{"id":"https://openalex.org/W4324311136","doi":"https://doi.org/10.1371/journal.pdig.0000117","title":"Data heterogeneity in federated learning with Electronic Health Records: Case studies of risk prediction for acute kidney injury and sepsis diseases in critical care","display_name":"Data heterogeneity in federated learning with Electronic Health Records: Case studies of risk prediction for acute kidney injury and sepsis diseases in critical care","publication_year":2023,"publication_date":"2023-03-15","ids":{"openalex":"https://openalex.org/W4324311136","doi":"https://doi.org/10.1371/journal.pdig.0000117","pmid":"https://pubmed.ncbi.nlm.nih.gov/36920974","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/10016691"},"language":"en","primary_location":{"id":"doi:10.1371/journal.pdig.0000117","doi":"https://doi.org/10.1371/journal.pdig.0000117","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pdig.0000117","pdf_url":"https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000117&type=printable","source":{"id":"https://openalex.org/S4210221150","display_name":"PLOS Digital Health","issn_l":"2767-3170","issn":["2767-3170"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-c","doyens","jufo-1","ki-jl-1","norway-1"],"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS Digital Health","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000117&type=printable","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068424949","display_name":"Suraj Rajendran","orcid":"https://orcid.org/0000-0002-8149-0157","observed_orcids":["https://orcid.org/0000-0002-8149-0157"]},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Suraj Rajendran","raw_affiliation_strings":["Tri-Institutional Computational Biology & Medicine Program, Cornell University, NY, USA","Tri-Institutional Computational Biology & Medicine Program, Cornell University, New York, New York, United States of America"],"raw_orcid":"https://orcid.org/0000-0002-8149-0157","affiliations":[{"raw_affiliation_string":"Tri-Institutional Computational Biology & Medicine Program, Cornell University, NY, USA","institution_ids":["https://openalex.org/I205783295"]},{"raw_affiliation_string":"Tri-Institutional Computational Biology & Medicine Program, Cornell University, New York, New York, United States of America","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058023532","display_name":"Zhenxing Xu","orcid":"https://orcid.org/0000-0001-9515-523X","observed_orcids":["https://orcid.org/0000-0001-9515-523X"]},"institutions":[{"id":"https://openalex.org/I4387153466","display_name":"Weill Cornell Medicine","ror":"https://ror.org/02r109517","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295","https://openalex.org/I4387153466"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhenxing Xu","raw_affiliation_strings":["Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA","Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, United States of America"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA","institution_ids":["https://openalex.org/I4387153466"]},{"raw_affiliation_string":"Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, United States of America","institution_ids":["https://openalex.org/I4387153466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040648859","display_name":"Weishen Pan","orcid":"https://orcid.org/0009-0006-0431-5642","observed_orcids":["https://orcid.org/0009-0006-0431-5642"]},"institutions":[{"id":"https://openalex.org/I4387153466","display_name":"Weill Cornell Medicine","ror":"https://ror.org/02r109517","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295","https://openalex.org/I4387153466"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weishen Pan","raw_affiliation_strings":["Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA","Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, United States of America"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA","institution_ids":["https://openalex.org/I4387153466"]},{"raw_affiliation_string":"Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, United States of America","institution_ids":["https://openalex.org/I4387153466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065041207","display_name":"Arnab K. Ghosh","orcid":"https://orcid.org/0000-0002-5887-3301","observed_orcids":["https://orcid.org/0000-0002-5887-3301"]},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]},{"id":"https://openalex.org/I4387153466","display_name":"Weill Cornell Medicine","ror":"https://ror.org/02r109517","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295","https://openalex.org/I4387153466"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arnab