{"id":"https://openalex.org/W3207092178","doi":"https://doi.org/10.1145/3471571","title":"A Multi-attention Collaborative Deep Learning Approach for Blood Pressure Prediction","display_name":"A Multi-attention Collaborative Deep Learning Approach for Blood Pressure Prediction","publication_year":2021,"publication_date":"2021-10-18","ids":{"openalex":"https://openalex.org/W3207092178","doi":"https://doi.org/10.1145/3471571","mag":"3207092178"},"language":"en","primary_location":{"id":"doi:10.1145/3471571","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3471571","pdf_url":null,"source":{"id":"https://openalex.org/S4210170305","display_name":"ACM Transactions on Management Information Systems","issn_l":"2158-656X","issn":["2158-656X","2158-6578"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Management Information Systems","raw_type":"journal-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/A5039928779","display_name":"Luo He","orcid":"https://orcid.org/0000-0002-5693-262X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Luo He","raw_affiliation_strings":["Department of Management Science and Engineering, Schoolof Economics and Management, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Management Science and Engineering, Schoolof Economics and Management, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100332465","display_name":"Hongyan Liu","orcid":"https://orcid.org/0000-0002-4902-1078"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongyan Liu","raw_affiliation_strings":["Department of Management Science and Engineering, Schoolof Economics and Management, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Management Science and Engineering, Schoolof Economics and Management, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028928432","display_name":"Ying\u2010Hui Yang","orcid":"https://orcid.org/0000-0003-1555-4746"},"institutions":[{"id":"https://openalex.org/I2803209242","display_name":"University of California System","ror":"https://ror.org/00pjdza24","country_code":"US","type":"education","lineage":["https://openalex.org/I2803209242"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yinghui Yang","raw_affiliation_strings":["Graduate School of Management, University of California, California, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Management, University of California, California, USA","institution_ids":["https://openalex.org/I2803209242"]}]},{"author_position":"last","author":{"id":null,"display_name":"Bei Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bei Wang","raw_affiliation_strings":["School of Information, Renmin University of China, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information, Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9554,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.80230281,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"13","issue":"2","first_page":"1","last_page":"20"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9980999827384949,"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":0.9980999827384949,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9769999980926514,"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"}},{"id":"https://openalex.org/T10144","display_name":"Blood Pressure and Hypertension Studies","score":0.9750000238418579,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7920718789100647},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.665880024433136},{"id":"https://openalex.org/keywords/blood-pressure","display_name":"Blood pressure","score":0.6332330703735352},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6331291794776917},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5643404722213745},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4817650318145752},{"id":"https://openalex.org/keywords/predictive-power","display_name":"Predictive power","score":0.44881054759025574},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.41869112849235535},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2360670566558838},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.10536810755729675}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7920718789100647},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.665880024433136},{"id":"https://openalex.org/C84393581","wikidata":"https://www.wikidata.org/wiki/Q82642","display_name":"Blood pressure","level":2,"score":0.6332330703735352},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6331291794776917},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5643404722213745},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4817650318145752},{"id":"https://openalex.org/C2778136018","wikidata":"https://www.wikidata.org/wiki/Q10350689","display_name":"Predictive