{"id":"https://openalex.org/W3211310242","doi":"https://doi.org/10.1145/3459637.3482064","title":"BiCMTS: Bidirectional Coupled Multivariate Learning of Irregular Time Series with Missing Values","display_name":"BiCMTS: Bidirectional Coupled Multivariate Learning of Irregular Time Series with Missing Values","publication_year":2021,"publication_date":"2021-10-26","ids":{"openalex":"https://openalex.org/W3211310242","doi":"https://doi.org/10.1145/3459637.3482064","mag":"3211310242"},"language":"en","primary_location":{"id":"doi:10.1145/3459637.3482064","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482064","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"},"type":"article","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/A5039023737","display_name":"Qinfen Wang","orcid":"https://orcid.org/0009-0009-1658-9298"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qinfen Wang","raw_affiliation_strings":["Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101681574","display_name":"Siyuan Ren","orcid":"https://orcid.org/0000-0001-9265-3161"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siyuan Ren","raw_affiliation_strings":["Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100670074","display_name":"Yong Xia","orcid":"https://orcid.org/0000-0001-9273-2847"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Xia","raw_affiliation_strings":["Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000798681","display_name":"Longbing Cao","orcid":null},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Longbing Cao","raw_affiliation_strings":["University of Technology Sydney, Sydney, NSW, Australia"],"affiliations":[{"raw_affiliation_string":"University of Technology Sydney, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I114017466"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5039023737"],"corresponding_institution_ids":["https://openalex.org/I17145004"],"apc_list":null,"apc_paid":null,"fwci":0.5439,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.73335505,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"3493","last_page":"3497"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9882000088691711,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9478999972343445,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.8522878885269165},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.7620318531990051},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.729097843170166},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.723137617111206},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6878474950790405},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6212629079818726},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6170108914375305},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5828534960746765},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4639522433280945},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4390212297439575},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43539202213287354},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3719910979270935},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32386356592178345}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.8522878885269165},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.7620318531990051},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.729097843170166},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.723137617111206},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6878474950790405},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6212629079818726},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6170108914375305},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5828534960746765},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4639522433280945},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4390212297439575},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43539202213287354},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3719910979270935},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32386356592178345},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3459637.3482064","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482064","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.800000011920929,"display_name":"Good health and well-being"}],"awards":[{"id":"https://openalex.org/G3201804429","display_name":null,"funder_award_id":"61771397","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1480376833","https://openalex.org/W1522684182","https://openalex.org/W2013619732","https://openalex.org/W2469618837","https://openalex.org/W2557283755","https://openalex.org/W2770565645","https://openalex.org/W2788057792","https://openalex.org/W2803805253","https://openalex.org/W2890686416","https://openalex.org/W2964010366","https://openalex.org/W2973432116","https://openalex.org/W2997705255","https://openalex.org/W3043922872","https://openalex.org/W3101973032","https://openalex.org/W3174697924"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W2055961818","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549"],"abstract_inverted_index":{"Multivariate":[0],"time":[1,58,80],"series":[2,81,96,138],"(MTS)":[3],"such":[4,25],"as":[5],"multiple":[6],"medical":[7],"measures":[8],"in":[9,130,141],"intensive":[10],"care":[11],"units":[12],"(ICU)":[13],"are":[14,99],"irregularly":[15],"acquired":[16],"and":[17,46,74,84,94,112,126,136],"hold":[18],"missing":[19,29,103],"values.":[20,104],"Conducting":[21],"learning":[22,67],"tasks":[23],"on":[24,108],"irregular":[26],"MTS":[27,43,66,86],"with":[28],"values,":[30],"e.g.,":[31],"predicting":[32],"the":[33,53,90,124,133],"mortality":[34,113],"of":[35,122],"ICU":[36,116],"patients,":[37,117],"poses":[38],"significant":[39],"challenge":[40],"to":[41,70,101],"existing":[42],"forecasting":[44],"models":[45],"recurrent":[47],"neural":[48],"networks":[49],"(RNNs),":[50],"which":[51],"capture":[52],"temporal":[54],"dependencies":[55],"within":[56,78],"a":[57,63,79,119],"series.":[59],"This":[60],"work":[61],"proposes":[62],"bidirectional":[64,92],"coupled":[65],"(BiCMTS)":[68],"method":[69],"represent":[71],"both":[72,109],"forward":[73],"backward":[75],"value":[76,139],"couplings":[77,140],"by":[82,87,132],"RNNs":[83,131],"between":[85],"self-attention":[88],"networks;":[89],"learned":[91],"intra-":[93,135],"inter-time":[95,137],"coupling":[97],"representations":[98],"fused":[100],"estimate":[102],"We":[105],"test":[106],"BiCMTS":[107],"data":[110],"imputation":[111],"prediction":[114],"for":[115],"showing":[118],"great":[120],"potential":[121],"leveraging":[123],"deep":[125],"hidden":[127],"relations":[128],"captured":[129],"BiCMTS-learned":[134],"MTS.":[142]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