Ghosh","raw_affiliation_strings":["Departments of Medicine, Weill Cornell Medical College, Cornell University, New York, New York","Departments of Medicine, Weill Cornell Medical College, Cornell University, New York, New York, United States of America"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Departments of Medicine, Weill Cornell Medical College, Cornell University, New York, New York","institution_ids":["https://openalex.org/I205783295","https://openalex.org/I4387153466"]},{"raw_affiliation_string":"Departments of Medicine, Weill Cornell Medical College, Cornell University, New York, New York, United States of America","institution_ids":["https://openalex.org/I205783295","https://openalex.org/I4387153466"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100455768","display_name":"Fei Wang","orcid":"https://orcid.org/0000-0001-9459-9461","observed_orcids":["https://orcid.org/0000-0001-9459-9461","https://orcid.org/0000-0002-8244-9551","https://orcid.org/0009-0008-9053-209X"]},"institutions":[{"id":"https://openalex.org/I4387153466","display_name":"Weill Cornell Medicine","ror":"https://ror.org/02r109517","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295","https://openalex.org/I4387153466"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Fei Wang","raw_affiliation_strings":["Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA","Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, United States of America"],"raw_orcid":"https://orcid.org/0000-0001-9459-9461","affiliations":[{"raw_affiliation_string":"Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA","institution_ids":["https://openalex.org/I4387153466"]},{"raw_affiliation_string":"Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, United States of America","institution_ids":["https://openalex.org/I4387153466"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100455768"],"corresponding_institution_ids":["https://openalex.org/I4387153466"],"apc_list":{"value":2575,"currency":"USD","value_usd":2575},"apc_paid":{"value":2575,"currency":"USD","value_usd":2575},"fwci":3.6314,"has_fulltext":true,"cited_by_count":52,"citation_normalized_percentile":{"value":0.94778631,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":100},"biblio":{"volume":"2","issue":"3","first_page":"e0000117","last_page":"e0000117"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9297000169754028,"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":0.9297000169754028,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.06449999660253525,"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/T10218","display_name":"Sepsis Diagnosis and Treatment","score":0.0010000000474974513,"subfield":{"id":"https://openalex.org/subfields/2706","display_name":"Critical Care and Intensive Care 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/federated-learning","display_name":"federated learning","score":0.9670000076293945},{"id":"https://openalex.org/keywords/data-heterogeneity","display_name":"data heterogeneity","score":0.9950000047683716},{"id":"https://openalex.org/keywords/electronic-health-records","display_name":"electronic health records","score":0.9900000095367432},{"id":"https://openalex.org/keywords/acute-kidney-injury","display_name":"acute kidney injury","score":0.824999988079071},{"id":"https://openalex.org/keywords/sepsis","display_name":"sepsis","score":0.8320000171661377},{"id":"https://openalex.org/keywords/risk-prediction","display_name":"risk prediction","score":0.753000020980835},{"id":"https://openalex.org/keywords/intensive-care-unit","display_name":"intensive care unit","score":0.9240000247955322},{"id":"https://openalex.org/keywords/privacy-protection","display_name":"privacy preservation","score":0.5740000009536743},{"id":"https://openalex.org/keywords/critical-care","display_name":"intensive care","score":0.5640000104904175},{"id":"https://openalex.org/keywords/medication-use","display_name":"medication use","score":0.5580000281333923},{"id":"https://openalex.org/keywords/population-distribution","display_name":"population distribution","score":0.6439999938011169},{"id":"https://openalex.org/keywords/site-information","display_name":"site information","score":0.38199999928474426},{"id":"https://openalex.org/keywords/model-generalization","display_name":"model generalization","score":0.5630000233650208}],"concepts":[{"id":"https://openalex.org/C3019952477","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Health