{"id":"https://openalex.org/W4385007369","doi":"https://doi.org/10.48550/arxiv.2307.10923","title":"Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series","display_name":"Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series","publication_year":2023,"publication_date":"2023-07-20","ids":{"openalex":"https://openalex.org/W4385007369","doi":"https://doi.org/10.48550/arxiv.2307.10923"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2307.10923","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2307.10923","pdf_url":"https://arxiv.org/pdf/2307.10923","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":null},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2307.10923","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067494217","display_name":"Aniruddh Raghu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Raghu, Aniruddh","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037842761","display_name":"Payal Chandak","orcid":"https://orcid.org/0000-0003-1097-803X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chandak, Payal","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034393434","display_name":"Ridwan Alam","orcid":"https://orcid.org/0000-0002-4332-4051"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alam, Ridwan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007282049","display_name":"John V. Guttag","orcid":"https://orcid.org/0000-0003-0992-0906"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guttag, John","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5024941370","display_name":"Collin M. Stultz","orcid":"https://orcid.org/0000-0002-3415-242X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stultz, Collin M.","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5067494217"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"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/T11021","display_name":"ECG Monitoring and Analysis","score":0.9775000214576721,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9775000214576721,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9351000189781189,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7774016857147217},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6972172260284424},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.6176563501358032},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5497699975967407},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4808650612831116},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.450231671333313},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.44324925541877747},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.42413851618766785},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40659549832344055},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.39507606625556946}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7774016857147217},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6972172260284424},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.6176563501358032},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5497699975967407},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4808650612831116},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.450231671333313},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.44324925541877747},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.42413851618766785},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40659549832344055},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39507606625556946},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2307.10923","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2307.10923","pdf_url":"https://arxiv.org/pdf/2307.10923","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":null},{"id":"doi:10.48550/arxiv.2307.10923","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2307.10923","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:2307.10923","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2307.10923","pdf_url":"https://arxiv.org/pdf/2307.10923","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":null},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W1919101720","https://openalex.org/W4390822878","https://openalex.org/W96888382","https://openalex.org/W2041308758","https://openalex.org/W4386126592","https://openalex.org/W4392529072","https://openalex.org/W4394131749","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"Self-supervised":[0],"learning":[1],"(SSL)":[2],"for":[3,35,46],"clinical":[4,36,189],"time":[5,37,48,81,193],"series":[6,38,82,194],"data":[7,17,91,140,204],"has":[8],"received":[9],"significant":[10],"attention":[11],"in":[12,41,97,142,145,175,180,234],"recent":[13],"literature,":[14],"since":[15],"these":[16],"are":[18,39,44],"highly":[19],"rich":[20],"and":[21,60,89,107,132,201,208,220,233],"provide":[22],"important":[23],"information":[24,150],"about":[25],"a":[26,52,109,119],"patient's":[27],"physiological":[28,67],"state.":[29],"However,":[30],"most":[31],"existing":[32,73],"SSL":[33,111,116,120],"methods":[34,74],"limited":[40],"that":[42,83,215],"they":[43],"designed":[45],"unimodal":[47],"series,":[49],"such":[50],"as":[51,174,179],"sequence":[53,131,144],"of":[54,128,136,162,197],"structured":[55,87,203],"features":[56,88],"(e.g.,":[57,69],"lab":[58,206],"values":[59,207],"vitals":[61,209],"signs)":[62],"or":[63,177],"an":[64,70],"individual":[65,138],"high-dimensional":[66,90,139],"signal":[68],"electrocardiogram).":[71],"These":[72],"cannot":[75],"be":[76,172],"readily":[77],"extended":[78],"to":[79,147,158,239],"model":[80],"exhibit":[84],"multimodality,":[85],"with":[86,217],"being":[92],"recorded":[93],"at":[94,125,133,151,166],"each":[95,167],"timestep":[96],"the":[98,126,129,134,137,143,159,192],"sequence.":[99],"In":[100],"this":[101,105],"work,":[102],"we":[103],"address":[104],"gap":[106],"propose":[108],"new":[110],"method":[112,185,219],"--":[113,117,169],"Sequential":[114],"Multi-Dimensional":[115],"where":[118,191],"loss":[121,163,244],"is":[122,156],"applied":[123],"both":[124,152,231],"level":[127,135,168],"entire":[130],"points":[141],"order":[146],"better":[148],"capture":[149],"scales.":[153],"Our":[154,211],"strategy":[155],"agnostic":[157],"specific":[160],"form":[161],"function":[164],"used":[165],"it":[170],"can":[171,237],"contrastive,":[173],"SimCLR,":[176],"non-contrastive,":[178],"VICReg.":[181],"We":[182],"evaluate":[183],"our":[184,218],"on":[186,223,230],"two":[187],"real-world":[188],"datasets,":[190,232],"contains":[195],"sequences":[196],"(1)":[198],"high-frequency":[199],"electrocardiograms":[200],"(2)":[202],"from":[205],"signs.":[210],"experimental":[212],"results":[213],"indicate":[214],"pre-training":[216],"then":[221],"fine-tuning":[222],"downstream":[224],"tasks":[225],"improves":[226],"performance":[227],"over":[228],"baselines":[229],"several":[235],"settings,":[236],"lead":[238],"improvements":[240],"across":[241],"different":[242],"self-supervised":[243],"functions.":[245]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-11T14:59:36.786465","created_date":"2025-10-10T00:00:00"}
