{"id":"https://openalex.org/W3172867975","doi":"https://doi.org/10.1109/jbhi.2021.3089459","title":"Exploiting Causality for Improved Prediction of Patient Volumes by Gaussian Processes","display_name":"Exploiting Causality for Improved Prediction of Patient Volumes by Gaussian Processes","publication_year":2021,"publication_date":"2021-06-15","ids":{"openalex":"https://openalex.org/W3172867975","doi":"https://doi.org/10.1109/jbhi.2021.3089459","mag":"3172867975","pmid":"https://pubmed.ncbi.nlm.nih.gov/34129511"},"language":"en","primary_location":{"id":"doi:10.1109/jbhi.2021.3089459","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2021.3089459","pdf_url":null,"source":{"id":"https://openalex.org/S2495854775","display_name":"IEEE Journal of Biomedical and Health Informatics","issn_l":"2168-2194","issn":["2168-2194","2168-2208"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Biomedical and Health Informatics","raw_type":"journal-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/A5040073385","display_name":"Guanchao Feng","orcid":"https://orcid.org/0000-0001-6021-0243"},"institutions":[{"id":"https://openalex.org/I4210108991","display_name":"IQVIA (United States)","ror":"https://ror.org/01mk44223","country_code":"US","type":"company","lineage":["https://openalex.org/I4210108991"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Guanchao Feng","raw_affiliation_strings":["IQVIA, Plymouth Meeting, PA, USA"],"affiliations":[{"raw_affiliation_string":"IQVIA, Plymouth Meeting, PA, USA","institution_ids":["https://openalex.org/I4210108991"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006750969","display_name":"Kezi Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210108991","display_name":"IQVIA (United States)","ror":"https://ror.org/01mk44223","country_code":"US","type":"company","lineage":["https://openalex.org/I4210108991"]},{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kezi Yu","raw_affiliation_strings":["Google, Mountain View, CA, USA","IQVIA, Plymouth Meeting, PA, USA"],"affiliations":[{"raw_affiliation_string":"Google, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"IQVIA, Plymouth Meeting, PA, USA","institution_ids":["https://openalex.org/I4210108991"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100398996","display_name":"Yunlong Wang","orcid":"https://orcid.org/0000-0003-2186-9306"},"institutions":[{"id":"https://openalex.org/I4210108991","display_name":"IQVIA (United States)","ror":"https://ror.org/01mk44223","country_code":"US","type":"company","lineage":["https://openalex.org/I4210108991"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yunlong Wang","raw_affiliation_strings":["IQVIA, Plymouth Meeting, PA, USA"],"affiliations":[{"raw_affiliation_string":"IQVIA, Plymouth Meeting, PA, USA","institution_ids":["https://openalex.org/I4210108991"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080930272","display_name":"Yilian Yuan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210108991","display_name":"IQVIA (United States)","ror":"https://ror.org/01mk44223","country_code":"US","type":"company","lineage":["https://openalex.org/I4210108991"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yilian Yuan","raw_affiliation_strings":["IQVIA, Plymouth Meeting, PA, USA"],"affiliations":[{"raw_affiliation_string":"IQVIA, Plymouth Meeting, PA, USA","institution_ids":["https://openalex.org/I4210108991"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006962534","display_name":"Petar M. Djuri\u0107","orcid":"https://orcid.org/0000-0001-7791-3199"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Petar M. Djuric","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA","institution_ids":["https://openalex.org/I59553526"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5040073385"],"corresponding_institution_ids":["https://openalex.org/I4210108991"],"apc_list":null,"apc_paid":null,"fwci":0.7618,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.70317782,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"25","issue":"7","first_page":"2487","last_page":"2496"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.989300012588501,"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"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.989300012588501,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9810000061988831,"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":0.9628000259399414,"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/spurious-relationship","display_name":"Spurious