{"id":"https://openalex.org/W2690721124","doi":"https://doi.org/10.1145/3097983.3098088","title":"Dipole","display_name":"Dipole","publication_year":2017,"publication_date":"2017-08-04","ids":{"openalex":"https://openalex.org/W2690721124","doi":"https://doi.org/10.1145/3097983.3098088","mag":"2690721124"},"language":"en","primary_location":{"id":"doi:10.1145/3097983.3098088","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3097983.3098088","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1706.05764","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Fenglong Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I63190737","display_name":"University at Buffalo, State University of New York","ror":"https://ror.org/01y64my43","country_code":"US","type":"education","lineage":["https://openalex.org/I63190737"]},{"id":"https://openalex.org/I4210132870","display_name":"Xerox (United States)","ror":"https://ror.org/03jwzwq24","country_code":"US","type":"company","lineage":["https://openalex.org/I4210132870"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Fenglong Ma","raw_affiliation_strings":["SUNY Buffalo & Xerox, Buffalo, NY, USA"],"affiliations":[{"raw_affiliation_string":"SUNY Buffalo & Xerox, Buffalo, NY, USA","institution_ids":["https://openalex.org/I4210132870","https://openalex.org/I63190737"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Radha Chitta","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Radha Chitta","raw_affiliation_strings":["Conduent Labs US, Rochester, NY, USA"],"affiliations":[{"raw_affiliation_string":"Conduent Labs US, Rochester, NY, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jing Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jing Zhou","raw_affiliation_strings":["Conduent Labs US, Rochester, NY, USA"],"affiliations":[{"raw_affiliation_string":"Conduent Labs US, Rochester, NY, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Quanzeng You","orcid":null},"institutions":[{"id":"https://openalex.org/I5388228","display_name":"University of Rochester","ror":"https://ror.org/022kthw22","country_code":"US","type":"education","lineage":["https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Quanzeng You","raw_affiliation_strings":["University of Rochester, Rochester, NY, USA"],"affiliations":[{"raw_affiliation_string":"University of Rochester, Rochester, NY, USA","institution_ids":["https://openalex.org/I5388228"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Tong Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I4210132870","display_name":"Xerox (United States)","ror":"https://ror.org/03jwzwq24","country_code":"US","type":"company","lineage":["https://openalex.org/I4210132870"]},{"id":"https://openalex.org/I4401726811","display_name":"Hartford Financial Services (United States)","ror":"https://ror.org/00mwq1g96","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726811"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tong Sun","raw_affiliation_strings":["United Technologies Research Center & Xerox, East Hartford, CT, USA"],"affiliations":[{"raw_affiliation_string":"United Technologies Research Center & Xerox, East Hartford, CT, USA","institution_ids":["https://openalex.org/I4210132870","https://openalex.org/I4401726811"]}]},{"author_position":"last","author":{"id":null,"display_name":"Jing Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I63190737","display_name":"University at Buffalo, State University of New York","ror":"https://ror.org/01y64my43","country_code":"US","type":"education","lineage":["https://openalex.org/I63190737"]},{"id":"https://openalex.org/I115441956","display_name":"Buffalo State University","ror":"https://ror.org/05ms04m92","country_code":"US","type":"education","lineage":["https://openalex.org/I115441956"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jing Gao","raw_affiliation_strings":["SUNY Buffalo, Buffalo, NY, USA"],"affiliations":[{"raw_affiliation_string":"SUNY Buffalo, Buffalo, NY, USA","institution_ids":["https://openalex.org/I115441956","https://openalex.org/I63190737"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210132870","https://openalex.org/I63190737"],"apc_list":null,"apc_paid":null,"fwci":30.9711,"has_fulltext":false,"cited_by_count":487,"citation_normalized_percentile":{"value":0.99704786,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1903","last_page":"1911"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9998999834060669,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9998999834060669,"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/T10028","display_name":"Topic Modeling","score":0.9941999912261963,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.986299991607666,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5728999972343445},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.48179998993873596},{"id":"https://openalex.org/keywords/health-records","display_name":"Health records","score":0.4666999876499176},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.4611000120639801},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4016000032424927},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.38280001282691956},{"id":"https://openalex.org/keywords/electronic-health-record","display_name":"Electronic health record","score":0.36079999804496765},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.3431999981403351}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5927000045776367},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5728999972343445},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5519999861717224},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.48179998993873596},{"id":"https://openalex.org/C3019952477","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Health