{"id":"https://openalex.org/W3111764145","doi":"https://doi.org/10.1109/smc42975.2020.9282952","title":"A System for Predicting Hospital Admission at Emergency Department Based on Electronic Health Record Using Convolution Neural Network","display_name":"A System for Predicting Hospital Admission at Emergency Department Based on Electronic Health Record Using Convolution Neural Network","publication_year":2020,"publication_date":"2020-10-11","ids":{"openalex":"https://openalex.org/W3111764145","doi":"https://doi.org/10.1109/smc42975.2020.9282952","mag":"3111764145"},"language":"en","primary_location":{"id":"doi:10.1109/smc42975.2020.9282952","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc42975.2020.9282952","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 Systems, Man, and Cybernetics (SMC)","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/A5015000598","display_name":"Li-Hung Yao","orcid":"https://orcid.org/0000-0001-5194-339X"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Li-Hung Yao","raw_affiliation_strings":["National Taiwan University, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"National Taiwan University, Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032554405","display_name":"Ka-Chun Leung","orcid":"https://orcid.org/0000-0003-1917-7554"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Ka-Chun Leung","raw_affiliation_strings":["National Taiwan University, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"National Taiwan University, Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083712950","display_name":"Jheng-Huang Hong","orcid":"https://orcid.org/0000-0003-1532-6516"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Jheng-Huang Hong","raw_affiliation_strings":["National Taiwan University, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"National Taiwan University, Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087454198","display_name":"Chu\u2010Lin Tsai","orcid":"https://orcid.org/0000-0003-4639-1513"},"institutions":[{"id":"https://openalex.org/I4210131804","display_name":"National Taiwan University Hospital","ror":"https://ror.org/03nteze27","country_code":"TW","type":"healthcare","lineage":["https://openalex.org/I4210131804"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chu-Lin Tsai","raw_affiliation_strings":["National Taiwan University Hospital, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"National Taiwan University Hospital, Taipei, Taiwan","institution_ids":["https://openalex.org/I4210131804"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101925654","display_name":"Li\u2010Chen Fu","orcid":"https://orcid.org/0000-0002-6947-7646"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Li-Chen Fu","raw_affiliation_strings":["National Taiwan University, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"National Taiwan University, Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5015000598"],"corresponding_institution_ids":["https://openalex.org/I16733864"],"apc_list":null,"apc_paid":null,"fwci":1.3064,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.82548718,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"546","last_page":"551"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11095","display_name":"Emergency and Acute Care Studies","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2711","display_name":"Emergency Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11095","display_name":"Emergency and Acute Care Studies","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2711","display_name":"Emergency Medicine"},"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9696999788284302,"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/T14374","display_name":"Statistical Methods in Epidemiology","score":0.9333999752998352,"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/triage","display_name":"Triage","score":0.8753740191459656},{"id":"https://openalex.org/keywords/emergency-department","display_name":"Emergency department","score":0.8178136944770813},{"id":"https://openalex.org/keywords/crowding","display_name":"Crowding","score":0.6485039591789246},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5672276020050049},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.5351771712303162},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.510765790939331},{"id":"https://openalex.org/keywords/medical-emergency","display_name":"Medical emergency","score":0.5069520473480225},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4530101716518402},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41818472743034363},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.4151465892791748},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.38669854402542114},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36234718561172485},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.1895902454853058},{"id":"https://openalex.org/keywords/nursing","display_name":"Nursing","score":0.16231000423431396}],"concepts":[{"id":"https://openalex.org/C2777120189","wikidata":"https://www.wikidata.org/wiki/Q780067","display_name":"Triage","level":2,"score":0.8753740191459656},{"id":"https://openalex.org/C2780724011","wikidata":"https://www.wikidata.org/wiki/Q1295316","display_name":"Emergency department","level":2,"score":0.8178136944770813},{"id":"https://openalex.org/C149333683","wikidata":"https://www.wikidata.org/wiki/Q5189188","display_name":"Crowding","level":2,"score":0.6485039591789246},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5672276020050049},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.5351771712303162},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.510765790939331},{"id":"https://openalex.org/C545542383","wikidata":"https://www.wikidata.org/wiki/Q2751242","display_name":"Medical