relationship","score":0.7970618009567261},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6945928335189819},{"id":"https://openalex.org/keywords/causality","display_name":"Causality (physics)","score":0.61359703540802},{"id":"https://openalex.org/keywords/granger-causality","display_name":"Granger causality","score":0.5340503454208374},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5207076668739319},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.510739803314209},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.510461688041687},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48125049471855164},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4605400264263153},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.44616666436195374},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.4109601080417633},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14811891317367554}],"concepts":[{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.7970618009567261},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6945928335189819},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.61359703540802},{"id":"https://openalex.org/C129824826","wikidata":"https://www.wikidata.org/wiki/Q2630107","display_name":"Granger causality","level":2,"score":0.5340503454208374},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5207076668739319},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.510739803314209},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.510461688041687},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48125049471855164},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4605400264263153},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.44616666436195374},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.4109601080417633},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14811891317367554},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D015984","descriptor_name":"Causality","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D015984","descriptor_name":"Causality","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D015984","descriptor_name":"Causality","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016011","descriptor_name":"Normal Distribution","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016011","descriptor_name":"Normal Distribution","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016011","descriptor_name":"Normal Distribution","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":2,"locations":[{"id":"doi:10.1109/jbhi.2021.3089459","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2021.3089459","pdf_url":null,"source":{"id":"https://openalex.org/S2495854775","display_name":"IEEE Journal of Biomedical and Health Informatics","issn_l":"2168-2194","issn":["2168-2194","2168-2208"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Biomedical and Health Informatics","raw_type":"journal-article"},{"id":"pmid:34129511","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34129511","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":"IEEE journal of biomedical and health informatics","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":69,"referenced_works":["https://openalex.org/W66306528","https://openalex.org/W137285897","https://openalex.org/W1480376833","https://openalex.org/W1549386224","https://openalex.org/W1558736232","https://openalex.org/W1586335931","https://openalex.org/W1601795611","https://openalex.org/W1663973292","https://openalex.org/W1964940342","https://openalex.org/W1991001067","https://openalex.org/W1996071783","https://openalex.org/W1998367480","https://openalex.org/W2015381017","https://openalex.org/W2015687261","https://openalex.org/W2030375820","https://openalex.org/W2031365860","https://openalex.org/W2040704490","https://openalex.org/W2047673811","https://openalex.org/W2051151491","https://openalex.org/W2053059880","https://openalex.org/W2083278075","https://openalex.org/W2088341700","https://openalex.org/W2099768828","https://openalex.org/W2118561568","https://openalex.org/W2156571267","https://openalex.org/W2166650852","https://openalex.org/W2167101736","https://openalex.org/W2169672624","https://openalex.org/W2174574259","https://openalex.org/W2178225550","https://openalex.org/W2199558101","https://openalex.org/W2289592566","https://openalex.org/W2295598076","https://openalex.org/W2295698653","https