{"id":"https://openalex.org/W3128043152","doi":"https://doi.org/10.1109/bibm49941.2020.9313333","title":"DeepComp: Which Competing Event Will Hit the Patient First?","display_name":"DeepComp: Which Competing Event Will Hit the Patient First?","publication_year":2020,"publication_date":"2020-12-16","ids":{"openalex":"https://openalex.org/W3128043152","doi":"https://doi.org/10.1109/bibm49941.2020.9313333","mag":"3128043152"},"language":"en","primary_location":{"id":"doi:10.1109/bibm49941.2020.9313333","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm49941.2020.9313333","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"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/A5104705753","display_name":"Yingxue Li","orcid":"https://orcid.org/0000-0001-6350-612X"},"institutions":[{"id":"https://openalex.org/I4401726822","display_name":"Ping An (China)","ror":"https://ror.org/004yv2z91","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726822"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yingxue Li","raw_affiliation_strings":["Ping An Health Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Ping An Health Technology, Beijing, China","institution_ids":["https://openalex.org/I4401726822"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072705996","display_name":"Wenxiao Jia","orcid":"https://orcid.org/0000-0002-3647-9151"},"institutions":[{"id":"https://openalex.org/I4401726822","display_name":"Ping An (China)","ror":"https://ror.org/004yv2z91","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726822"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenxiao Jia","raw_affiliation_strings":["Ping An Health Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Ping An Health Technology, Beijing, China","institution_ids":["https://openalex.org/I4401726822"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022855408","display_name":"Ya\u2010Shu Kang","orcid":"https://orcid.org/0000-0001-6095-1383"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yashu Kang","raw_affiliation_strings":["College of computing, Georgia Institute of Technology, United States"],"affiliations":[{"raw_affiliation_string":"College of computing, Georgia Institute of Technology, United States","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048909793","display_name":"Tiange Chen","orcid":"https://orcid.org/0000-0002-3992-6391"},"institutions":[{"id":"https://openalex.org/I4401726822","display_name":"Ping An (China)","ror":"https://ror.org/004yv2z91","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726822"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tiange Chen","raw_affiliation_strings":["Ping An Health Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Ping An Health Technology, Beijing, China","institution_ids":["https://openalex.org/I4401726822"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100331094","display_name":"Xiang Li","orcid":"https://orcid.org/0000-0002-9851-6376"},"institutions":[{"id":"https://openalex.org/I4401726822","display_name":"Ping An (China)","ror":"https://ror.org/004yv2z91","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726822"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Li","raw_affiliation_strings":["Ping An Health Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Ping An Health Technology, Beijing, China","institution_ids":["https://openalex.org/I4401726822"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011357334","display_name":"Xin Du","orcid":"https://orcid.org/0000-0002-7463-8707"},"institutions":[{"id":"https://openalex.org/I4210119028","display_name":"Beijing Anzhen Hospital","ror":"https://ror.org/02h2j1586","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210119028"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Du","raw_affiliation_strings":["Beijing Anzhen Hospital, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Anzhen Hospital, Beijing, China","institution_ids":["https://openalex.org/I4210119028"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073552277","display_name":"Jianzeng Dong","orcid":"https://orcid.org/0000-0001-7299-7805"},"institutions":[{"id":"https://openalex.org/I4210119028","display_name":"Beijing Anzhen Hospital","ror":"https://ror.org/02h2j1586","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210119028"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianzeng Dong","raw_affiliation_strings":["Beijing Anzhen Hospital, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Anzhen Hospital, Beijing, China","institution_ids":["https://openalex.org/I4210119028"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011723711","display_name":"Changsheng Ma","orcid":"https://orcid.org/0000-0002-5387-5957"},"institutions":[{"id":"https://openalex.org/I4210119028","display_name":"Beijing Anzhen Hospital","ror":"https://ror.org/02h2j1586","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210119028"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changsheng Ma","raw_affiliation_strings":["Beijing Anzhen Hospital, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Anzhen Hospital, Beijing, China","institution_ids":["https://openalex.org/I4210119028"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100455768","display_name":"Fei Wang","orcid":"https://orcid.org/0000-0001-9459-9461"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fei Wang","raw_affiliation_strings":["Cornell University, United States"],"affiliations":[{"raw_affiliation_string":"Cornell University, United States","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023775139","display_name":"Guotong Xie","orcid":null},"institutions":[{"id":"https://openalex.org/I4401726822","display_name":"Ping An (China)","ror":"https://ror.org/004yv2z91","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726822"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guotong Xie","raw_affiliation_strings":["Ping An Health Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Ping An Health Technology, Beijing, China","institution_ids":["https://openalex.org/I4401726822"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":10,"corresponding_author_ids":["https://openalex.org/A5104705753"],"corresponding_institution_ids":["https://openalex.org/I4401726822"],"apc_list":null,"apc_paid":null,"fwci":0.1326,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.57983301,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"629","last_page":"636"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9995999932289124,"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":0.9995999932289124,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9886999726295471,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/T10136","display_name":"Statistical