emergency","level":1,"score":0.5069520473480225},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4530101716518402},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41818472743034363},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.4151465892791748},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.38669854402542114},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36234718561172485},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.1895902454853058},{"id":"https://openalex.org/C159110408","wikidata":"https://www.wikidata.org/wiki/Q121176","display_name":"Nursing","level":1,"score":0.16231000423431396},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc42975.2020.9282952","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc42975.2020.9282952","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 Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","score":0.47999998927116394,"display_name":"Partnerships for the goals"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322108","display_name":"Ministry of Science and Technology","ror":"https://ror.org/032e49973"},{"id":"https://openalex.org/F4320323900","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W4007095","https://openalex.org/W1509328462","https://openalex.org/W1538131130","https://openalex.org/W1592338563","https://openalex.org/W1623006543","https://openalex.org/W1674618911","https://openalex.org/W1832693441","https://openalex.org/W1887152358","https://openalex.org/W1952102297","https://openalex.org/W2008274261","https://openalex.org/W2009658183","https://openalex.org/W2029598996","https://openalex.org/W2038379663","https://openalex.org/W2068227175","https://openalex.org/W2097117768","https://openalex.org/W2117413738","https://openalex.org/W2120615054","https://openalex.org/W2126617751","https://openalex.org/W2133440648","https://openalex.org/W2144354855","https://openalex.org/W2345073978","https://openalex.org/W2400561932","https://openalex.org/W2746791326","https://openalex.org/W2752349109","https://openalex.org/W2752529319","https://openalex.org/W2788608859","https://openalex.org/W2797445167","https://openalex.org/W2884597820","https://openalex.org/W2888970663","https://openalex.org/W2902662000","https://openalex.org/W2963292145","https://openalex.org/W2964296212","https://openalex.org/W2964962196","https://openalex.org/W2984942011","https://openalex.org/W2989587439","https://openalex.org/W4220700636","https://openalex.org/W4241057490","https://openalex.org/W4300906778","https://openalex.org/W6632100814","https://openalex.org/W6636749155","https://openalex.org/W6678744208","https://openalex.org/W6684860816","https://openalex.org/W6770062216"],"related_works":["https://openalex.org/W2144451503","https://openalex.org/W2033023095","https://openalex.org/W125325933","https://openalex.org/W2147580721","https://openalex.org/W3034796941","https://openalex.org/W2288311891","https://openalex.org/W1983600708","https://openalex.org/W2002733784","https://openalex.org/W2593666988","https://openalex.org/W2087024057"],"abstract_inverted_index":{"Emergency":[0,54],"Department":[1],"(ED)":[2],"crowding":[3,43],"has":[4,39,58],"become":[5,59],"an":[6,189],"issue":[7],"of":[8,26,32,75,146,205],"delayed":[9],"patient":[10,203],"treatment":[11],"and":[12,44,89,158,176,221,225],"even":[13],"a":[14,60,104,114,124,162,177,182],"public":[15],"healthcare":[16],"problem":[17],"around":[18],"the":[19,29,35,70,73,85,109,128,147,174,186,218],"world.":[20],"According":[21],"to":[22,41,92,96,111,134,172,211],"recent":[23],"research":[24],"studies":[25,149],"many":[27],"countries,":[28],"increasing":[30],"number":[31],"patients":[33,95],"in":[34,46,67,100,140,228],"emergency":[36],"department":[37],"which":[38,200],"led":[40],"unprecedented":[42],"delays":[45],"care.":[47],"For":[48,184],"that":[49,106],"reason,":[50],"triage":[51,93,113],"into":[52],"five-level":[53],"Severity":[55],"Index":[56],"(ESI)":[57],"major":[61],"method":[62],"for":[63,155],"improving":[64],"medical":[65],"priorities":[66],"ED.":[68],"Although":[69],"ESI":[71,97],"mitigates":[72],"process":[74],"ED":[76,130,141],"treatment,":[77],"so":[78],"far":[79],"it":[80],"still":[81],"heavily":[82],"relies":[83],"on":[84,127,161],"nurse's":[86],"subjective":[87],"judgment":[88],"is":[90,117,198],"easy":[91],"most":[94,145],"level":[98],"3":[99],"current":[101],"practice.":[102],"Therefore,":[103],"system":[105,125,168],"can":[107],"help":[108],"doctors":[110],"accurately":[112],"patient's":[115],"condition":[116],"imperative.":[118],"In":[119],"this":[120],"work,":[121],"we":[122],"propose":[123],"based":[126],"patients'":[129],"electronic":[131],"health":[132],"record":[133],"predict":[135],"hospitalizations":[136],"after":[137],"assigned":[138],"procedures":[139],"are":[142],"completed.":[143],"While":[144],"related":[148],"have":[150],"employed":[151],"traditional":[152],"machine":[153],"learning":[154],"triage-related":[156],"classification":[157],"highly":[159],"relied":[160],"feature":[163],"selection":[164],"process,":[165],"our":[166,229],"proposed":[167],"used":[169,199],"data-to-image":[170],"transform":[171],"produce":[173],"input":[175],"convolutional":[178],"neural":[179],"network":[180],"as":[181],"classifier.":[183],"validation,":[185],"data":[187],"from":[188,209],"open":[190],"dataset":[191],"(National":[192],"Hospital":[193],"Ambulatory":[194],"Medical":[195],"Care":[196],"Survey)":[197],"includes":[201],"118,602":[202],"visits":[204],"United":[206],"States":[207],"EDs":[208],"2012":[210],"2016":[212],"survey":[213],"years.":[214],"To":[215],"sum":[216],"up,":[217],"resulting":[219],"AUROC":[220],"accuracy":[222],"achieve":[223],"0.86":[224],"0.77,":[226],"respectively,":[227],"work.":[230]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}