records","level":3,"score":0.5413835048675537},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.5374971032142639},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.47631654143333435},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.47065606713294983},{"id":"https://openalex.org/C2780472472","wikidata":"https://www.wikidata.org/wiki/Q424337","display_name":"Acute kidney injury","level":2,"score":0.46805045008659363},{"id":"https://openalex.org/C2781385661","wikidata":"https://www.wikidata.org/wiki/Q4677918","display_name":"Acute care","level":3,"score":0.44431164860725403},{"id":"https://openalex.org/C3020144179","wikidata":"https://www.wikidata.org/wiki/Q10871684","display_name":"Electronic health record","level":3,"score":0.44139811396598816},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.395752489566803},{"id":"https://openalex.org/C177713679","wikidata":"https://www.wikidata.org/wiki/Q679690","display_name":"Intensive care medicine","level":1,"score":0.3925364911556244},{"id":"https://openalex.org/C545542383","wikidata":"https://www.wikidata.org/wiki/Q2751242","display_name":"Medical emergency","level":1,"score":0.38854461908340454},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3647176921367645},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36180442571640015},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.24513670802116394},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0}],"mesh":[],"locations_count":7,"locations":[{"id":"doi:10.1371/journal.pdig.0000117","doi":"https://doi.org/10.1371/journal.pdig.0000117","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pdig.0000117","pdf_url":"https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000117&type=printable","source":{"id":"https://openalex.org/S4210221150","display_name":"PLOS Digital Health","issn_l":"2767-3170","issn":["2767-3170"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-c","doyens","jufo-1","ki-jl-1","norway-1"],"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS Digital Health","raw_type":"journal-article"},{"id":"doi:10.1101/2022.08.30.22279382","doi":"https://doi.org/10.1101/2022.08.30.22279382","is_oa":true,"landing_page_url":"https://doi.org/10.1101/2022.08.30.22279382","pdf_url":"https://www.medrxiv.org/content/medrxiv/early/2022/09/01/2022.08.30.22279382.full.pdf","source":{"id":"https://openalex.org/S3005729997","display_name":"medRxiv","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310315909"],"host_organization_lineage_names":["Cold Spring Harbor Laboratory Press"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"medRxiv","raw_type":"posted-content"},{"id":"pmh:oai:pubmedcentral.nih.gov:10016691","doi":null,"is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10016691","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10016691/pdf/pdig.0000117.pdf","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","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS Digit Health","raw_type":"Text"},{"id":"pmid:36920974","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36920974","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":"PLOS digital health","raw_type":"Journal Article"},{"id":"pmh:oai:RePEc:plo:pdig00:0000117","doi":null,"is_oa":false,"landing_page_url":"https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000117","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},{"id":"pmh:oai:doaj.org/article:4b4bfdd058684d939486168e8a5cf408","doi":null,"is_oa":true,"landing_page_url":"https://doaj.org/article/4b4bfdd058684d939486168e8a5cf408","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,"listed_in":[],"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":"PLOS Digital Health, Vol 2, Iss 3, p e0000117 (2023)","raw_type":"article"},{"id":"pmh:oai:doaj.org/article:76ae171b9dc44ffdb5d09d83f4396f56","doi":null,"is_oa":true,"landing_page_url":"https://doaj.org/article/76ae171b9dc44ffdb5d09d83f4396f56","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,"listed_in":[],"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":"PLOS Digital Health, Vol 2, Iss 3 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1371/journal.pdig.0000117","doi":"https://doi.org/10.1371/journal.pdig.0000117","is_oa":true,"landing_page_url":"https://doi.org/10.1371/journal.pdig.0000117","pdf_url":"https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000117&type=printable","source":{"id":"https://openalex.org/S4210221150","display_name":"PLOS Digital Health","issn_l":"2767-3170","issn":["2767-3170"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-c","doyens","jufo-1","ki-jl-1","norway-1"],"host_organization":"https://openalex.org/P4310315706","host_organization_name":"Public Library of