{"id":"https://openalex.org/W4384518177","doi":"https://doi.org/10.1109/cbms58004.2023.00304","title":"Early prediction of the risk of ICU mortality with Deep Federated Learning","display_name":"Early prediction of the risk of ICU mortality with Deep Federated Learning","publication_year":2023,"publication_date":"2023-06-01","ids":{"openalex":"https://openalex.org/W4384518177","doi":"https://doi.org/10.1109/cbms58004.2023.00304"},"language":"en","primary_location":{"id":"doi:10.1109/cbms58004.2023.00304","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cbms58004.2023.00304","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"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/A5000389167","display_name":"Korbinian Randl","orcid":"https://orcid.org/0000-0002-7938-2747"},"institutions":[{"id":"https://openalex.org/I161593684","display_name":"Stockholm University","ror":"https://ror.org/05f0yaq80","country_code":"SE","type":"education","lineage":["https://openalex.org/I161593684"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Korbinian Randl","raw_affiliation_strings":["Stockholm University,Dept. of Computer &#x0026; Systems Sciences,Stockholm,Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stockholm University,Dept. of Computer &#x0026; Systems Sciences,Stockholm,Sweden","institution_ids":["https://openalex.org/I161593684"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050242171","display_name":"N\u00faria Llad\u00f3s Armengol","orcid":null},"institutions":[{"id":"https://openalex.org/I161593684","display_name":"Stockholm University","ror":"https://ror.org/05f0yaq80","country_code":"SE","type":"education","lineage":["https://openalex.org/I161593684"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"N\u00faria Llad\u00f3s Armengol","raw_affiliation_strings":["Stockholm University,Dept. of Computer &#x0026; Systems Sciences,Stockholm,Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stockholm University,Dept. of Computer &#x0026; Systems Sciences,Stockholm,Sweden","institution_ids":["https://openalex.org/I161593684"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086562160","display_name":"Lena Mondrejevski","orcid":"https://orcid.org/0000-0002-1790-3842"},"institutions":[{"id":"https://openalex.org/I161593684","display_name":"Stockholm University","ror":"https://ror.org/05f0yaq80","country_code":"SE","type":"education","lineage":["https://openalex.org/I161593684"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Lena Mondrejevski","raw_affiliation_strings":["Stockholm University,Dept. of Computer &#x0026; Systems Sciences,Stockholm,Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stockholm University,Dept. of Computer &#x0026; Systems Sciences,Stockholm,Sweden","institution_ids":["https://openalex.org/I161593684"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060236527","display_name":"Ioanna Miliou","orcid":"https://orcid.org/0000-0002-1357-1967"},"institutions":[{"id":"https://openalex.org/I161593684","display_name":"Stockholm University","ror":"https://ror.org/05f0yaq80","country_code":"SE","type":"education","lineage":["https://openalex.org/I161593684"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Ioanna Miliou","raw_affiliation_strings":["Stockholm University,Dept. of Computer &#x0026; Systems Sciences,Stockholm,Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stockholm University,Dept. of Computer &#x0026; Systems Sciences,Stockholm,Sweden","institution_ids":["https://openalex.org/I161593684"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.1211,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.89699731,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"706","last_page":"711"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9991999864578247,"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":0.9991999864578247,"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":0.9921000003814697,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.9667999744415283,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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/machine-learning","display_name":"Machine learning","score":0.7591783404350281},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7363020777702332},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7175762057304382},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6814572811126709},{"id":"https://openalex.org/keywords/intensive-care-unit","display_name":"Intensive care unit","score":0.6030043959617615},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.527777910232544},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5078840851783752},{"id":"https://openalex.org/keywords/intensive-care","display_name":"Intensive care","score":0.43397000432014465},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.19678199291229248},{"id":"https://openalex.org/keywords/intensive-care-medicine","display_name":"Intensive