{"id":"https://openalex.org/W3011132328","doi":"https://doi.org/10.1109/tpami.2020.2979450","title":"Bias in Cross-Entropy-Based Training of Deep Survival Networks","display_name":"Bias in Cross-Entropy-Based Training of Deep Survival Networks","publication_year":2020,"publication_date":"2020-03-09","ids":{"openalex":"https://openalex.org/W3011132328","doi":"https://doi.org/10.1109/tpami.2020.2979450","mag":"3011132328","pmid":"https://pubmed.ncbi.nlm.nih.gov/32149626"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2020.2979450","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2020.2979450","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068791240","display_name":"Shekoufeh Gorgi Zadeh","orcid":"https://orcid.org/0000-0002-3314-2262"},"institutions":[{"id":"https://openalex.org/I135140700","display_name":"University of Bonn","ror":"https://ror.org/041nas322","country_code":"DE","type":"education","lineage":["https://openalex.org/I135140700"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Shekoufeh Gorgi Zadeh","raw_affiliation_strings":["Department of Computer Science, University of Bonn, Bonn, Germany"],"raw_orcid":"https://orcid.org/0000-0002-3314-2262","affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Bonn, Bonn, Germany","institution_ids":["https://openalex.org/I135140700"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031620559","display_name":"Matthias Schmid","orcid":"https://orcid.org/0000-0002-0788-0317"},"institutions":[{"id":"https://openalex.org/I135140700","display_name":"University of Bonn","ror":"https://ror.org/041nas322","country_code":"DE","type":"education","lineage":["https://openalex.org/I135140700"]},{"id":"https://openalex.org/I4210099379","display_name":"Zimmer Biomet (Germany)","ror":"https://ror.org/016731f38","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210099379","https://openalex.org/I4210115238"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Matthias Schmid","raw_affiliation_strings":["Department of Medical Biometry, Informatics and Epidemiology, Medical Faculty, University of Bonn, Bonn, Germany"],"raw_orcid":"https://orcid.org/0000-0002-0788-0317","affiliations":[{"raw_affiliation_string":"Department of Medical Biometry, Informatics and Epidemiology, Medical Faculty, University of Bonn, Bonn, Germany","institution_ids":["https://openalex.org/I135140700","https://openalex.org/I4210099379"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":8.5308,"has_fulltext":false,"cited_by_count":197,"citation_normalized_percentile":{"value":0.98153172,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"43","issue":"9","first_page":"3126","last_page":"3137"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9929999709129333,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9929999709129333,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9921000003814697,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9854999780654907,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.6922265887260437},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6274985671043396},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.5961475372314453},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4725379943847656},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4630996882915497},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4564807713031769},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44992291927337646},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39340484142303467}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6922265887260437},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6274985671043396},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.5961475372314453},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4725379943847656},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4630996882915497},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4564807713031769},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44992291927337646},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39340484142303467},{"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/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","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:10.1109/tpami.2020.2979450","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2020.2979450","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:32149626","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/32149626","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on pattern analysis and machine intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7400000095367432,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G899897991","display_name":null,"funder_award_id":"SCHM 2966/2-1","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"}],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1457323852","https://openalex.org/W1972283434","https://openalex.org/W2074703669","https://openalex.org/W2084139018","https://openalex.org/W2108673310","https://openalex.org/W2119910794","https://openalex.org/W2132432664","https://openalex.org/W2143612262","https://openalex.org/W2144922697","https://openalex.org/W2162249417","https://openalex.org/W2169033570","https://openalex.org/W2415877456","https://openalex.org/W2506664684","https://openalex.org/W2565516711","https://openalex.org/W2571620227","https://openalex.org/W2587019100","https://openalex.org/W2592929672","https://openalex.org/W2611099133","https://openalex.org/W2618421739","https://openalex.org/W2745940724","https://openalex.org/W2761668583","https://openalex.org/W2789172526","https://openalex.org/W2889217150","https://openalex.org/W2894748956","https://openalex.org/W2894845372","https://openalex.org/W2963246719","https://openalex.org/W2963924008","https://openalex.org/W3105445034","https://openalex.org/W4246466113","https://openalex.org/W4298642237","https://openalex.org/W4300874613","https://openalex.org/W6716448252","https://openalex.org/W6733081445","https://openalex.org/W6736995961","https://openalex.org/W6738327226","https://openalex.org/W6744891212","https://openalex.org/W6749156665"],"related_works":["https://openalex.org/W230091440","https://openalex.org/W2233261550","https://openalex.org/W2810751659","https://openalex.org/W258997015","https://openalex.org/W2997094352","https://openalex.org/W4375867731","https://openalex.org/W3216976533","https://openalex.org/W4380075502","https://openalex.org/W3046682795","https://openalex.org/W2994927414"],"abstract_inverted_index":{"Over":[0],"the":[1,8,23,30,50,78,98,143,161,174,178,199],"last":[2],"years,":[3],"utilizing":[4],"deep":[5,46,90],"learning":[6],"for":[7,29,39,88],"analysis":[9,76],"of":[10,25,32,52,62,77,105,165],"survival":[11,47,63,91,145],"data":[12,41,64],"has":[13,20],"become":[14],"attractive":[15],"to":[16,22],"many":[17],"researchers.":[18],"This":[19],"led":[21],"advent":[24],"numerous":[26],"network":[27],"architectures":[28],"prediction":[31,136,187],"possibly":[33],"censored":[34],"time-to-event":[35,168],"variables.":[36],"Unlike":[37],"networks":[38,48],"cross-sectional":[40],"(used":[42],"e.g.,":[43],"in":[44,103,133,142,183,191],"classification),":[45],"require":[49],"specification":[51],"a":[53,84,106,134,139,166],"suitably":[54],"defined":[55,102],"loss":[56,80,86,100,155,176,181],"function":[57,87,156,164],"that":[58,128,157,172],"incorporates":[59],"typical":[60],"characteristics":[61],"such":[65],"as":[66,196],"censoring":[67],"and":[68,116,123,138,189],"time-dependent":[69],"features.":[70],"Here,":[71],"we":[72,126,151],"provide":[73],"an":[74,153,192],"in-depth":[75],"cross-entropy":[79,99,175],"function,":[81],"which":[82],"is":[83,101,158],"popular":[85],"training":[89],"networks.":[92],"For":[93],"each":[94],"time":[95],"point":[96],"t,":[97],"terms":[104],"binary":[107],"outcome":[108],"with":[109],"levels":[110],"\"event":[111,117],"at":[112],"or":[113],"before":[114],"t\"":[115],"after":[118],"t\".":[119],"Using":[120],"both":[121],"theoretical":[122],"empirical":[124],"approaches,":[125],"show":[127,171],"this":[129,149],"definition":[130],"may":[131],"result":[132],"high":[135],"error":[137],"heavy":[140],"bias":[141],"predicted":[144],"probabilities.":[146],"To":[147],"overcome":[148],"problem,":[150],"analyze":[152],"alternative":[154],"derived":[159],"from":[160],"negative":[162,179],"log-likelihood":[163,180],"discrete":[167],"model.":[169],"We":[170],"replacing":[173],"by":[177,198],"results":[182],"much":[184],"better":[185],"calibrated":[186],"rules":[188],"also":[190],"improved":[193],"discriminatory":[194],"power,":[195],"measured":[197],"concordance":[200],"index.":[201]},"counts_by_year":[{"year":2026,"cited_by_count":24},{"year":2025,"cited_by_count":63},{"year":2024,"cited_by_count":47},{"year":2023,"cited_by_count":29},{"year":2022,"cited_by_count":21},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":4}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}