://openalex.org/W2587455020","https://openalex.org/W2760921629","https://openalex.org/W2770975716","https://openalex.org/W2780155251","https://openalex.org/W2783890820","https://openalex.org/W2896556344","https://openalex.org/W2899523379","https://openalex.org/W2907554505","https://openalex.org/W2912279788","https://openalex.org/W2919115771","https://openalex.org/W2926309618","https://openalex.org/W2939748664","https://openalex.org/W2961475402","https://openalex.org/W2962842733","https://openalex.org/W2999030003","https://openalex.org/W3015606100","https://openalex.org/W3091533194","https://openalex.org/W3097993951","https://openalex.org/W3102476541","https://openalex.org/W3104099238","https://openalex.org/W3105296179","https://openalex.org/W3105524694","https://openalex.org/W3192241548","https://openalex.org/W4234292328","https://openalex.org/W4289083414","https://openalex.org/W4289236186","https://openalex.org/W6602631663","https://openalex.org/W6605566567","https://openalex.org/W6632829569","https://openalex.org/W6663733957","https://openalex.org/W6674989108","https://openalex.org/W6682904970","https://openalex.org/W6684893893","https://openalex.org/W6761772066","https://openalex.org/W6835052771"],"related_works":["https://openalex.org/W3113091479","https://openalex.org/W2162899405","https://openalex.org/W2035792466","https://openalex.org/W2977645287","https://openalex.org/W2352405217","https://openalex.org/W4251418261","https://openalex.org/W1972675643","https://openalex.org/W2154758532","https://openalex.org/W1964286703","https://openalex.org/W2169866437"],"abstract_inverted_index":{"Estimating":[0],"and":[1,13,35,48,64,115,119,205,248],"surveillance":[2],"volumes":[3,23,47,108,202],"of":[4,7,21,42,75,109,129,181,186,201,203,239],"patients":[5,110,204],"are":[6,122,166,224],"great":[8],"importance":[9],"for":[10,94,149,174],"public":[11],"health":[12],"resource":[14],"allocation.":[15],"In":[16,125],"many":[17,27],"situations,":[18],"the":[19,56,79,117,120,142,160,176,184,213,222,227,244],"change":[20],"these":[22],"is":[24,40,81,105,153],"correlated":[25],"with":[26,246],"factors,":[28],"e.g.,":[29],"seasonal":[30],"environmental":[31],"variables,":[32],"medicine":[33],"sales,":[34],"patient":[36,46,240],"medical":[37],"claims.":[38],"It":[39],"often":[41],"interest":[43,104],"to":[44,49,155,235],"predict":[45],"that":[50,111,196],"end,":[51],"discovering":[52],"causalities":[53,98],"can":[54,66,208,229],"improve":[55],"prediction":[57,76,80,95],"accuracy.":[58],"Correlations":[59],"do":[60],"not":[61],"imply":[62],"causations":[63],"they":[65],"be":[67,156,210,230],"spurious,":[68],"which":[69,152],"in":[70,87,106,190,199,219],"turn":[71],"may":[72],"entail":[73],"deterioration":[74],"performance":[77],"if":[78],"based":[82,96],"on":[83,97],"them.":[84],"By":[85],"contrast,":[86],"this":[88],"paper,":[89],"we":[90,133,140,169],"propose":[91],"an":[92],"approach":[93,228],"discovered":[99],"by":[100],"Gaussian":[101,143],"processes.":[102],"Our":[103],"estimating":[107],"suffer":[112],"from":[113,183],"allergy":[114],"where":[116],"model":[118],"results":[121],"highly":[123],"interpretable.":[124],"selecting":[126,175],"features,":[127],"instead":[128],"only":[130],"using":[131],"correlation,":[132],"take":[134],"causal":[135,150],"information":[136],"into":[137],"account.":[138],"Specifically,":[139],"adopt":[141],"processes-based":[144],"convergent":[145],"cross":[146],"mapping":[147],"framework":[148],"discovery":[151],"proven":[154],"more":[157],"reliable":[158],"than":[159],"Granger":[161],"causality":[162],"when":[163,221],"time":[164],"series":[165],"coupled.":[167],"Moreover,":[168],"introduce":[170],"a":[171,187,191],"novel":[172],"method":[173,215,245],"history":[177],"or":[178],"look-back":[179],"length":[180],"features":[182],"perspective":[185],"dynamical":[188],"system":[189],"principled":[192],"manner.":[193],"The":[194],"quasi-periodicities":[195],"commonly":[197],"exist":[198],"observations":[200],"environment":[206],"variables":[207],"readily":[209],"accommodated.":[211],"Further,":[212],"proposed":[214],"performs":[216],"well":[217],"even":[218],"cases":[220],"data":[223],"scarce.":[225],"Also,":[226],"modified":[231],"without":[232],"much":[233],"difficulty":[234],"forecast":[236],"other":[237],"types":[238],"volumes.":[241],"We":[242],"validate":[243],"synthetic":[247],"real-world":[249],"datasets.":[250]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