records","level":3,"score":0.4666999876499176},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46309998631477356},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.4611000120639801},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4016000032424927},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.38280001282691956},{"id":"https://openalex.org/C3020144179","wikidata":"https://www.wikidata.org/wiki/Q10871684","display_name":"Electronic health record","level":3,"score":0.36079999804496765},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3431999981403351},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3269999921321869},{"id":"https://openalex.org/C141679102","wikidata":"https://www.wikidata.org/wiki/Q3517747","display_name":"Temporality","level":2,"score":0.319599986076355},{"id":"https://openalex.org/C173523689","wikidata":"https://www.wikidata.org/wiki/Q215589","display_name":"Dipole","level":2,"score":0.3165000081062317},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.30880001187324524},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.29109999537467957},{"id":"https://openalex.org/C145642194","wikidata":"https://www.wikidata.org/wiki/Q870895","display_name":"Health informatics","level":3,"score":0.29100000858306885},{"id":"https://openalex.org/C3018822202","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Patient data","level":2,"score":0.2809999883174896},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C195910791","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Medical record","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C2780799671","wikidata":"https://www.wikidata.org/wiki/Q17087362","display_name":"Transient (computer programming)","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3097983.3098088","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3097983.3098088","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1706.05764","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1706.05764","pdf_url":"https://arxiv.org/pdf/1706.05764","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":{"id":"pmh:oai:arXiv.org:1706.05764","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1706.05764","pdf_url":"https://arxiv.org/pdf/1706.05764","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G828023077","display_name":null,"funder_award_id":"IIS-1514204","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1902237438","https://openalex.org/W1998249118","https://openalex.org/W2004910511","https://openalex.org/W2005464046","https://openalex.org/W2031250362","https://openalex.org/W2042954874","https://openalex.org/W2064675550","https://openalex.org/W2107878631","https://openalex.org/W2131774270","https://openalex.org/W2152175008","https://openalex.org/W2242522500","https://openalex.org/W2302086703","https://openalex.org/W2557074642"],"related_works":[],"abstract_inverted_index":{"Predicting":[0],"the":[1,8,20,58,70,95,102,105,111,118,157,161,165,176,182,185,191,201,223,229,234],"future":[2,145,166],"health":[3,146],"information":[4,158],"of":[5,22,29,31,63,107,113,159,178],"patients":[6],"from":[7,101],"historical":[9],"Electronic":[10],"Health":[11],"Records":[12],"(EHR)":[13],"is":[14,115],"a":[15],"core":[16],"research":[17],"task":[18,54],"in":[19],"development":[21],"personalized":[23],"healthcare.":[24],"Patient":[25],"EHR":[26,65,86,219],"data":[27,66,87],"consist":[28],"sequences":[30,114],"visits":[32,122,163,180],"over":[33],"time,":[34],"where":[35],"each":[36],"visit":[37],"contains":[38],"multiple":[39],"medical":[40,203,211],"codes,":[41],"including":[42],"diagnosis,":[43],"medication,":[44],"and":[45,60,67,88,117,139,164,168,239],"procedure":[46],"codes.":[47],"The":[48],"most":[49],"important":[50],"challenges":[51],"for":[52,142,181],"this":[53,76],"are":[55,123,207],"to":[56,68,84,93,154,174,199],"model":[57,85,141],"temporality":[59],"high":[61],"dimensionality":[62],"sequential":[64],"interpret":[69,94,190,200],"prediction":[71,192,230,237],"results.":[72,96],"Existing":[73],"work":[74],"solves":[75],"problem":[77,103],"by":[78,125,210],"employing":[79],"recurrent":[80,151],"neural":[81,152],"networks":[82,153],"(RNNs)":[83],"utilizing":[89],"simple":[90,138],"attention":[91,172,186],"mechanism":[92],"However,":[97],"RNN-based":[98,127],"approaches":[99,238],"suffer":[100],"that":[104,222],"performance":[106],"RNNs":[108],"drops":[109],"when":[110],"length":[112],"large,":[116],"relationships":[119,177],"between":[120],"subsequent":[121],"ignored":[124],"current":[126],"approaches.":[128],"To":[129],"address":[130],"these":[131],"issues,":[132],"we":[133],"propose":[134],"Dipole,":[135],"an":[136],"end-to-end,":[137],"robust":[140],"predicting":[143],"patients'":[144],"information.":[147],"Dipole":[148,188,195,225],"employs":[149],"bidirectional":[150],"remember":[155],"all":[156],"both":[160],"past":[162],"visits,":[167],"it":[169],"introduces":[170],"three":[171],"mechanisms":[173],"measure":[175],"different":[179],"prediction.":[183],"With":[184],"mechanisms,":[187],"can":[189,226],"results":[193,214],"effectively.":[194],"also":[196],"allows":[197],"us":[198],"learned":[202],"code":[204],"representations":[205],"which":[206],"confirmed":[208],"positively":[209],"experts.":[212],"Experimental":[213],"on":[215],"two":[216],"real":[217],"world":[218],"datasets":[220],"show":[221],"proposed":[224],"significantly":[227],"improve":[228],"accuracy":[231],"compared":[232],"with":[233],"state-of-the-art":[235],"diagnosis":[236],"provide":[240],"clinically":[241],"meaningful":[242],"interpretation.":[243]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":48},{"year":2024,"cited_by_count":62},{"year":2023,"cited_by_count":70},{"year":2022,"cited_by_count":70},{"year":2021,"cited_by_count":81},{"year":2020,"cited_by_count":64},{"year":2019,"cited_by_count":51},{"year":2018,"cited_by_count":30},{"year":2017,"cited_by_count":4}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2017-06-30T00:00:00"}