{"id":"https://openalex.org/W4312397225","doi":"https://doi.org/10.1109/access.2022.3211334","title":"A Multi-Headed Transformer Approach for Predicting the Patient\u2019s Clinical Time-Series Variables From Charted Vital Signs","display_name":"A Multi-Headed Transformer Approach for Predicting the Patient\u2019s Clinical Time-Series Variables From Charted Vital Signs","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4312397225","doi":"https://doi.org/10.1109/access.2022.3211334"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3211334","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3211334","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09907018.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09907018.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080143856","display_name":"Gaspard Harerimana","orcid":"https://orcid.org/0000-0003-4761-562X"},"institutions":[{"id":"https://openalex.org/I4210130200","display_name":"Carnegie Mellon University Africa","ror":"https://ror.org/02f33m021","country_code":"RW","type":"education","lineage":["https://openalex.org/I4210130200","https://openalex.org/I74973139"]}],"countries":["RW"],"is_corresponding":false,"raw_author_name":"Gaspard Harerimana","raw_affiliation_strings":["Department of Information Technology, Carnegie Mellon University, Kigali, Rwanda"],"raw_orcid":"https://orcid.org/0000-0003-4761-562X","affiliations":[{"raw_affiliation_string":"Department of Information Technology, Carnegie Mellon University, Kigali, Rwanda","institution_ids":["https://openalex.org/I4210130200"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100726106","display_name":"Jong Wook Kim","orcid":"https://orcid.org/0000-0001-8373-1893"},"institutions":[{"id":"https://openalex.org/I157264075","display_name":"Sangmyung University","ror":"https://ror.org/01x4whx42","country_code":"KR","type":"education","lineage":["https://openalex.org/I157264075"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jong Wook Kim","raw_affiliation_strings":["Department of Computer Science, Sangmyung University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0001-8373-1893","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Sangmyung University, Seoul, South Korea","institution_ids":["https://openalex.org/I157264075"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067151609","display_name":"Beakcheol Jang","orcid":"https://orcid.org/0000-0002-3911-5935"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Beakcheol Jang","raw_affiliation_strings":["Graduate School of Information, Yonsei University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-3911-5935","affiliations":[{"raw_affiliation_string":"Graduate School of Information, Yonsei University, Seoul, South Korea","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.3205,"has_fulltext":true,"cited_by_count":23,"citation_normalized_percentile":{"value":0.8980878,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"10","issue":null,"first_page":"105993","last_page":"106004"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9966999888420105,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.958899974822998,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.7552471160888672},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6974658370018005},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.61332768201828},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5985007882118225},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.5898137092590332},{"id":"https://openalex.org/keywords/vital-signs","display_name":"Vital signs","score":0.5634133815765381},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5432146787643433},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5319044589996338},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5036267638206482},{"id":"https://openalex.org/keywords/recurrence-plot","display_name":"Recurrence plot","score":0.4682563245296478},{"id":"https://openalex.org/keywords/health-records","display_name":"Health records","score":0.4652705192565918},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.44970035552978516},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42738714814186096},{"id":"https://openalex.org/keywords/f1-score","display_name":"F1 score","score":0.4268152117729187},{"id":"https://openalex.org/keywords/electronic-health-record","display_name":"Electronic health record","score":0.41811349987983704},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3275958299636841},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.20711055397987366},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.20461305975914001},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09989598393440247}],"concepts":[{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.7552471160888672},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6974658370018005},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.61332768201828},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5985007882118225},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.5898137092590332},{"id":"https://openalex.org/C2776890885","wikidata":"https://www.wikidata.org/wiki/Q1067560","display_name":"Vital signs","level":2,"score":0.5634133815765381},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5432146787643433},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5319044589996338},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5036267638206482},{"id":"https://openalex.org/C173134143","wikidata":"https://www.wikidata.org/wiki/Q2797953","display_name":"Recurrence plot","level":3,"score":0.4682563245296478},{"id":"https://openalex.org/C3019952477","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Health records","level":3,"score":0.4652705192565918},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.44970035552978516},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42738714814186096},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.4268152117729187},{"id":"https://openalex.org/C3020144179","wikidata":"https://www.wikidata.org/wiki/Q10871684","display_name":"Electronic health record","level":3,"score":0.41811349987983704},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3275958299636841},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.20711055397987366},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.20461305975914001},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09989598393440247},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C141071460","wikidata":"https://www.wikidata.org/wiki/Q40821","display_name":"Surgery","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3211334","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3211334","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09907018.