{"id":"https://openalex.org/W4402856754","doi":"https://doi.org/10.1145/3627673.3679947","title":"SurvReLU: Inherently Interpretable Survival Analysis via Deep ReLU Networks","display_name":"SurvReLU: Inherently Interpretable Survival Analysis via Deep ReLU Networks","publication_year":2024,"publication_date":"2024-10-20","ids":{"openalex":"https://openalex.org/W4402856754","doi":"https://doi.org/10.1145/3627673.3679947"},"language":"en","primary_location":{"id":"doi:10.1145/3627673.3679947","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627673.3679947","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3627673.3679947","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xiaotong Sun","orcid":"https://orcid.org/0009-0004-3520-4369"},"institutions":[{"id":"https://openalex.org/I78715868","display_name":"University of Arkansas at Fayetteville","ror":"https://ror.org/05jbt9m15","country_code":"US","type":"education","lineage":["https://openalex.org/I78715868"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xiaotong Sun","raw_affiliation_strings":["University of Arkansas, Fayetteville, AR, USA"],"affiliations":[{"raw_affiliation_string":"University of Arkansas, Fayetteville, AR, USA","institution_ids":["https://openalex.org/I78715868"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058590658","display_name":"Peijie Qiu","orcid":"https://orcid.org/0000-0002-1591-5436"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peijie Qiu","raw_affiliation_strings":["Washington University in St. Louis, St. Louis, MO, USA"],"affiliations":[{"raw_affiliation_string":"Washington University in St. Louis, St. Louis, MO, USA","institution_ids":["https://openalex.org/I204465549"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052240317","display_name":"Shengfan Zhang","orcid":"https://orcid.org/0000-0001-9866-7177"},"institutions":[{"id":"https://openalex.org/I78715868","display_name":"University of Arkansas at Fayetteville","ror":"https://ror.org/05jbt9m15","country_code":"US","type":"education","lineage":["https://openalex.org/I78715868"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shengfan Zhang","raw_affiliation_strings":["University of Arkansas, Fayetteville, AR, USA"],"affiliations":[{"raw_affiliation_string":"University of Arkansas, Fayetteville, AR, USA","institution_ids":["https://openalex.org/I78715868"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I78715868"],"apc_list":null,"apc_paid":null,"fwci":0.3407,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.66526708,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"4081","last_page":"4085"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9955000281333923,"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":0.9955000281333923,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9671000242233276,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.965399980545044,"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/interpretability","display_name":"Interpretability","score":0.92247474193573},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7147065997123718},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.668170154094696},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6146960258483887},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5150501728057861},{"id":"https://openalex.org/keywords/black-box","display_name":"Black box","score":0.4885800778865814},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4841252267360687},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4837815463542938},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.48133787512779236},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.4121033549308777},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3362252712249756},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10435470938682556}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.92247474193573},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7147065997123718},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.668170154094696},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6146960258483887},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5150501728057861},{"id":"https://openalex.org/C94966114","wikidata":"https://www.wikidata.org/wiki/Q29256","display_name":"Black box","level":2,"score":0.4885800778865814},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4841252267360687},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4837815463542938},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.48133787512779236},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.4121033549308777},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3362252712249756},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10435470938682556},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3627673.3679947","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627673.3679947","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2407.14463","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2407.14463","pdf_url":"https://arxiv.org/pdf/2407.14463","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":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3627673.3679947","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627673.3679947","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.7200000286102295,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G6910641553","display_name":null,"funder_award_id":"1920920","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2005895632","https://openalex.org/W2097998348","https://openalex.org/W2110992644","https://openalex.org/W2146842127","https://openalex.org/W2515050826","https://openalex.org/W2789172526","https://openalex.org/W2932252889","https://openalex.org/W2963246719","https://openalex.org/W3022643593","https://openalex.org/W3099478002","https://openalex.org/W4287828539","https://openalex.org/W4293241248","https://openalex.org/W4298426643","https://openalex.org/W6674385629","https://openalex.org/W6774302960","https://openalex.org/W6776486363"],"related_works":["https://openalex.org/W2797441709","https://openalex.org/W2346578521","https://openalex.org/W4297660007","https://openalex.org/W2886918272","https://openalex.org/W4387589990","https://openalex.org/W2943982549","https://openalex.org/W3101055019","https://openalex.org/W2910028250","https://openalex.org/W4241566321","https://openalex.org/W3094353829"],"abstract_inverted_index":{"Survival":[0],"analysis":[1],"models":[2,10,41,67,72],"time-to-event":[3],"distributions":[4],"with":[5,98],"censorship.":[6],"Recently,":[7],"deep":[8,65,74,86,103],"survival":[9,40,66,71,104,113],"using":[11],"neural":[12],"networks":[13],"have":[14],"dominated":[15],"due":[16,52],"to":[17,47,49,53],"their":[18,25],"representational":[19,100],"power":[20,101],"and":[21,68,111,127],"state-of-the-art":[22],"performance.":[23],"However,":[24],"\"black-box\"":[26],"nature":[27],"hinders":[28],"interpretability,":[29],"which":[30],"is":[31,131],"crucial":[32],"in":[33,123],"real-world":[34],"applications.":[35],"In":[36,56],"contrast,":[37],"\"white-box\"":[38],"tree-based":[39,70,96],"offer":[42],"better":[43],"interpretability":[44,94],"but":[45],"struggle":[46],"converge":[48],"global":[50],"optima":[51],"greedy":[54],"expansion.":[55],"this":[57],"paper,":[58],"we":[59],"bridge":[60],"the":[61,93,99,117,120],"gap":[62],"between":[63],"previous":[64],"traditional":[69],"through":[73],"rectified":[75],"linear":[76],"unit":[77],"(ReLU)":[78],"networks.":[79],"We":[80],"show":[81],"that":[82],"a":[83],"deliberately":[84],"constructed":[85],"ReLU":[87],"network":[88],"(termed":[89],"SurvReLU)":[90],"can":[91],"harness":[92],"of":[95,102,119,125],"structures":[97],"models.":[105],"Empirical":[106],"studies":[107],"on":[108],"both":[109],"simulated":[110],"real":[112],"benchmark":[114],"datasets":[115],"showed":[116],"effectiveness":[118],"proposed":[121],"SurvReLU":[122],"terms":[124],"performance":[126],"interoperability.":[128],"The":[129],"code":[130],"available":[132],"at":[133],"https://github.com/xs018/SurvReLU.":[134]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2024-09-26T00:00:00"}