{"id":"https://openalex.org/W4406611775","doi":"https://doi.org/10.1109/wsc63780.2024.10838870","title":"Bayesian Optimization for Clinical Pathway Decomposition From Aggregate Data","display_name":"Bayesian Optimization for Clinical Pathway Decomposition From Aggregate Data","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406611775","doi":"https://doi.org/10.1109/wsc63780.2024.10838870"},"language":"en","primary_location":{"id":"doi:10.1109/wsc63780.2024.10838870","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc63780.2024.10838870","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 Winter Simulation Conference (WSC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/10044/1/115546","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080111195","display_name":"William Plumb","orcid":"https://orcid.org/0000-0002-7858-9607"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"William Plumb","raw_affiliation_strings":["Imperial College,Dept. of Computing,London,UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Imperial College,Dept. of Computing,London,UK","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085417650","display_name":"Alex Bottle","orcid":"https://orcid.org/0000-0001-9978-2011"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Alex Bottle","raw_affiliation_strings":["School of Public Health, Imperial College,London,UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Public Health, Imperial College,London,UK","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045967404","display_name":"Giuliano Casale","orcid":"https://orcid.org/0000-0003-4548-7951"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Giuliano Casale","raw_affiliation_strings":["Imperial College,Dept. of Computing,London,UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Imperial College,Dept. of Computing,London,UK","institution_ids":["https://openalex.org/I47508984"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I47508984"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.37815126,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"952","last_page":"963"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.9872999787330627,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.9872999787330627,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9837999939918518,"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":0.9556999802589417,"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/bayesian-probability","display_name":"Bayesian probability","score":0.6560277938842773},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.6030493378639221},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.5926005244255066},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5627991557121277},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34857216477394104},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.07959854602813721},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.06538870930671692}],"concepts":[{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6560277938842773},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.6030493378639221},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.5926005244255066},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5627991557121277},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34857216477394104},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.07959854602813721},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.06538870930671692},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/wsc63780.2024.10838870","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc63780.2024.10838870","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 Winter Simulation Conference (WSC)","raw_type":"proceedings-article"},{"id":"pmh:oai:spiral.imperial.ac.uk:10044/1/115546","is_oa":true,"landing_page_url":"http://hdl.handle.net/10044/1/115546","pdf_url":null,"source":{"id":"https://openalex.org/S4306401396","display_name":"Spiral (Imperial College London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I47508984","host_organization_name":"Imperial College London","host_organization_lineage":["https://openalex.org/I47508984"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Winter Simulation Conference 2024","raw_type":"Conference Paper"}],"best_oa_location":{"id":"pmh:oai:spiral.imperial.ac.uk:10044/1/115546","is_oa":true,"landing_page_url":"http://hdl.handle.net/10044/1/115546","pdf_url":null,"source":{"id":"https://openalex.org/S4306401396","display_name":"Spiral (Imperial College London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I47508984","host_organization_name":"Imperial College London","host_organization_lineage":["https://openalex.org/I47508984"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Winter Simulation Conference 2024","raw_type":"Conference Paper"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1572352401","https://openalex.org/W2027943354","https://openalex.org/W2100132188","https://openalex.org/W2119047901","https://openalex.org/W2121409501","https://openalex.org/W2142406320","https://openalex.org/W2144543704","https://openalex.org/W2150775708","https://openalex.org/W2159024459","https://openalex.org/W2315311660","https://openalex.org/W2735852811","https://openalex.org/W2889282079","https://openalex.org/W2904358320","https://openalex.org/W2946215047","https://openalex.org/W2970634970","https://openalex.org/W3034414418","https://openalex.org/W3044311607","https://openalex.org/W3116008906","https://openalex.org/W3153562187","https://openalex.org/W4224022798","https://openalex.org/W4289520498"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Data":[0],"protection":[1],"rules":[2],"often":[3],"impose":[4],"anonymization":[5],"requirements":[6],"on":[7],"datasets":[8],"by":[9,54],"means":[10],"of":[11,18,29,40,100,109,124],"aggregations":[12],"that":[13,25,58,76],"hinder":[14],"the":[15,41,49,61,87,101,106,110,131],"exact":[16],"simulation":[17,50],"individual":[19],"subjects.":[20,42],"For":[21],"example,":[22,53],"clinical":[23],"pathways":[24,57,90,133],"disclose":[26],"medical":[27],"conditions":[28],"patients":[30],"may":[31],"typically":[32],"need":[33],"to":[34,37,84,126,130],"be":[35],"aggregated":[36],"preserve":[38],"anonymity":[39],"However,":[43],"aggregation":[44],"unavoidably":[45],"results":[46],"in":[47,120],"biasing":[48],"process,":[51],"for":[52],"introducing":[55],"spurious":[56],"can":[59],"skew":[60],"simulated":[62],"trajectories.":[63],"In":[64,113],"this":[65,69],"paper,":[66],"we":[67,115],"study":[68],"problem":[70],"and":[71],"develop":[72],"approximate":[73],"decomposition":[74],"methods":[75],"mitigate":[77],"its":[78],"impact.":[79],"Our":[80],"method":[81],"is":[82],"shown":[83],"produce":[85],"from":[86],"raw":[88],"aggregates":[89],"with":[91,128,135],"higher":[92],"fidelity":[93],"than":[94],"sampling":[95],"a":[96,117],"Markov":[97,137],"chain":[98,138],"model":[99],"aggregate":[102,136],"data,":[103],"even":[104],"preserving":[105],"same":[107],"length":[108],"original":[111],"pathways.":[112],"particular,":[114],"observe":[116],"relative":[118],"increase":[119],"average":[121],"cosine":[122],"similarity":[123],"up":[125],"52%":[127],"respect":[129],"true":[132],"compared":[134],"sampling.":[139]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}