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:4ba37274c9fb4b9f8237c178935c39fd","is_oa":true,"landing_page_url":"https://doaj.org/article/4ba37274c9fb4b9f8237c178935c39fd","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 105993-106004 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3211334","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3211334","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09907018.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6899999976158142,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[{"id":"https://openalex.org/G6126467850","display_name":null,"funder_award_id":"NRF-2022R1F1A1063961","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320309369","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44"},{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4312397225.pdf","grobid_xml":"https://content.openalex.org/works/W4312397225.grobid-xml"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1832693441","https://openalex.org/W1836465849","https://openalex.org/W1972296981","https://openalex.org/W2109453625","https://openalex.org/W2120156715","https://openalex.org/W2148143831","https://openalex.org/W2177178155","https://openalex.org/W2314720829","https://openalex.org/W2396881363","https://openalex.org/W2506321621","https://openalex.org/W2510642588","https://openalex.org/W2518582440","https://openalex.org/W2594265094","https://openalex.org/W2728116991","https://openalex.org/W2767989436","https://openalex.org/W2773560649","https://openalex.org/W2791076086","https://openalex.org/W2892009249","https://openalex.org/W2917049430","https://openalex.org/W2924333399","https://openalex.org/W2941599692","https://openalex.org/W2945248062","https://openalex.org/W2946775356","https://openalex.org/W2953675148","https://openalex.org/W2956033525","https://openalex.org/W2956817960","https://openalex.org/W2963532813","https://openalex.org/W2973071945","https://openalex.org/W2974669915","https://openalex.org/W2988244882","https://openalex.org/W2995685608","https://openalex.org/W3008191852","https://openalex.org/W3101973032","https://openalex.org/W6631190155","https://openalex.org/W6638667902","https://openalex.org/W6680930200","https://openalex.org/W6684665197","https://openalex.org/W6685675117","https://openalex.org/W6739901393","https://openalex.org/W6740612379","https://openalex.org/W6746213830","https://openalex.org/W6760760346","https://openalex.org/W6763007057","https://openalex.org/W6764679822","https://openalex.org/W6765228583","https://openalex.org/W6780226713","https://openalex.org/W6785528530"],"related_works":["https://openalex.org/W187932805","https://openalex.org/W2909369938","https://openalex.org/W4392490004","https://openalex.org/W1641026212","https://openalex.org/W4402738807","https://openalex.org/W2911982698","https://openalex.org/W2323588885","https://openalex.org/W3047677938","https://openalex.org/W2087134418","https://openalex.org/W2078646730"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"has":[2],"progressively":[3],"been":[4],"the":[5,13,22,28,45,49,59,84,88,103,109,127,133,151,158,163,166,170,179,187,191,196,202,223,240,248,253,261,265,271],"spotlight":[6],"of":[7,48,105,165,217,227,237],"innovations":[8],"that":[9,17,129,149,177],"aim":[10],"to":[11,26,75,81,97,125],"leverage":[12],"clinical":[14,93,138],"time-series":[15],"data":[16],"are":[18,73,130],"longitudinally":[19],"recorded":[20,250],"in":[21,68,239],"Electronic":[23],"Health":[24],"Records(EHR)":[25],"forecast":[27],"patient\u2019s":[29,152,184],"survival":[30],"and":[31,77,79,120,186,219,234,257],"vital":[32,230],"signs":[33],"deterioration.":[34],"However,":[35],"their":[36,42,98],"recording":[37],"velocity,":[38],"as":[39,41,199,201,270],"well":[40,200,246],"noisiness,":[43],"hinder":[44],"proper":[46],"adoption":[47],"recently":[50],"proposed":[51,113],"benchmarks.":[52],"The":[53,212,243],"Recurrent":[54],"Neural":[55],"Networks":[56],"(RNN)":[57],"especially":[58],"Long-short":[60],"Term":[61],"Memory":[62],"(LSTMs)":[63],"have":[64],"achieved":[65,121],"better":[66],"results":[67],"recent":[69],"studies":[70],"but":[71],"they":[72],"hard":[74],"train":[76],"interpret":[78],"fail":[80],"properly":[82],"capture":[83],"long-term":[85],"dependencies.":[86],"Moreover,":[87],"RNNs":[89,134],"suffer":[90],"greatly":[91],"with":[92],"time":[94,139,154,231],"series":[95,140,155,232],"due":[96],"sequential":[99],"processing":[100],"which":[101,144],"cripples":[102],"prospect":[104],"parallel":[106],"processing.":[107],"Recently":[108],"Transformer":[110,142],"approach":[111],"was":[112],"for":[114,173,229,247,264],"Natural":[115],"Language":[116],"Processing":[117],"(NLP)":[118],"tasks":[119,176],"state-of-the-art":[122],"results.":[123],"Hence":[124],"tackle":[126],"drawbacks":[128],"suffered":[131],"by":[132],"we":[135,168],"propose":[136],"a":[137,146],"Multi-head":[141],"(MHT),":[143],"is":[145],"transformer-based":[147],"model":[148,167,172,207,244],"forecasts":[150],"future":[153],"variables":[156,251],"using":[157],"vitals":[159],"signs.":[160],"To":[161],"prove":[162],"generalization":[164],"use":[169],"same":[171],"other":[174],"critical":[175],"describe":[178],"Intensive":[180],"Care":[181],"Unit":[182],"(ICU)":[183],"progression":[185],"associated":[188],"risks":[189],"like":[190,252,260],"remaining":[192],"Length":[193],"Of":[194],"Stay(LoS),":[195],"In-hospital":[197],"Mortality":[198],"24":[203],"hours":[204],"mortality.":[205],"Our":[206],"achieves":[208],"an":[209,220,235],"Area":[210,221],"Under":[211,222],"Curve-Receiver":[213],"Operating":[214],"Characteristics(":[215],"AUC-ROC)":[216],"0.98":[218],"Curve,":[224],"Precision-Recall":[225],"(AUC-PR)":[226],"0.424":[228],"prediction,":[233],"AUC-ROC":[236],"0.875":[238],"mortality":[241],"prediction.":[242],"performs":[245,258],"frequently":[249],"Heart":[254],"Rate":[255],"(HR)":[256],"barely":[259],"LSTM":[262],"counterparts":[263],"intermittently":[266],"captured":[267],"records":[268],"such":[269],"White":[272],"Blood":[273],"Count":[274],"(WBC).":[275]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}