{"id":"https://openalex.org/W7127442204","doi":"https://doi.org/10.48550/arxiv.2602.02179","title":"SurvKAN: A Fully Parametric Survival Model Based on Kolmogorov-Arnold Networks","display_name":"SurvKAN: A Fully Parametric Survival Model Based on Kolmogorov-Arnold Networks","publication_year":2026,"publication_date":"2026-02-02","ids":{"openalex":"https://openalex.org/W7127442204","doi":"https://doi.org/10.48550/arxiv.2602.02179"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.02179","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124957324","display_name":"Marina Mastroleo","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Mastroleo, Marina","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088968519","display_name":"Alberto Archetti","orcid":"https://orcid.org/0000-0003-3826-4645"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Archetti, Alberto","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040013478","display_name":"Federico Mastroleo","orcid":"https://orcid.org/0000-0001-6580-6767"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mastroleo, Federico","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5124934869","display_name":"Matteo Matteucci","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matteucci, Matteo","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5124957324"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9699000120162964,"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":0.9699000120162964,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.009499999694526196,"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":0.004699999932199717,"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/interpretability","display_name":"Interpretability","score":0.9438999891281128},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.7005000114440918},{"id":"https://openalex.org/keywords/proportional-hazards-model","display_name":"Proportional hazards model","score":0.5026000142097473},{"id":"https://openalex.org/keywords/survival-analysis","display_name":"Survival analysis","score":0.4823000133037567},{"id":"https://openalex.org/keywords/concordance","display_name":"Concordance","score":0.43059998750686646},{"id":"https://openalex.org/keywords/transparency","display_name":"Transparency (behavior)","score":0.42010000348091125},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.4124000072479248},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.3935999870300293}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9438999891281128},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.7005000114440918},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6180999875068665},{"id":"https://openalex.org/C50382708","wikidata":"https://www.wikidata.org/wiki/Q223218","display_name":"Proportional hazards model","level":2,"score":0.5026000142097473},{"id":"https://openalex.org/C10515644","wikidata":"https://www.wikidata.org/wiki/Q543310","display_name":"Survival analysis","level":2,"score":0.4823000133037567},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4812999963760376},{"id":"https://openalex.org/C160798450","wikidata":"https://www.wikidata.org/wiki/Q4230870","display_name":"Concordance","level":2,"score":0.43059998750686646},{"id":"https://openalex.org/C2780233690","wikidata":"https://www.wikidata.org/wiki/Q535347","display_name":"Transparency (behavior)","level":2,"score":0.42010000348091125},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41339999437332153},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.4124000072479248},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.3935999870300293},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3885999917984009},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3799999952316284},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.3774000108242035},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.37130001187324524},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3409000039100647},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.33329999446868896},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3122999966144562},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.266400009393692},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.2556999921798706},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.02179","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.02179","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.02179","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:doi:10.48550/arxiv.2602.02179","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":null,"raw_type":"Article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.7522501945495605}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"prediction":[1],"of":[2],"time-to-event":[3],"outcomes":[4],"is":[5],"critical":[6],"for":[7],"clinical":[8,51,68],"decision-making,":[9],"treatment":[10],"planning,":[11],"and":[12,40,59,72,179,184],"resource":[13],"allocation":[14],"in":[15,28],"modern":[16],"healthcare.":[17],"While":[18],"classical":[19,178],"survival":[20,109,143,166],"models":[21,77],"such":[22,82],"as":[23,83,124],"Cox":[24,97],"remain":[25,92],"widely":[26],"adopted":[27],"standard":[29,165],"practice,":[30],"they":[31],"rely":[32],"on":[33,112,140,164],"restrictive":[34],"assumptions,":[35],"including":[36],"linear":[37],"covariate":[38],"relationships":[39],"proportional":[41,118],"hazards":[42,119],"over":[43,160],"time,":[44],"that":[45,115,131,153,169,194],"often":[46],"fail":[47],"to":[48,87,128,177],"capture":[49],"real-world":[50],"dynamics.":[52],"Recent":[53],"deep":[54],"learning":[55],"approaches":[56],"like":[57],"DeepSurv":[58],"DeepHit":[60],"offer":[61],"improved":[62],"expressivity":[63],"but":[64,91],"sacrifice":[65],"interpretability,":[66],"limiting":[67],"adoption":[69],"where":[70],"trust":[71],"transparency":[73],"are":[74],"paramount.":[75],"Hybrid":[76],"incorporating":[78],"Kolmogorov-Arnold":[79],"Networks":[80],"(KANs),":[81],"CoxKAN,":[84],"have":[85],"begun":[86],"address":[88],"this":[89,100],"trade-off":[90],"constrained":[93],"by":[94],"the":[95,117,134,141],"semi-parametric":[96],"framework.":[98],"In":[99],"work":[101],"we":[102],"introduce":[103],"SurvKAN,":[104],"a":[105,129],"fully":[106],"parametric,":[107],"time-continuous":[108],"model":[110],"based":[111],"KAN":[113,130],"architectures":[114],"eliminates":[116],"constraint.":[120],"SurvKAN":[121,170],"treats":[122],"time":[123],"an":[125],"explicit":[126],"input":[127],"directly":[132],"predicts":[133],"log-hazard":[135],"function,":[136],"enabling":[137],"end-to-end":[138],"training":[139],"full":[142],"likelihood.":[144],"Our":[145],"architecture":[146],"preserves":[147],"interpretability":[148,188],"through":[149],"learnable":[150],"univariate":[151],"functions":[152],"indicate":[154],"how":[155],"individual":[156],"features":[157],"influence":[158],"risk":[159],"time.":[161],"Extensive":[162],"experiments":[163],"benchmarks":[167],"demonstrate":[168],"achieves":[171],"competitive":[172],"or":[173],"superior":[174],"performance":[175],"compared":[176],"state-of-the-art":[180],"baselines":[181],"across":[182],"concordance":[183],"calibration":[185],"metrics.":[186],"Additionally,":[187],"analyses":[189],"reveal":[190],"clinically":[191],"meaningful":[192],"patterns":[193],"align":[195],"with":[196],"medical":[197],"domain":[198],"knowledge.":[199]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-04T00:00:00"}