care medicine","score":0.1767888367176056},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08878439664840698},{"id":"https://openalex.org/keywords/operations-management","display_name":"Operations management","score":0.0856524407863617}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7591783404350281},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7363020777702332},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7175762057304382},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6814572811126709},{"id":"https://openalex.org/C2776376669","wikidata":"https://www.wikidata.org/wiki/Q5094647","display_name":"Intensive care unit","level":2,"score":0.6030043959617615},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.527777910232544},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5078840851783752},{"id":"https://openalex.org/C2987404301","wikidata":"https://www.wikidata.org/wiki/Q679690","display_name":"Intensive care","level":2,"score":0.43397000432014465},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.19678199291229248},{"id":"https://openalex.org/C177713679","wikidata":"https://www.wikidata.org/wiki/Q679690","display_name":"Intensive care medicine","level":1,"score":0.1767888367176056},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08878439664840698},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0856524407863617},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cbms58004.2023.00304","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cbms58004.2023.00304","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8700000047683716,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W2053637704","https://openalex.org/W2064675550","https://openalex.org/W2120156715","https://openalex.org/W2129506238","https://openalex.org/W2162800060","https://openalex.org/W2396881363","https://openalex.org/W2787810682","https://openalex.org/W2941095363","https://openalex.org/W2964199361","https://openalex.org/W2964407252","https://openalex.org/W3199147158","https://openalex.org/W4293812377","https://openalex.org/W4318619660","https://openalex.org/W6631190155","https://openalex.org/W6748617090","https://openalex.org/W6761562718"],"related_works":["https://openalex.org/W4380075502","https://openalex.org/W4223943233","https://openalex.org/W4312200629","https://openalex.org/W4360585206","https://openalex.org/W4364306694","https://openalex.org/W4380086463","https://openalex.org/W4225161397","https://openalex.org/W3014300295","https://openalex.org/W3164822677","https://openalex.org/W4250304930"],"abstract_inverted_index":{"Intensive":[0,105,166],"Care":[1,106,167],"Units":[2],"usually":[3],"carry":[4],"patients":[5],"with":[6,32],"a":[7,33,73,161],"serious":[8],"risk":[9,26,103],"of":[10,18,67,96,104,118,127,210],"mortality.":[11],"Recent":[12],"research":[13],"has":[14],"shown":[15],"the":[16,23,64,94,102,115,142,156,176,182,197,208,214],"ability":[17,95],"Machine":[19,59,74,123],"Learning":[20,60,71,75,99,124,137],"to":[21,44,56,85,100,188],"indicate":[22],"patients'":[24],"mortality":[25,108,169],"and":[27,47,121,130,148,151,206],"point":[28],"physicians":[29],"toward":[30],"individuals":[31],"heightened":[34],"need":[35],"for":[36,78,164,213],"care.":[37],"Nevertheless,":[38],"healthcare":[39],"data":[40,66,79],"is":[41,72,152,179,186],"often":[42],"subject":[43],"privacy":[45,80],"regulations":[46],"can":[48,82,204],"therefore":[49],"not":[50],"be":[51,83],"easily":[52],"shared":[53],"in":[54,125],"order":[55],"build":[57],"Centralized":[58],"models":[61],"that":[62,81,135,175,195],"use":[63],"combined":[65],"multiple":[68],"hospitals.":[69],"Federated":[70,98,136],"framework":[76],"designed":[77],"used":[84],"circumvent":[86],"this":[87,90],"problem.":[88],"In":[89,171],"study,":[91],"we":[92,173,193],"evaluate":[93],"deep":[97],"predict":[101],"Unit":[107,168],"at":[109,216],"an":[110,200],"early":[111,165,201],"stage.":[112],"We":[113],"compare":[114],"predictive":[116],"performance":[117,178,209],"Federated,":[119],"Centralized,":[120],"Local":[122],"terms":[126],"AUPRC,":[128],"F1-score,":[129],"AUROC.":[131],"Our":[132],"results":[133],"show":[134,194],"performs":[138],"equally":[139],"well":[140],"as":[141,199],"centralized":[143],"approach":[144,212],"(for":[145],"2,":[146],"4,":[147],"8":[149],"clients)":[150],"substantially":[153],"better":[154],"than":[155],"local":[157],"approach,":[158],"thus":[159],"providing":[160],"viable":[162],"solution":[163],"prediction.":[170],"addition,":[172],"demonstrate":[174],"prediction":[177],"higher":[180],"when":[181],"patient":[183],"history":[184],"window":[185],"closer":[187],"discharge":[189],"or":[190],"death.":[191],"Finally,":[192],"using":[196],"F1-score":[198],"stopping":[202],"metric":[203],"stabilize":[205],"increase":[207],"our":[211],"task":[215],"hand.":[217]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
