{"id":"https://openalex.org/W4315588764","doi":"https://doi.org/10.48550/arxiv.2301.03150","title":"MOTOR: A Time-To-Event Foundation Model For Structured Medical Records","display_name":"MOTOR: A Time-To-Event Foundation Model For Structured Medical Records","publication_year":2023,"publication_date":"2023-01-09","ids":{"openalex":"https://openalex.org/W4315588764","doi":"https://doi.org/10.48550/arxiv.2301.03150"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2301.03150","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2301.03150","pdf_url":"https://arxiv.org/pdf/2301.03150","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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/2301.03150","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010316094","display_name":"Ethan Steinberg","orcid":"https://orcid.org/0000-0001-7166-5032"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Steinberg, Ethan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028277225","display_name":"Jason Fries","orcid":"https://orcid.org/0000-0001-9316-5768"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fries, Jason","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103009473","display_name":"Yizhe Xu","orcid":"https://orcid.org/0000-0002-6179-8664"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Yizhe","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5041175834","display_name":"Nigam H. Shah","orcid":"https://orcid.org/0000-0001-9385-7158"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shah, Nigam","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5010316094"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":8,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9994999766349792,"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":0.9994999766349792,"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/T10350","display_name":"Electronic Health Records Systems","score":0.9819999933242798,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T14400","display_name":"Medical Coding and Health Information","score":0.9495000243186951,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6889551877975464},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6876763105392456},{"id":"https://openalex.org/keywords/software-portability","display_name":"Software portability","score":0.6487324237823486},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.6458716988563538},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5889274477958679},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5350756645202637},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.5202423334121704},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4834367036819458},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11965644359588623}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6889551877975464},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6876763105392456},{"id":"https://openalex.org/C63000827","wikidata":"https://www.wikidata.org/wiki/Q3080428","display_name":"Software portability","level":2,"score":0.6487324237823486},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6458716988563538},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5889274477958679},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5350756645202637},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.5202423334121704},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4834367036819458},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11965644359588623},{"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/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"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/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"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:2301.03150","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2301.03150","pdf_url":"https://arxiv.org/pdf/2301.03150","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2301.03150","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2301.03150","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2301.03150","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2301.03150","pdf_url":"https://arxiv.org/pdf/2301.03150","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4315588764.pdf","grobid_xml":"https://content.openalex.org/works/W4315588764.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W107105315","https://openalex.org/W4367156293","https://openalex.org/W1584537303","https://openalex.org/W4388155270","https://openalex.org/W1872724644","https://openalex.org/W2750549761","https://openalex.org/W28826848","https://openalex.org/W2122272819","https://openalex.org/W2130894091","https://openalex.org/W4385572368"],"abstract_inverted_index":{"We":[0,97,147],"present":[1],"a":[2,45,184],"self-supervised,":[3],"time-to-event":[4],"(TTE)":[5],"foundation":[6,156,175],"model":[7,157,176,188],"called":[8],"MOTOR":[9,82,123,155,171],"(Many":[10],"Outcome":[11],"Time":[12],"Oriented":[13],"Representations)":[14],"which":[15,49],"is":[16,50,172],"pretrained":[17,187],"on":[18,88,103,162],"timestamped":[19],"sequences":[20],"of":[21,41],"events":[22],"in":[23,54],"electronic":[24],"health":[25,29],"records":[26,93],"(EHR)":[27],"and":[28,115,181],"insurance":[30],"claims.":[31],"TTE":[32,57,179],"models":[33,58,120],"are":[34,74,140],"used":[35],"for":[36,158,177,189],"estimating":[37],"the":[38,42,163,173],"probability":[39],"distribution":[40],"time":[43,66],"until":[44],"specific":[46],"event":[47],"occurs,":[48],"an":[51],"important":[52],"task":[53],"medical":[55,178],"settings.":[56],"provide":[59],"many":[60],"advantages":[61],"over":[62,130],"classification":[63],"using":[64],"fixed":[65],"horizons,":[67],"including":[68],"naturally":[69],"handling":[70],"censored":[71],"observations,":[72],"but":[73],"challenging":[75],"to":[76,90,137,143],"train":[77],"with":[78],"limited":[79],"labeled":[80],"data.":[81],"addresses":[83],"this":[84],"challenge":[85],"by":[86,128,135,152],"pretraining":[87],"up":[89,136],"55M":[91],"patient":[92,108],"(9B":[94],"clinical":[95],"events).":[96],"evaluate":[98,149],"MOTOR's":[99],"transfer":[100],"learning":[101],"performance":[102],"19":[104],"tasks,":[105],"across":[106],"3":[107],"databases":[109],"(a":[110],"private":[111],"EHR":[112],"system,":[113],"MIMIC-IV,":[114],"Merative":[116],"claims":[117],"data).":[118],"Task-specific":[119],"adapted":[121],"from":[122],"improve":[124,132],"time-dependent":[125],"C":[126],"statistics":[127],"4.6%":[129],"state-of-the-art,":[131],"label":[133],"efficiency":[134],"95%":[138],",and":[139],"more":[141],"robust":[142],"temporal":[144],"distributional":[145],"shifts.":[146],"further":[148],"cross-site":[150],"portability":[151],"adapting":[153],"our":[154],"six":[159],"prediction":[160],"tasks":[161],"MIMIC-IV":[164],"dataset,":[165],"where":[166],"it":[167],"outperforms":[168],"all":[169],"baselines.":[170],"first":[174],"predictions":[180],"we":[182],"release":[183],"143M":[185],"parameter":[186],"research":[190],"use":[191],"at":[192],"[redacted":[193],"URL].":[194]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-07T14:57:38.498316","created_date":"2025-10-10T00:00:00"}