996023191","https://openalex.org/W2013805216","https://openalex.org/W2023502360","https://openalex.org/W2040619589","https://openalex.org/W2053106197","https://openalex.org/W2067978628","https://openalex.org/W2073202536","https://openalex.org/W2079735306","https://openalex.org/W2085074082","https://openalex.org/W2119191234","https://openalex.org/W2161554615","https://openalex.org/W2313427692","https://openalex.org/W2469314752","https://openalex.org/W2574184737","https://openalex.org/W2740747242","https://openalex.org/W2788807338","https://openalex.org/W2802736684","https://openalex.org/W2900069888","https://openalex.org/W2913340405","https://openalex.org/W2951815801","https://openalex.org/W4229779919","https://openalex.org/W4244978678","https://openalex.org/W4293236771","https://openalex.org/W4294457161","https://openalex.org/W4378951071"],"related_works":["https://openalex.org/W2384605597","https://openalex.org/W2387743295","https://openalex.org/W3179968364","https://openalex.org/W3082787378","https://openalex.org/W2136007095","https://openalex.org/W2366230879","https://openalex.org/W3208425359","https://openalex.org/W2349927912","https://openalex.org/W4288102755","https://openalex.org/W3204019825"],"abstract_inverted_index":{"In":[0,35],"the":[1,26,40,46,61,64,82,95,101,106],"clinical":[2,66],"domain,":[3],"it":[4],"is":[5,15],"important":[6],"to":[7,43,93],"understand":[8],"whether":[9],"an":[10,52],"adverse":[11],"drug":[12,50],"reaction":[13],"(ADR)":[14],"caused":[16],"by":[17,59,71],"a":[18,30,49,98,111,115],"particular":[19],"medication.":[20],"Clinical":[21],"judgement":[22],"studies":[23],"help":[24],"judge":[25],"causal":[27],"relation":[28],"between":[29,48,118,125],"medication":[31],"and":[32,51,122],"its":[33],"ADRs.":[34],"this":[36],"study,":[37],"we":[38],"present":[39],"first":[41],"attempt":[42],"automatically":[44],"infer":[45],"causality":[47,76],"ADR":[53,75],"from":[54],"electronic":[55],"health":[56],"records":[57],"(EHRs)":[58],"answering":[60,68],"Naranjo":[62,102],"questionnaire,":[63,103],"validated":[65],"question":[67],"set":[69],"used":[70],"domain":[72],"experts":[73],"for":[74],"assessment.":[77],"Using":[78],"physicians'":[79],"annotation":[80],"as":[81],"gold":[83],"standard,":[84],"our":[85],"proposed":[86],"joint":[87],"model,":[88],"which":[89],"uses":[90],"multi-task":[91],"learning":[92],"predict":[94],"answers":[96],"of":[97,100],"subset":[99],"significantly":[104],"outperforms":[105],"baseline":[107],"pipeline":[108],"model":[109],"with":[110],"good":[112],"margin,":[113],"achieving":[114],"macro-weighted":[116],"f-score":[117,124],"0.3652":[119],"-":[120,127],"0.5271":[121],"micro-weighted":[123],"0.9523":[126],"0.9918.":[128]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
