{"id":"https://openalex.org/W2773114958","doi":"https://doi.org/10.1109/bibm.2017.8217753","title":"Patient outcome prediction via convolutional neural networks based on multi-granularity medical concept embedding","display_name":"Patient outcome prediction via convolutional neural networks based on multi-granularity medical concept embedding","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2773114958","doi":"https://doi.org/10.1109/bibm.2017.8217753","mag":"2773114958"},"language":"en","primary_location":{"id":"doi:10.1109/bibm.2017.8217753","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2017.8217753","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 Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5110664700","display_name":"Yujuan Feng","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153682","display_name":"Intelligent Health (United Kingdom)","ror":"https://ror.org/0576zak10","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210153682"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yujuan Feng","raw_affiliation_strings":["Department of Computer Science and Technology, State Key Lab of Intelligent Technology and Systems"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, State Key Lab of Intelligent Technology and Systems","institution_ids":["https://openalex.org/I4210153682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100413849","display_name":"Min Xu","orcid":"https://orcid.org/0000-0001-9581-8849"},"institutions":[{"id":"https://openalex.org/I4210153682","display_name":"Intelligent Health (United Kingdom)","ror":"https://ror.org/0576zak10","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210153682"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Xu Min","raw_affiliation_strings":["Department of Computer Science and Technology, State Key Lab of Intelligent Technology and Systems"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, State Key Lab of Intelligent Technology and Systems","institution_ids":["https://openalex.org/I4210153682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100446686","display_name":"Ning Chen","orcid":"https://orcid.org/0000-0001-8384-2948"},"institutions":[{"id":"https://openalex.org/I4210153682","display_name":"Intelligent Health (United Kingdom)","ror":"https://ror.org/0576zak10","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210153682"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ning Chen","raw_affiliation_strings":["Department of Computer Science and Technology, State Key Lab of Intelligent Technology and Systems"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, State Key Lab of Intelligent Technology and Systems","institution_ids":["https://openalex.org/I4210153682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100357677","display_name":"Hu Chen","orcid":"https://orcid.org/0000-0001-9300-6572"},"institutions":[{"id":"https://openalex.org/I271893122","display_name":"National Health and Family Planning Commission","ror":"https://ror.org/052eegr76","country_code":"CN","type":"government","lineage":["https://openalex.org/I271893122","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hu Chen","raw_affiliation_strings":["Bureau of Medical Administration National Health and Family Planning Commission People's Republic of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bureau of Medical Administration National Health and Family Planning Commission People's Republic of China","institution_ids":["https://openalex.org/I271893122"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002391709","display_name":"Xiaolei Xie","orcid":"https://orcid.org/0000-0002-5133-9712"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaolei Xie","raw_affiliation_strings":["Department of Industrial Engineering, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100328796","display_name":"Haibo Wang","orcid":"https://orcid.org/0000-0002-8580-829X"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haibo Wang","raw_affiliation_strings":["China Standard Medical Information Research Center, Shenzhen, Guangdong, China","Clinical Trial Unit, Sun Yat-sen University, Guangzhou, Guangdong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Standard Medical Information Research Center, Shenzhen, Guangdong, China","institution_ids":[]},{"raw_affiliation_string":"Clinical Trial Unit, Sun Yat-sen University, Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100443189","display_name":"Ting Chen","orcid":"https://orcid.org/0000-0002-3228-9166"},"institutions":[{"id":"https://openalex.org/I4210153682","display_name":"Intelligent Health (United Kingdom)","ror":"https://ror.org/0576zak10","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210153682"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ting Chen","raw_affiliation_strings":["Department of Computer Science and Technology, State Key Lab of Intelligent Technology and Systems"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, State Key Lab of Intelligent Technology and Systems","institution_ids":["https://openalex.org/I4210153682"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"770","last_page":"777"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9998000264167786,"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":0.9998000264167786,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9914000034332275,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.991100013256073,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.783572793006897},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7672703266143799},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6614546775817871},{"id":"https://openalex.org/keywords/ontology","display_name":"Ontology","score":0.549938976764679},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.548712432384491},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5060858130455017},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5056018829345703},{"id":"https://openalex.org/keywords/outcome","display_name":"Outcome (game theory)","score":0.4953247606754303},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4834190607070923},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4540775716304779}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.783572793006897},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7672703266143799},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6614546775817871},{"id":"https://openalex.org/C25810664","wikidata":"https://www.wikidata.org/wiki/Q44325","display_name":"Ontology","level":2,"score":0.549938976764679},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.548712432384491},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5060858130455017},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5056018829345703},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.4953247606754303},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4834190607070923},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4540775716304779},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm.2017.8217753","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2017.8217753","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 Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1614298861","https://openalex.org/W1923902524","https://openalex.org/W2022142729","https://openalex.org/W2027106132","https://openalex.org/W2042954874","https://openalex.org/W2043902829","https://openalex.org/W2050189583","https://openalex.org/W2099307202","https://openalex.org/W2101234009","https://openalex.org/W2117130368","https://openalex.org/W2153579005","https://openalex.org/W2163605009","https://openalex.org/W2187089797","https://openalex.org/W2259469853","https://openalex.org/W2404901863","https://openalex.org/W2405317410","https://openalex.org/W2481271618","https://openalex.org/W2511950764","https://openalex.org/W2514071032","https://openalex.org/W2610332124","https://openalex.org/W2613452149","https://openalex.org/W2950577311","https://openalex.org/W2998704965","https://openalex.org/W4285719527","https://openalex.org/W4294170691","https://openalex.org/W6675354045","https://openalex.org/W6680532216","https://openalex.org/W6682691769","https://openalex.org/W6684191040","https://openalex.org/W6726028665","https://openalex.org/W6737540812"],"related_works":["https://openalex.org/W2931688134","https://openalex.org/W2377919138","https://openalex.org/W2378857091","https://openalex.org/W103652678","https://openalex.org/W4226090359","https://openalex.org/W2059697060","https://openalex.org/W936373746","https://openalex.org/W2975817033","https://openalex.org/W4382701072","https://openalex.org/W4256502920"],"abstract_inverted_index":{"The":[0,91,110],"large":[1],"availability":[2],"of":[3,14,54,63,79,104,128,149,170],"biomedical":[4],"data":[5,177],"brings":[6],"opportunities":[7],"and":[8,32,70,77,100,178,186],"challenges":[9],"to":[10,59,124],"health":[11,33],"care.":[12],"Representation":[13],"medical":[15,26,55,71,97,108,131,150,162,184],"concepts":[16,56],"has":[17],"been":[18],"well":[19],"studied":[20],"in":[21,153,183],"many":[22],"applications,":[23],"such":[24],"as":[25],"informatics,":[27],"cohort":[28],"selection,":[29],"risk":[30],"prediction,":[31],"care":[34],"quality":[35,103],"measurement.":[36],"In":[37,164],"this":[38],"paper,":[39],"we":[40],"propose":[41],"an":[42],"efficient":[43],"multichannel":[44],"convolutional":[45],"neural":[46],"network":[47],"(CNN)":[48],"model":[49,139,167],"based":[50,144],"on":[51,73,145],"multi-granularity":[52],"embeddings":[53],"named":[57],"MG-CNN,":[58],"examine":[60],"the":[61,84,102,117,126,146,154,175],"effect":[62],"individual":[64],"patient":[65],"characteristics":[66],"including":[67],"demographic":[68],"factors":[69],"comorbidities":[72],"total":[74],"hospital":[75],"costs":[76],"length":[78],"stay":[80],"(LOS)":[81],"by":[82,116],"using":[83],"Hospital":[85],"Quality":[86],"Monitoring":[87],"System":[88],"(HQMS)":[89],"data.":[90],"proposed":[92],"embedding":[93,105],"method":[94],"leverages":[95],"prior":[96],"hierarchical":[98],"ontology":[99],"improves":[101],"for":[106,158],"rare":[107],"concepts.":[109,132],"embedded":[111],"vectors":[112],"are":[113],"further":[114],"visualized":[115],"t-Distributed":[118],"Stochastic":[119],"Neighbor":[120],"Embedding":[121],"(t-SNE)":[122],"technique":[123],"demonstrate":[125,135],"effectiveness":[127],"grouping":[129],"related":[130],"Experimental":[133],"results":[134],"that":[136],"our":[137],"MG-CNN":[138,166],"outperforms":[140],"traditional":[141],"regression":[142],"methods":[143],"one-hot":[147],"representation":[148],"concepts,":[151],"especially":[152],"outcome":[155],"prediction":[156],"tasks":[157],"patients":[159],"with":[160],"low-frequency":[161],"events.":[163],"summary,":[165],"is":[168],"capable":[169],"mining":[171],"potential":[172],"knowledge":[173],"from":[174],"clinical":[176,188],"will":[179],"be":[180],"broadly":[181],"applicable":[182],"research":[185],"inform":[187],"decisions.":[189]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
