{"id":"https://openalex.org/W4405254567","doi":"https://doi.org/10.48550/arxiv.2412.06815","title":"Federated Block-Term Tensor Regression for decentralised data analysis in healthcare","display_name":"Federated Block-Term Tensor Regression for decentralised data analysis in healthcare","publication_year":2024,"publication_date":"2024-12-02","ids":{"openalex":"https://openalex.org/W4405254567","doi":"https://doi.org/10.48550/arxiv.2412.06815"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2412.06815","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2412.06815","pdf_url":"https://arxiv.org/pdf/2412.06815","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2412.06815","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086373617","display_name":"Axel Faes","orcid":"https://orcid.org/0000-0002-1637-255X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Faes, Axel","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064168270","display_name":"Ashkan Pirmani","orcid":"https://orcid.org/0000-0003-4549-1002"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pirmani, Ashkan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041984163","display_name":"Yves Moreau","orcid":"https://orcid.org/0000-0002-4647-6560"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Moreau, Yves","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5061462867","display_name":"Liesbet M. Peeters","orcid":"https://orcid.org/0000-0002-6066-3899"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peeters, Liesbet M.","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5086373617"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T14393","display_name":"Health, Environment, Cognitive Aging","score":0.6078000068664551,"subfield":{"id":"https://openalex.org/subfields/2307","display_name":"Health, Toxicology and Mutagenesis"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T14393","display_name":"Health, Environment, Cognitive Aging","score":0.6078000068664551,"subfield":{"id":"https://openalex.org/subfields/2307","display_name":"Health, Toxicology and Mutagenesis"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.6039999723434448,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"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/term","display_name":"Term (time)","score":0.7354447841644287},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.6558521389961243},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5783886313438416},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5052810311317444},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.48858413100242615},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4179673194885254},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33177801966667175},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2668113112449646},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2280237078666687},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.13298171758651733},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.06413543224334717}],"concepts":[{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.7354447841644287},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.6558521389961243},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5783886313438416},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5052810311317444},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.48858413100242615},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4179673194885254},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33177801966667175},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2668113112449646},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2280237078666687},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.13298171758651733},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.06413543224334717},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"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":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2412.06815","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2412.06815","pdf_url":"https://arxiv.org/pdf/2412.06815","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2412.06815","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2412.06815","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2412.06815","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2412.06815","pdf_url":"https://arxiv.org/pdf/2412.06815","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4405254567.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W31220157","https://openalex.org/W2312753042","https://openalex.org/W4289356671","https://openalex.org/W2389155397","https://openalex.org/W2165884543","https://openalex.org/W3186837933","https://openalex.org/W2368989808","https://openalex.org/W1969346022","https://openalex.org/W2034959125","https://openalex.org/W2355687852"],"abstract_inverted_index":{"Block-Term":[0,56],"Tensor":[1,57],"Regression":[2,58],"(BTTR)":[3],"has":[4],"proven":[5],"to":[6,76,99,160,175,213],"be":[7],"a":[8,91,153],"powerful":[9],"tool":[10],"for":[11,24,65,147,164,221],"modeling":[12],"complex,":[13],"high-dimensional":[14],"data":[15,72,83],"by":[16],"leveraging":[17],"multilinear":[18],"relationships,":[19],"making":[20],"it":[21],"particularly":[22],"well-suited":[23],"applications":[25],"in":[26,95,107,140],"healthcare":[27],"and":[28,44,85,118,189,205,217],"neuroscience.":[29],"However,":[30],"traditional":[31],"implementations":[32],"of":[33,62,155,201,208],"BTTR":[34,63,191],"rely":[35],"on":[36],"centralized":[37,190,223],"datasets,":[38,182],"which":[39],"pose":[40],"significant":[41],"privacy":[42,84],"risks":[43],"hinder":[45],"collaboration":[46],"across":[47],"institutions.":[48],"To":[49],"address":[50],"these":[51],"challenges,":[52],"we":[53],"introduce":[54],"Federated":[55],"(FBTTR),":[59],"an":[60,197,206],"extension":[61],"designed":[64],"federated":[66,100,186],"learning":[67,101,187],"scenarios.":[68],"FBTTR":[69,89,133,172],"enables":[70],"decentralized":[71],"analysis,":[73],"allowing":[74],"institutions":[75],"collaboratively":[77],"build":[78],"predictive":[79],"models":[80],"while":[81],"preserving":[82],"complying":[86],"with":[87],"regulations.":[88],"represents":[90],"major":[92],"step":[93],"forward":[94],"applying":[96],"tensor":[97],"regression":[98],"environments.":[102],"Its":[103],"performance":[104,154],"is":[105,173],"evaluated":[106],"two":[108],"case":[109,125,170],"studies:":[110],"finger":[111,142],"movement":[112],"decoding":[113,141],"from":[114],"Electrocorticography":[115],"(ECoG)":[116],"signals":[117],"heart":[119,177],"disease":[120,178],"prediction.":[121],"In":[122,167,193],"the":[123,128,145,150,168,194,222],"first":[124],"study,":[126,171],"using":[127,179],"BCI":[129],"Competition":[130],"IV":[131],"dataset,":[132,146],"outperforms":[134],"non-multilinear":[135],"models,":[136],"demonstrating":[137],"superior":[138],"accuracy":[139,207],"movements.":[143],"For":[144],"subject":[148],"3,":[149],"thumb":[151],"obtained":[152,200],"0.76":[156],"$\\pm$":[157,162,203,210,215,219],".05":[158],"compared":[159,212],"0.71":[161],"0.05":[163],"centralised":[165],"BTTR.":[166],"second":[169],"applied":[174],"predict":[176],"real-world":[180],"clinical":[181],"outperforming":[183],"both":[184],"standard":[185],"approaches":[188],"models.":[192],"Fed-Heart-Disease":[195],"Dataset,":[196],"AUC-ROC":[198],"was":[199],"0.872":[202],"0.02":[204,211],"0.772":[209],"0.812":[214],"0.003":[216],"0.753":[218],"0.007":[220],"model.":[224]},"counts_by_year":[],"updated_date":"2026-03-11T14:59:36.786465","created_date":"2024-12-12T00:00:00"}