power","level":2,"score":0.44881054759025574},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.41869112849235535},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2360670566558838},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.10536810755729675},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3471571","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3471571","pdf_url":null,"source":{"id":"https://openalex.org/S4210170305","display_name":"ACM Transactions on Management Information Systems","issn_l":"2158-656X","issn":["2158-656X","2158-6578"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Management Information Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1016671922","display_name":"\u8003\u8651\u5fc3\u7406\u56e0\u7d20\u7684\u7528\u6237\u5728\u7ebf\u884c\u4e3a\u9884\u6d4b\u53ca\u5176\u5728\u63a8\u8350\u7cfb\u7edf\u4e2d\u7684\u5e94\u7528\u7814\u7a76","funder_award_id":"71771131","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":45,"referenced_works":["https://openalex.org/W179875071","https://openalex.org/W938417270","https://openalex.org/W1522301498","https://openalex.org/W1594031697","https://openalex.org/W1640842704","https://openalex.org/W1656898673","https://openalex.org/W1924770834","https://openalex.org/W1964357740","https://openalex.org/W2064675550","https://openalex.org/W2083817839","https://openalex.org/W2086706273","https://openalex.org/W2112028307","https://openalex.org/W2122585011","https://openalex.org/W2126831543","https://openalex.org/W2133564696","https://openalex.org/W2141966633","https://openalex.org/W2143612262","https://openalex.org/W2143747826","https://openalex.org/W2157331557","https://openalex.org/W2244501064","https://openalex.org/W2293185259","https://openalex.org/W2404505570","https://openalex.org/W2470673105","https://openalex.org/W2490300818","https://openalex.org/W2590462354","https://openalex.org/W2604922364","https://openalex.org/W2607113351","https://openalex.org/W2608498389","https://openalex.org/W2615062958","https://openalex.org/W2729161046","https://openalex.org/W2745673637","https://openalex.org/W2765541885","https://openalex.org/W2784502443","https://openalex.org/W2790522759","https://openalex.org/W2795354529","https://openalex.org/W2805089815","https://openalex.org/W2810652837","https://openalex.org/W2897630147","https://openalex.org/W2905761996","https://openalex.org/W2947596730","https://openalex.org/W2979946792","https://openalex.org/W2981683190","https://openalex.org/W2999337765","https://openalex.org/W3145054521","https://openalex.org/W4242603449"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4230611425","https://openalex.org/W2787993192","https://openalex.org/W2731899572","https://openalex.org/W2008493941","https://openalex.org/W4304166257","https://openalex.org/W4380075502","https://openalex.org/W3133294580","https://openalex.org/W4287660321"],"abstract_inverted_index":{"We":[0],"develop":[1],"a":[2,18,124,129],"deep":[3],"learning":[4],"model":[5,41,78,88],"based":[6,16],"on":[7,17],"Long":[8],"Short-term":[9],"Memory":[10],"(LSTM)":[11],"to":[12,56,71,80,94,133,143,177,188],"predict":[13,144,189],"blood":[14,117,135,145,161],"pressure":[15,146,162],"unique":[19],"data":[20,120],"set":[21],"collected":[22,122],"from":[23,68],"physical":[24,30,139,153],"examination":[25,31,85,140],"centers":[26],"capturing":[27],"comprehensive":[28],"multi-year":[29],"and":[32,60,137,175],"lab":[33],"results.":[34,62],"In":[35,63],"the":[36,73,77,81,138,157],"Multi-attention":[37],"Collaborative":[38],"Deep":[39],"Learning":[40],"(MAC-LSTM)":[42],"we":[43,50,65],"developed":[44],"for":[45,155,173],"this":[46],"type":[47],"of":[48,54,76,128],"data,":[49],"incorporate":[51],"three":[52],"types":[53],"attention":[55],"generate":[57],"more":[58],"explainable":[59],"accurate":[61],"addition,":[64],"leverage":[66],"information":[67],"similar":[69],"users":[70],"enhance":[72],"predictive":[74,91],"power":[75],"due":[79],"challenges":[82],"with":[83,111],"short":[84],"history.":[86],"Our":[87,119],"significantly":[89],"reduces":[90],"errors":[92],"compared":[93],"several":[95],"state-of-the-art":[96],"baseline":[97],"models.":[98],"Experimental":[99],"results":[100,164],"not":[101],"only":[102],"demonstrate":[103],"our":[104,160],"model\u2019s":[105],"superiority":[106],"but":[107],"also":[108],"provide":[109],"us":[110],"new":[112],"insights":[113],"about":[114],"factors":[115],"influencing":[116],"pressure.":[118],"is":[121],"in":[123,151,169],"natural":[125],"setting":[126,130],"instead":[127],"designed":[131],"specifically":[132],"study":[134],"pressure,":[136],"items":[141,149],"used":[142,168,187],"are":[147],"common":[148],"included":[150],"regular":[152],"examinations":[154],"all":[156],"users.":[158],"Therefore,":[159],"prediction":[163],"can":[165,185],"be":[166,186],"easily":[167],"an":[170],"alert":[171],"system":[172],"patients":[174],"doctors":[176],"plan":[178],"prevention":[179],"or":[180],"intervention.":[181],"The":[182],"same":[183],"approach":[184],"other":[190],"health-related":[191],"indexes":[192],"such":[193],"as":[194],"BMI.":[195]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