Methods and Inference","score":0.9833999872207642,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/censoring","display_name":"Censoring (clinical trials)","score":0.7799614667892456},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7090809345245361},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5961852073669434},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5480567812919617},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5250403881072998},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.46676748991012573},{"id":"https://openalex.org/keywords/conditional-probability","display_name":"Conditional probability","score":0.4543158710002899},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4506162405014038},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4228399991989136},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41830235719680786},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3542414903640747},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3409920632839203},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.21908238530158997},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.21555224061012268},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10504674911499023}],"concepts":[{"id":"https://openalex.org/C137668524","wikidata":"https://www.wikidata.org/wiki/Q189813","display_name":"Censoring (clinical trials)","level":2,"score":0.7799614667892456},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7090809345245361},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5961852073669434},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5480567812919617},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5250403881072998},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.46676748991012573},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.4543158710002899},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4506162405014038},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4228399991989136},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41830235719680786},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3542414903640747},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3409920632839203},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.21908238530158997},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.21555224061012268},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10504674911499023},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm49941.2020.9313333","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm49941.2020.9313333","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.5899999737739563},{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.41999998688697815}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1973165502","https://openalex.org/W1979300931","https://openalex.org/W2038981426","https://openalex.org/W2074703669","https://openalex.org/W2106421416","https://openalex.org/W2117974397","https://openalex.org/W2134978098","https://openalex.org/W2142300056","https://openalex.org/W2144922697","https://openalex.org/W2171515720","https://openalex.org/W2264767503","https://openalex.org/W2416828311","https://openalex.org/W2789172526","https://openalex.org/W2791340070","https://openalex.org/W2792137403","https://openalex.org/W2963246719","https://openalex.org/W3099478002"],"related_works":["https://openalex.org/W2363656491","https://openalex.org/W4376988598","https://openalex.org/W3008584592","https://openalex.org/W2575822034","https://openalex.org/W2365248444","https://openalex.org/W2064864137","https://openalex.org/W2694207508","https://openalex.org/W4239087487","https://openalex.org/W4285709146","https://openalex.org/W4382602469"],"abstract_inverted_index":{"When":[0],"taking":[1],"care":[2],"of":[3,13,50,73,106,117],"complex":[4,170],"patients":[5],"with":[6,30],"multiple":[7,32],"morbidities,":[8],"accurately":[9],"predicting":[10],"the":[11,31,51,71,74,81,90,103,115,122,135,139,146,157,165,169,174,183,189,226],"occurrence":[12],"each":[14,107,111,118,142,193],"cause-specific":[15],"event":[16,84,109,144],"is":[17,85,126,180],"critical":[18],"for":[19,141,192],"designing":[20],"optimal":[21],"treatment":[22,199],"plans.":[23],"However,":[24],"standard":[25],"survival":[26,63,136],"analysis":[27,64],"cannot":[28],"deal":[29],"(usually":[33],"competing)":[34],"adverse":[35],"events":[36,41],"and":[37,101,138,211],"views":[38,102],"those":[39],"competing":[40,75,108,143,175,190],"as":[42,114],"censored.":[43],"This":[44],"will":[45],"result":[46],"in":[47,167],"biased":[48],"estimation":[49],"incidence":[52],"rate.":[53],"In":[54,132],"this":[55,133],"paper,":[56],"we":[57],"propose":[58],"a":[59,95],"deep":[60],"learning":[61],"based":[62],"algorithm":[65],"called":[66],"DeepComp":[67,93,218],"to":[68,88,128,182,186,225],"jointly":[69],"predict":[70],"progress":[72],"events,":[76],"which":[77,83,196],"can":[78],"thus":[79],"inform":[80],"doctors":[82],"more":[86],"likely":[87],"hit":[89],"patient":[91],"first.":[92],"constructs":[94],"multi-task":[96],"recurrent":[97],"neural":[98],"network":[99],"(RNN)":[100],"conditional":[104],"probability":[105,123,137],"at":[110],"time":[112,147],"point":[113],"output":[116],"RNN":[119],"cell.":[120],"Then":[121],"chain":[124],"rule":[125],"utilized":[127],"combine":[129],"them":[130],"together.":[131],"way,":[134],"risk":[140],"over":[145],"space":[148],"are":[149],"obtained.":[150],"The":[151,216],"multitask":[152],"structure":[153],"not":[154],"only":[155],"prevents":[156],"model":[158,166],"from":[159],"unreasonable":[160],"censoring":[161],"but":[162],"also":[163],"aids":[164],"capturing":[168],"hidden":[171],"association":[172],"among":[173],"events.":[176],"A":[177],"novel":[178],"penalty":[179],"added":[181],"loss":[184],"function":[185],"better":[187],"discriminate":[188],"risks":[191],"particular":[194],"patient,":[195],"could":[197],"benefit":[198],"decision-making.":[200],"We":[201],"conduct":[202],"comprehensive":[203],"experiments":[204],"on":[205],"two":[206],"real-world":[207],"clinical":[208],"data":[209,214],"sets":[210],"one":[212],"synthetic":[213],"set.":[215],"proposed":[217],"method":[219],"achieves":[220],"significant":[221],"performance":[222],"improvement":[223],"compared":[224],"state-of-the-art":[227],"baseline":[228],"methods.":[229]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}