{"id":"https://openalex.org/W2782565073","doi":"https://doi.org/10.1109/bigdata.2017.8258049","title":"LSTM for septic shock: Adding unreliable labels to reliable predictions","display_name":"LSTM for septic shock: Adding unreliable labels to reliable predictions","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2782565073","doi":"https://doi.org/10.1109/bigdata.2017.8258049","mag":"2782565073"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2017.8258049","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258049","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","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/A5100368759","display_name":"Yuan Zhang","orcid":"https://orcid.org/0000-0003-1693-0889"},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yuan Zhang","raw_affiliation_strings":["Computer Science, North Carolina State University"],"affiliations":[{"raw_affiliation_string":"Computer Science, North Carolina State University","institution_ids":["https://openalex.org/I137902535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100746872","display_name":"Chen Lin","orcid":"https://orcid.org/0000-0003-4739-2999"},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chen Lin","raw_affiliation_strings":["Computer Science, North Carolina State University"],"affiliations":[{"raw_affiliation_string":"Computer Science, North Carolina State University","institution_ids":["https://openalex.org/I137902535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090231772","display_name":"Min Chi","orcid":"https://orcid.org/0000-0003-1765-7837"},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Min Chi","raw_affiliation_strings":["Computer Science, North Carolina State University"],"affiliations":[{"raw_affiliation_string":"Computer Science, North Carolina State University","institution_ids":["https://openalex.org/I137902535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057833118","display_name":"Julie S. Ivy","orcid":"https://orcid.org/0000-0002-1138-4076"},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Julie Ivy","raw_affiliation_strings":["Industrial and System Engineering, North Carolina State University"],"affiliations":[{"raw_affiliation_string":"Industrial and System Engineering, North Carolina State University","institution_ids":["https://openalex.org/I137902535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013796744","display_name":"M\u00fcge Capan","orcid":"https://orcid.org/0000-0003-3194-6117"},"institutions":[{"id":"https://openalex.org/I1281704429","display_name":"Christiana Care Health System","ror":"https://ror.org/02h905004","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1281704429"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Muge Capan","raw_affiliation_strings":["Christiana Care Health System"],"affiliations":[{"raw_affiliation_string":"Christiana Care Health System","institution_ids":["https://openalex.org/I1281704429"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077182059","display_name":"Jeanne M. Huddleston","orcid":"https://orcid.org/0000-0001-9101-4122"},"institutions":[{"id":"https://openalex.org/I2802423016","display_name":"WinnMed","ror":"https://ror.org/02s47w807","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I2802423016"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jeanne M. Huddleston","raw_affiliation_strings":["Mayo Clinic"],"affiliations":[{"raw_affiliation_string":"Mayo Clinic","institution_ids":["https://openalex.org/I2802423016"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100368759"],"corresponding_institution_ids":["https://openalex.org/I137902535"],"apc_list":null,"apc_paid":null,"fwci":2.9253,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.93115632,"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":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":0.9987000226974487,"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":0.9987000226974487,"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/T10218","display_name":"Sepsis Diagnosis and Treatment","score":0.9933000206947327,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"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/T11186","display_name":"Hydrology and Drought Analysis","score":0.947700023651123,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/septic-shock","display_name":"Septic shock","score":0.8755965232849121},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6236649751663208},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5433602333068848},{"id":"https://openalex.org/keywords/moment","display_name":"Moment (physics)","score":0.5009787082672119},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4995148181915283},{"id":"https://openalex.org/keywords/shock","display_name":"Shock (circulatory)","score":0.494326114654541},{"id":"https://openalex.org/keywords/sepsis","display_name":"Sepsis","score":0.4907478094100952},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47389641404151917},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.44855812191963196},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.442600280046463},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4095316231250763},{"id":"https://openalex.org/keywords/intensive-care-medicine","display_name":"Intensive care medicine","score":0.3547814190387726},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.1401793360710144},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.11246073246002197}],"concepts":[{"id":"https://openalex.org/C2777628635","wikidata":"https://www.wikidata.org/wiki/Q1765564","display_name":"Septic shock","level":3,"score":0.8755965232849121},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6236649751663208},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5433602333068848},{"id":"https://openalex.org/C179254644","wikidata":"https://www.wikidata.org/wiki/Q13222844","display_name":"Moment (physics)","level":2,"score":0.5009787082672119},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4995148181915283},{"id":"https://openalex.org/C2781300812","wikidata":"https://www.wikidata.org/wiki/Q178061","display_name":"Shock (circulatory)","level":2,"score":0.494326114654541},{"id":"https://openalex.org/C2778384902","wikidata":"https://www.wikidata.org/wiki/Q183134","display_name":"Sepsis","level":2,"score":0.4907478094100952},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47389641404151917},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.44855812191963196},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.442600280046463},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4095316231250763},{"id":"https://openalex.org/C177713679","wikidata":"https://www.wikidata.org/wiki/Q679690","display_name":"Intensive care medicine","level":1,"score":0.3547814190387726},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.1401793360710144},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.11246073246002197},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2017.8258049","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258049","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"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":39,"referenced_works":["https://openalex.org/W31857981","https://openalex.org/W111978056","https://openalex.org/W203426424","https://openalex.org/W768467596","https://openalex.org/W1481682751","https://openalex.org/W1489605872","https://openalex.org/W1807259383","https://openalex.org/W1943063538","https://openalex.org/W1988386616","https://openalex.org/W1989652873","https://openalex.org/W1993397663","https://openalex.org/W2001380325","https://openalex.org/W2004915879","https://openalex.org/W2040709089","https://openalex.org/W2045380240","https://openalex.org/W2055092429","https://openalex.org/W2083009186","https://openalex.org/W2100220027","https://openalex.org/W2107947924","https://openalex.org/W2112940647","https://openalex.org/W2123715374","https://openalex.org/W2141173017","https://openalex.org/W2145577370","https://openalex.org/W2154990912","https://openalex.org/W2162181053","https://openalex.org/W2163903329","https://openalex.org/W2280404143","https://openalex.org/W2372800617","https://openalex.org/W2402268235","https://openalex.org/W2490992274","https://openalex.org/W2622626827","https://openalex.org/W2742706476","https://openalex.org/W2963078493","https://openalex.org/W3017100980","https://openalex.org/W3023757607","https://openalex.org/W6604505254","https://openalex.org/W6647624743","https://openalex.org/W6680930200","https://openalex.org/W6713098461"],"related_works":["https://openalex.org/W2141522520","https://openalex.org/W1984304831","https://openalex.org/W3025110062","https://openalex.org/W4292539933","https://openalex.org/W2406430194","https://openalex.org/W2997146678","https://openalex.org/W2994793273","https://openalex.org/W2015975988","https://openalex.org/W2065102252","https://openalex.org/W2377678510"],"abstract_inverted_index":{"Sepsis":[0],"is":[1,126,205,221],"a":[2,20,83,89,111,133,179],"leading":[3],"cause":[4],"of":[5,17,41,79,88,128,141,152,202,210,227,239,246],"death":[6],"over":[7,132],"the":[8,13,38,42,76,149,158,162,170,203,219,224,228,237,244],"world":[9],"and":[10,29,35,91,161,191,194],"septic":[11,61,64,80,104,116,153,231,247],"shock,":[12],"most":[14,33],"severe":[15],"complication":[16],"sepsis,":[18],"reaches":[19],"mortality":[21],"rate":[22],"as":[23,25,60,157,169],"high":[24],"50%.":[26],"Early":[27],"diagnosis":[28],"treatment":[30],"can":[31],"prevent":[32],"morbidity":[34],"mortality.":[36],"Nowadays,":[37],"increasing":[39],"availability":[40],"electronic":[43],"health":[44],"records":[45],"(EHRs)":[46],"has":[47],"generated":[48],"great":[49],"interests":[50],"in":[51,82,243],"developing":[52,230],"models":[53],"to":[54,74,86,94,114,146],"predict":[55,115],"acute":[56],"medical":[57],"conditions":[58],"such":[59],"shock.":[62,105,232],"However,":[63],"shock":[65,81,117,248],"prediction":[66,226],"faces":[67],"two":[68,139],"major":[69],"challenges":[70],":":[71],"1)":[72],"how":[73,93],"capture":[75],"informative":[77,144],"progression":[78],"long":[84,134],"visit":[85],"hospital":[87],"patient;":[90],"2)":[92],"obtain":[95],"reliable":[96],"predictions":[97],"without":[98],"well-established":[99],"moment-by-moment":[100,171],"ground-truth":[101,213],"labels":[102,145],"for":[103,223],"In":[106],"this":[107],"work,":[108],"we":[109,216],"proposed":[110,241],"generic":[112],"framework":[113,137,220],"based":[118],"on":[119,178],"Long-Short":[120],"Term":[121],"Memory":[122],"(LSTM)":[123],"model,":[124],"which":[125],"capable":[127],"memorizing":[129],"temporal":[130],"dependencies":[131],"period.":[135],"The":[136,200,233],"integrates":[138],"levels":[140],"imperfect":[142],"yet":[143],"jointly":[147],"learn":[148],"discriminative":[150],"patterns":[151],"shock:":[154],"ICD-9":[155],"code":[156],"visit-level":[159],"label":[160],"clinical":[163],"criteria":[164],"designed":[165],"by":[166,188],"domain":[167],"experts":[168],"event-level":[172],"label.":[173],"We":[174],"evaluate":[175],"our":[176,240],"method":[177,204,242],"real-world":[180],"data":[181],"extracted":[182],"from":[183],"an":[184],"EHR":[185],"system":[186],"constituted":[187],"12,954":[189],"visits":[190],"1,348,625":[192],"events,":[193],"compare":[195],"it":[196],"against":[197],"multiple":[198],"baselines.":[199],"robustness":[201],"validated":[206],"using":[207],"three":[208],"sets":[209],"clinician-proposed":[211],"adjusted":[212],"labels.":[214],"Also,":[215],"explore":[217],"whether":[218],"effective":[222],"early":[225],"patients":[229],"experimental":[234],"results":[235],"demonstrate":[236],"superiority":[238],"task":[245],"prediction.":[249]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