Science","host_organization_lineage":["https://openalex.org/P4310315706"],"host_organization_lineage_names":["Public Library of Science"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PLOS Digital Health","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.8560000061988831,"id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[{"id":"https://openalex.org/G1964106676","display_name":null,"funder_award_id":"RF1AG072449","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3916121238","display_name":"CAREER: Interpretable Deep Modeling of Discrete Time Event Sequences","funder_award_id":"1750326","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5237391046","display_name":"Identification of Mild Cognitive Impairment using Machine Learning from Language and Behavior Markers","funder_award_id":"RF1AG072449","funder_id":"https://openalex.org/F4320337337","funder_display_name":"National Institute on Aging"},{"id":"https://openalex.org/G5594834694","display_name":null,"funder_award_id":"T32-GM083937","funder_id":"https://openalex.org/F4320337354","funder_display_name":"National Institute of General Medical Sciences"},{"id":"https://openalex.org/G6061274122","display_name":"Collaborative Research: III: Medium: A consolidated framework of computational privacy and machine learning","funder_award_id":"2212175","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G9000147831","display_name":null,"funder_award_id":"1T32-GM083937","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G9000915007","display_name":"Computational Drug Repurposing for AD/ADRD with Integrative Analysis of Real World Data and Biomedical Knowledge","funder_award_id":"R01AG076234","funder_id":"https://openalex.org/F4320337337","funder_display_name":"National Institute on Aging"},{"id":"https://openalex.org/G982506158","display_name":"Tri-Institutional Training Program in Computational Biology and Medicine","funder_award_id":"5t32gm083937-09","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309327","display_name":"Google","ror":"https://ror.org/00njsd438"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337337","display_name":"National Institute on Aging","ror":"https://ror.org/049v75w11"},{"id":"https://openalex.org/F4320337354","display_name":"National Institute of General Medical Sciences","ror":"https://ror.org/04q48ey07"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4324311136.pdf","grobid_xml":"https://content.openalex.org/works/W4324311136.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W1973895663","https://openalex.org/W1981976602","https://openalex.org/W1996531679","https://openalex.org/W2014575120","https://openalex.org/W2099287015","https://openalex.org/W2130502422","https://openalex.org/W2162262523","https://openalex.org/W2282181907","https://openalex.org/W2492513888","https://openalex.org/W2744556021","https://openalex.org/W2755626276","https://openalex.org/W2759850233","https://openalex.org/W2766585573","https://openalex.org/W2787798272","https://openalex.org/W2794885170","https://openalex.org/W2995282027","https://openalex.org/W3013299166","https://openalex.org/W3021654819","https://openalex.org/W3024264813","https://openalex.org/W3045674654","https://openalex.org/W3046153892","https://openalex.org/W3048873596","https://openalex.org/W3098674050","https://openalex.org/W3100779497","https://openalex.org/W3118996476","https://openalex.org/W3133814152","https://openalex.org/W3174786846","https://openalex.org/W3175954176","https://openalex.org/W4200334578","https://openalex.org/W4200450447","https://openalex.org/W4206676242","https://openalex.org/W4206878041","https://openalex.org/W4225576041","https://openalex.org/W4225603140","https://openalex.org/W4240212793","https://openalex.org/W4240926358","https://openalex.org/W4280499562","https://openalex.org/W4281616841","https://openalex.org/W4281920142","https://openalex.org/W4289519525"],"related_works":["https://openalex.org/W4390227131","https://openalex.org/W4387454120","https://openalex.org/W3211725878","https://openalex.org/W4285495008","https://openalex.org/W7214643556","https://openalex.org/W3184883837","https://openalex.org/W4413428992","https://openalex.org/W4387850546","https://openalex.org/W4403664165","https://openalex.org/W7127038294"],"abstract_inverted_index":{"Abstract":[0],"With":[1],"the":[2,25,59,136,171,177,221,254,292,295,349,360,372,383,394,402,440,446,493,534,551],"wider":[3],"availability":[4,316],"of":[5,27,54,61,105,139,173,291,301,317,321,332,352,404,442,533,542,553],"healthcare":[6,48,55,322,353,385],"data":[7,56,74,91,106,174,185,207,216,311,323,361,386,391,405,443,455,479,488,559],"such":[8,103,324,549],"as":[9,166,325,435