{"id":"https://openalex.org/W2977676495","doi":"https://doi.org/10.1109/ijcnn.2019.8852132","title":"Stochastic Imputation and Uncertainty-Aware Attention to EHR for Mortality Prediction","display_name":"Stochastic Imputation and Uncertainty-Aware Attention to EHR for Mortality Prediction","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2977676495","doi":"https://doi.org/10.1109/ijcnn.2019.8852132","mag":"2977676495"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8852132","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852132","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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/A5003023447","display_name":"Eunji Jun","orcid":"https://orcid.org/0000-0002-3121-7734"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Eunji Jun","raw_affiliation_strings":["Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041386692","display_name":"Ahmad Wisnu Mulyadi","orcid":"https://orcid.org/0000-0003-2449-6488"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Ahmad Wisnu Mulyadi","raw_affiliation_strings":["Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033720496","display_name":"Heung\u2010Il Suk","orcid":"https://orcid.org/0000-0001-7019-8962"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Heung-Il Suk","raw_affiliation_strings":["Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I197347611"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5003023447"],"corresponding_institution_ids":["https://openalex.org/I197347611"],"apc_list":null,"apc_paid":null,"fwci":2.3803,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.91499068,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9998999834060669,"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":0.9998999834060669,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10028","display_name":"Topic Modeling","score":0.9700000286102295,"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/missing-data","display_name":"Missing data","score":0.8469767570495605},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7503834366798401},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.747144341468811},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6784969568252563},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6178673505783081},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.540571928024292},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5337603688240051},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.529405951499939},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.526756227016449},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.47359636425971985},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4672059416770935},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3180080056190491}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.8469767570495605},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7503834366798401},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.747144341468811},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6784969568252563},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6178673505783081},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.540571928024292},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5337603688240051},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.529405951499939},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.526756227016449},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.47359636425971985},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4672059416770935},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3180080056190491}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8852132","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852132","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.8600000143051147}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1919216911","https://openalex.org/W1959608418","https://openalex.org/W2064675550","https://openalex.org/W2072604363","https://openalex.org/W2157331557","https://openalex.org/W2518582440","https://openalex.org/W2550179689","https://openalex.org/W2618813671","https://openalex.org/W2797790897","https://openalex.org/W2803403013","https://openalex.org/W2806031239","https://openalex.org/W2963078493","https://openalex.org/W2963258546","https://openalex.org/W2963271116","https://openalex.org/W2963911037","https://openalex.org/W2964010366","https://openalex.org/W2964121744","https://openalex.org/W3035623224","https://openalex.org/W4289760582","https://openalex.org/W6631190155","https://openalex.org/W6638444622","https://openalex.org/W6640963894","https://openalex.org/W6680930200","https://openalex.org/W6726186668","https://openalex.org/W6738179808","https://openalex.org/W6750349331","https://openalex.org/W6751145664","https://openalex.org/W6753068923"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549","https://openalex.org/W2753779043"],"abstract_inverted_index":{"Electronic":[0],"health":[1],"records":[2],"(EHR)":[3],"have":[4],"become":[5],"an":[6],"important":[7],"source":[8],"of":[9,18,25,48,91,107],"a":[10,16,31,66],"patient":[11],"data":[12,59,73],"but":[13],"characterized":[14],"by":[15],"variety":[17],"missing":[19,58,72,101],"values.":[20,102],"Using":[21],"the":[22,45,84,88,92,100,105],"variational":[23,28],"inference":[24],"Bayesian":[26],"framework,":[27],"autoencoder":[29],"(VAE),":[30],"deep":[32],"generative":[33,89],"model,":[34],"has":[35,54],"been":[36,55],"shown":[37],"to":[38,43],"be":[39],"efficient":[40],"and":[41,77,94],"accurate":[42],"capture":[44],"latent":[46],"structure":[47],"complex":[49],"high-dimensional":[50],"data.":[51],"Recently,":[52],"it":[53],"used":[56],"for":[57,115],"imputation.":[60],"In":[61],"this":[62],"paper,":[63],"we":[64],"propose":[65],"general":[67],"framework":[68],"that":[69],"incorporates":[70],"effective":[71],"imputation":[74],"using":[75],"VAE":[76,93],"multivariate":[78],"time":[79],"series":[80],"prediction.":[81],"We":[82,103],"utilize":[83],"uncertainty":[85],"obtained":[86],"from":[87],"network":[90],"employ":[95],"uncertainty-aware":[96],"attention":[97],"in":[98,129],"imputing":[99],"evaluated":[104],"performance":[106,124],"our":[108],"architecture":[109],"on":[110],"real-world":[111],"clinical":[112],"dataset":[113],"(MIMIC-III)":[114],"in-hospital":[116],"mortality":[117,130],"prediction":[118,131],"task.":[119,132],"Our":[120],"results":[121],"showed":[122],"higher":[123],"than":[125],"other":[126],"competing":[127],"methods":[128]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}