{"id":"https://openalex.org/W4411078084","doi":null,"title":"Bayesian ensemble learning method for the analysis of multipollutant mixtures data.","display_name":"Bayesian ensemble learning method for the analysis of multipollutant mixtures data.","publication_year":2025,"publication_date":"2025-05-23","ids":{"openalex":"https://openalex.org/W4411078084","pmid":"https://pubmed.ncbi.nlm.nih.gov/40470479","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/12136477"},"language":"en","primary_location":{"id":"pmid:40470479","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40470479","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,"listed_in":[],"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":"ArXiv","raw_type":"Journal Article"},"volume":null,"type":"preprint","indexed_in":["pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12136477","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yu-Chien Ning","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yu-Chien Ning","raw_affiliation_strings":["Department of Epidemiology, Harvard T.H. Chan School of Public Health, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Epidemiology, Harvard T.H. Chan School of Public Health, MA, USA","institution_ids":["https://openalex.org/I136199984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101875380","display_name":"Xin Zhou","orcid":"https://orcid.org/0000-0002-6731-0535","observed_orcids":["https://orcid.org/0000-0002-6731-0535"]},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xin Zhou","raw_affiliation_strings":["Department of Biostatistics, Yale School of Public Health, CT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biostatistics, Yale School of Public Health, CT, USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024488327","display_name":"Francine Laden","orcid":"https://orcid.org/0000-0002-2813-2174","observed_orcids":["https://orcid.org/0000-0002-2813-2174"]},"institutions":[{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Francine Laden","raw_affiliation_strings":["Departments of Environmental Health and Epidemiology, Harvard T.H. Chan School of Public Health, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Departments of Environmental Health and Epidemiology, Harvard T.H. Chan School of Public Health, MA, USA","institution_ids":["https://openalex.org/I136199984"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103146308","display_name":"Molin Wang","orcid":"https://orcid.org/0000-0003-1951-8961","observed_orcids":["https://orcid.org/0000-0003-1951-8961"]},"institutions":[{"id":"https://openalex.org/I1283280774","display_name":"Brigham and Women's Hospital","ror":"https://ror.org/04b6nzv94","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I121934306","https://openalex.org/I1283280774","https://openalex.org/I48633490"]},{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Molin Wang","raw_affiliation_strings":["Department of Epidemiology, Harvard T.H. Chan School of Public Health, MA, USA","Departments of Biostatistics, Harvard T.H. Chan School of Public Health, MA, USA","Channing Division of Network Medicine, Brigham at Women's Hospital and Harvard Medical School, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Epidemiology, Harvard T.H. Chan School of Public Health, MA, USA","institution_ids":["https://openalex.org/I136199984"]},{"raw_affiliation_string":"Departments of Biostatistics, Harvard T.H. Chan School of Public Health, MA, USA","institution_ids":["https://openalex.org/I136199984"]},{"raw_affiliation_string":"Channing Division of Network Medicine, Brigham at Women's Hospital and Harvard Medical School, MA, USA","institution_ids":["https://openalex.org/I1283280774","https://openalex.org/I136199984"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14374","display_name":"Statistical Methods in Epidemiology","score":0.42820000648498535,"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/T14374","display_name":"Statistical Methods in Epidemiology","score":0.42820000648498535,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.08609999716281891,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.06989999860525131,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/long-term-exposure","display_name":"long-term exposure","score":0.6570000052452087},{"id":"https://openalex.org/keywords/health-effects-of-pollution","display_name":"health effects of pollution","score":0.5889999866485596},{"id":"https://openalex.org/keywords/nonlinear-dose-response-relationship","display_name":"nonlinear dose-response relationship","score":0.3610000014305115},{"id":"https://openalex.org/keywords/interaction-effects","display_name":"interaction effects","score":0.8560000061988831},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"uncertainty quantification","score":0.8740000128746033},{"id":"https://openalex.org/keywords/nurses-health-study","display_name":"Nurses' Health Study","score":0.9570000171661377}],"concepts":[{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6459593772888184},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.49223652482032776},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4919902980327606},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4070967435836792},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38792309165000916},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3264209032058716},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16484129428863525}],"mesh":[],"locations_count":3,"locations":[{"id":"pmid:40470479","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40470479","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,"listed_in":[],"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":"ArXiv","raw_type":"Journal Article"},{"id":"pmh:oai:europepmc.org:10949884","doi":null,"is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12136477","pdf_url":null,"source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"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":null,"raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:12136477","doi":null,"is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12136477","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ArXiv","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:europepmc.org:10949884","doi":null,"is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12136477","pdf_url":null,"source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"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":null,"raw_type":"Text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.46810001134872437,"display_name":"Good health and well-being"}],"study_designs":[],"awards":[{"id":"https://openalex.org/G4189837557","display_name":"Statistical Methods to Account for Exposure Uncertainty in Environmental Epidemiology","funder_award_id":"R01ES026246","funder_id":"https://openalex.org/F4320337361","funder_display_name":"National Institute of Environmental Health Sciences"}],"funders":[{"id":"https://openalex.org/F4320337361","display_name":"National Institute of Environmental Health Sciences","ror":"https://ror.org/00j4k1h63"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":1,"referenced_works":["https://openalex.org/W2026849018"],"related_works":["https://openalex.org/W6947956266","https://openalex.org/W2912220915","https://openalex.org/W3136994940","https://openalex.org/W4224950666","https://openalex.org/W2888209655","https://openalex.org/W2727334288","https://openalex.org/W1224235025","https://openalex.org/W3195691732","https://openalex.org/W7124158062","https://openalex.org/W2922250865"],"abstract_inverted_index":{"from":[0,103],"Bayesian":[1],"ensemble":[2],"learning":[3],"to":[4,95],"estimate":[5],"the":[6,66,85,93,104],"relationship":[7],"between":[8],"multipollutant":[9,60,98],"mixtures":[10],"and":[11,33,50,75,81],"health":[12],"on":[13],"chronic":[14],"exposures":[15],"in":[16,43,73],"epidemiology":[17],"research.":[18],"This":[19],"approach":[20],"offers":[21],"several":[22],"key":[23],"advantages":[24],"over":[25],"existing":[26],"methods:":[27],"(1)":[28],"it":[29,40,52],"is":[30,41],"computationally":[31],"efficient":[32],"well-suited":[34],"for":[35,78],"analyzing":[36],"large":[37],"datasets;":[38],"(2)":[39],"flexible":[42],"estimating":[44,74],"various":[45],"correlated":[46,59],"nonlinear":[47],"functions":[48],"simultaneously;":[49],"(3)":[51],"accurately":[53],"identifies":[54],"active":[55],"variables":[56],"within":[57],"highly":[58],"mixtures.":[61],"Through":[62],"simulations,":[63],"we":[64,91],"demonstrate":[65],"method's":[67],"superiority":[68],"by":[69],"comparing":[70],"its":[71],"accuracy":[72],"quantifying":[76],"uncertainties":[77],"both":[79],"main":[80],"interaction":[82],"effects":[83],"with":[84,100],"commonly":[86],"used":[87],"method,":[88],"BKMR.":[89],"Last,":[90],"apply":[92],"method":[94],"analyze":[96],"a":[97],"dataset":[99],"10,110":[101],"participates":[102],"Nurses'":[105],"Health":[106],"Study.":[107]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2025-10-10T00:00:00"}