,550],"Electronic":[10,326],"Health":[11,327],"Records":[12,328],"(EHR),":[13],"more":[14,16],"and":[15,124,159,199,240,258,284,306,330,366,427,471,526],"data-driven":[17],"based":[18,142,195,467],"approaches":[19],"have":[20,229,501],"been":[21],"proposed":[22],"to":[23,119,131,169,231,245,252,310,339,348,358,409,438,503,558],"improve":[24],"quality":[26],"care":[28,133,151,432],"delivery.":[29],"Predictive":[30],"modeling,":[31],"which":[32,249],"aims":[33],"at":[34,555],"building":[35,111,410],"computational":[36],"models":[37,64,115,194,267,342,415,464,509],"for":[38,180,343,449],"predicting":[39],"clinical":[40,77],"risk,":[41],"is":[42,145,356],"a":[43,246,270,318,368,512],"popular":[44],"research":[45],"topic":[46,148],"in":[47,86,110,116,149,164,176,381,393,416,430,445],"analytics.":[49],"However,":[50,90,390],"concerns":[51],"about":[52],"privacy":[53],"may":[57,98,397],"hinder":[58],"development":[60],"effective":[62],"predictive":[63,193,341,463],"that":[65],"are":[66],"generalizable":[67],"because":[68],"this":[69,88,154,419],"often":[70],"requires":[71],"rich":[72],"diverse":[73],"from":[75,93,208,217,363,480,489],"multiple":[76,187,457,462],"institutions.":[78],"Recently,":[79],"federated":[80,255,376],"learning":[81,377],"(FL)":[82,378],"has":[83],"demonstrated":[84],"promise":[85,380],"addressing":[87,382],"concern.":[89],"heterogeneity":[92,104,175,293,392,403,444,535],"different":[94,299,364,540],"local":[95,203,225,274,475,497,516],"participating":[96],"sites":[97,283,525],"affect":[99],"prediction":[100,114,138,163,182,399,414,429,452],"performance.":[101,400],"Exploring":[102,401],"sources":[107,290,387,406,532],"would":[108,407],"aid":[109],"accurate":[112,411],"risk":[113,162,181,345,413,451],"FL.":[117,417],"Due":[118,347],"acute":[120,423],"kidney":[121,424],"injury":[122,425],"(AKI)":[123,426],"sepsis\u2019":[125],"high":[126],"prevalence":[127],"among":[128],"patients":[129],"admitted":[130],"intensive":[132,431],"units":[134,433],"(ICU),":[135],"early":[137],"these":[140],"conditions":[141],"on":[143,196,371,468],"AI":[144],"an":[146],"important":[147],"critical":[150],"medicine.":[152],"In":[153,189,220,418,460,492],"study,":[155,420],"we":[156,191,277,287,421,519,529],"take":[157,422],"AKI":[158],"sepsis":[160,428],"onset":[161],"ICU":[165,554],"two":[167,436],"examples":[168,437],"explore":[170,439],"impact":[172],"FL":[178,200,222,271,395,447,472,494,513],"framework":[179,204,214,272,396,448,476,486,514],"using":[183,453],"EHR":[184,296,454,537],"across":[186,282,456,524],"hospitals.":[188],"particular,":[190,461],"built":[192,268,466,510],"local,":[197,469],"pooled,":[198,470],"frameworks.":[201,285,473,527],"The":[202,212,298,315,474,484,539],"only":[205,477],"used":[206,251,478],"each":[209,224,263,481,496],"site":[210,226,307,482,498,547],"itself.":[211,483],"pooled":[213,485],"combined":[215,373,487],"all":[218,490],"sites.":[219,459,491],"framework,":[223,495],"did":[227,499],"not":[228,500],"access":[230,502],"other":[232,504],"sites\u2019":[233,505],"data.":[234,297,374,506,538],"A":[235],"model":[236,370],"was":[237,250],"trained":[238],"locally":[239],"its":[241],"parameters":[242],"were":[243,465],"shared":[244,261],"central":[247],"aggregator,":[248],"update":[253],"model\u2019s":[256],"weights":[257],"then":[259],"subsequently,":[260],"with":[262,388],"site.":[264],"We":[265,507],"found":[266,508],"within":[269,294,511,536],"outperformed":[273,515],"counterparts.":[275,517],"Then,":[276,518],"analyzed":[278,520],"variable":[279,521],"importance":[280,522],"discrepancies":[281,523],"Finally,":[286,528],"explored":[288,530],"potential":[289,531],"distributions":[300,541],"demographic":[302,543],"profiles,":[303,544],"medication":[304,545],"use,":[305,546],"information":[308,548],"contributed":[309,557],"heterogeneity.":[312,560],"Author":[313],"Summary":[314],"large":[319],"amount":[320],"(EHR)":[329],"advances":[331],"artificial":[333],"intelligence":[334],"(AI)":[335],"techniques":[336],"provides":[337],"opportunities":[338],"build":[340],"disease":[344,412,450],"prediction.":[346],"sensitive":[350],"nature":[351],"data,":[354],"it":[355],"challenging":[357],"collect":[359],"together":[362],"hospitals":[365],"train":[367],"unified":[369],"Recent":[375],"demonstrates":[379],"fragmented":[384],"privacy-preservation.":[389],"influence":[398],"contribute":[408],"(ICU)":[434],"effects":[441],"hospital":[458],"type":[552],"admission":[556]},"counts_by_year":[{"year":2026,"cited_by_count":18},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":1}],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2023-03-16T00:00:00"}