{"id":"https://openalex.org/W3137164726","doi":"https://doi.org/10.1109/bigdata50022.2020.9378420","title":"DUGRA: Dual-Graph Representation Learning for Health Information Networks","display_name":"DUGRA: Dual-Graph Representation Learning for Health Information Networks","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3137164726","doi":"https://doi.org/10.1109/bigdata50022.2020.9378420","mag":"3137164726"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9378420","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378420","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 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/A5055007304","display_name":"Qifan Wang","orcid":"https://orcid.org/0000-0002-5304-7975"},"institutions":[{"id":"https://openalex.org/I5023651","display_name":"McGill University","ror":"https://ror.org/01pxwe438","country_code":"CA","type":"education","lineage":["https://openalex.org/I5023651"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Qifan Wang","raw_affiliation_strings":["School of Computer Science, McGill University, Montreal, QC, Canada"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, McGill University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I5023651"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021788449","display_name":"Benjamin C. M. Fung","orcid":"https://orcid.org/0000-0001-8423-2906"},"institutions":[{"id":"https://openalex.org/I5023651","display_name":"McGill University","ror":"https://ror.org/01pxwe438","country_code":"CA","type":"education","lineage":["https://openalex.org/I5023651"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Benjamin C. M. Fung","raw_affiliation_strings":["School of Information Studies, McGill University, Montreal, QC, Canada"],"affiliations":[{"raw_affiliation_string":"School of Information Studies, McGill University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I5023651"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065705195","display_name":"Patrick C. K. Hung","orcid":"https://orcid.org/0000-0002-9903-4862"},"institutions":[{"id":"https://openalex.org/I39470171","display_name":"University of Ontario Institute of Technology","ror":"https://ror.org/016zre027","country_code":"CA","type":"education","lineage":["https://openalex.org/I39470171"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Patrick C. K. Hung","raw_affiliation_strings":["Faculty of Business and Information Technology, Ontario Tech University, Oshawa, ON, Canada"],"affiliations":[{"raw_affiliation_string":"Faculty of Business and Information Technology, Ontario Tech University, Oshawa, ON, Canada","institution_ids":["https://openalex.org/I39470171"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5055007304"],"corresponding_institution_ids":["https://openalex.org/I5023651"],"apc_list":null,"apc_paid":null,"fwci":0.2651,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.65799195,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"4961","last_page":"4970"},"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/T10028","display_name":"Topic Modeling","score":0.9968000054359436,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.993399977684021,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7423500418663025},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5969011187553406},{"id":"https://openalex.org/keywords/medical-diagnosis","display_name":"Medical diagnosis","score":0.591579020023346},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.558367908000946},{"id":"https://openalex.org/keywords/ontology","display_name":"Ontology","score":0.5489417314529419},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.5203378200531006},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4939773678779602},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4625270962715149},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.41868042945861816},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3371542990207672},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3256979286670685}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7423500418663025},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5969011187553406},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.591579020023346},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.558367908000946},{"id":"https://openalex.org/C25810664","wikidata":"https://www.wikidata.org/wiki/Q44325","display_name":"Ontology","level":2,"score":0.5489417314529419},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.5203378200531006},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4939773678779602},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4625270962715149},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.41868042945861816},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3371542990207672},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3256979286670685},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9378420","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378420","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 Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320994","display_name":"Canada Research Chairs","ror":"https://ror.org/0517h6h17"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":71,"referenced_works":["https://openalex.org/W6908809","https://openalex.org/W2141173017","https://openalex.org/W2153579005","https://openalex.org/W2164019165","https://openalex.org/W2255847468","https://openalex.org/W2284851926","https://openalex.org/W2396881363","https://openalex.org/W2481271618","https://openalex.org/W2517221375","https://openalex.org/W2519887557","https://openalex.org/W2557074642","https://openalex.org/W2604314403","https://openalex.org/W2606780347","https://openalex.org/W2621053657","https://openalex.org/W2690721124","https://openalex.org/W2742491462","https://openalex.org/W2751808960","https://openalex.org/W2788104422","https://openalex.org/W2796547658","https://openalex.org/W2807021761","https://openalex.org/W2889495884","https://openalex.org/W2896457183","https://openalex.org/W2896538705","https://openalex.org/W2905224888","https://openalex.org/W2914721378","https://openalex.org/W2952869515","https://openalex.org/W2962767366","https://openalex.org/W2962876161","https://openalex.org/W2962946486","https://openalex.org/W2963078493","https://openalex.org/W2963271116","https://openalex.org/W2963341956","https://openalex.org/W2963622218","https://openalex.org/W2963858333","https://openalex.org/W2964010366","https://openalex.org/W2964015378","https://openalex.org/W2964051675","https://openalex.org/W2964068143","https://openalex.org/W2964990093","https://openalex.org/W2965570621","https://openalex.org/W2978527981","https://openalex.org/W2985962305","https://openalex.org/W3000098859","https://openalex.org/W3005285779","https://openalex.org/W3017637887","https://openalex.org/W3099136959","https://openalex.org/W3100848837","https://openalex.org/W3112116031","https://openalex.org/W4294170691","https://openalex.org/W4294558607","https://openalex.org/W4297733535","https://openalex.org/W6600284362","https://openalex.org/W6680930200","https://openalex.org/W6682691769","https://openalex.org/W6684165356","https://openalex.org/W6685812147","https://openalex.org/W6726186668","https://openalex.org/W6726341914","https://openalex.org/W6726873649","https://openalex.org/W6736685754","https://openalex.org/W6738964360","https://openalex.org/W6739001092","https://openalex.org/W6743410771","https://openalex.org/W6748141922","https://openalex.org/W6748816092","https://openalex.org/W6754519054","https://openalex.org/W6755207826","https://openalex.org/W6757374366","https://openalex.org/W6765671411","https://openalex.org/W6769250239","https://openalex.org/W6772893534"],"related_works":["https://openalex.org/W4296209631","https://openalex.org/W4249377076","https://openalex.org/W3097449145","https://openalex.org/W2561617217","https://openalex.org/W4210389441","https://openalex.org/W2025378473","https://openalex.org/W2355801475","https://openalex.org/W2395241803","https://openalex.org/W2003333417","https://openalex.org/W2003211637"],"abstract_inverted_index":{"With":[0],"the":[1,23,28,55,60,69,73,82,117,120,125,128,131,134,137,151,157,162,175,188,200],"rapidly":[2],"growing":[3],"volume":[4],"and":[5,179],"variety":[6],"of":[7,30,63,81,87,119,136,177,206],"Electronic":[8],"Health":[9],"Records":[10],"(EHR)":[11],"data,":[12,35],"deep-learning":[13],"models":[14,203],"exhibit":[15],"state-of-the-art":[16,202],"performance":[17],"for":[18],"many":[19,36],"predictive":[20],"tasks":[21],"in":[22,33,97,164,204],"health":[24],"domain.":[25],"To":[26],"overcome":[27],"challenge":[29,49],"high":[31],"dimensionality":[32],"EHR":[34],"representation":[37],"learning":[38],"methods":[39,84],"have":[40],"been":[41],"proposed":[42],"to":[43,52,113,124,155,160],"learn":[44],"low-dimensional":[45],"diagnosis":[46,182,189,207],"representations.":[47],"Another":[48],"is":[50,76,102],"how":[51],"effectively":[53],"incorporate":[54],"domain":[56],"knowledge,":[57],"such":[58],"as":[59],"International":[61],"Classification":[62],"Diseases":[64],"(ICD)":[65],"medical":[66,74,121],"ontology,":[67],"into":[68],"learned":[70,138,191],"embeddings.":[71,139],"Albeit":[72],"ontology":[75,122,153],"a":[77,104,145,169,180],"knowledge":[78],"graph,":[79],"none":[80],"existing":[83],"take":[85],"advantage":[86],"Graph":[88],"Neural":[89],"Network":[90],"(GNN),":[91],"which":[92,110],"has":[93],"demonstrated":[94],"its":[95],"ability":[96],"other":[98],"domains.":[99],"The":[100],"problem":[101],"that":[103,187],"GNN":[105],"with":[106],"multiple":[107],"hidden":[108],"layers,":[109],"are":[111],"required":[112],"propagate":[114],"information":[115,163],"from":[116,150,192],"leaf":[118],"graph":[123,148,154,172],"root,":[126],"dilutes":[127],"differences":[129],"among":[130],"nodes,":[132],"degrading":[133],"quality":[135],"In":[140],"this":[141],"paper":[142],"we":[143,167],"introduce":[144],"densely":[146],"connected":[147],"derived":[149],"original":[152],"tackle":[156],"problem.":[158],"Furthermore,":[159],"model":[161],"patient":[165],"records,":[166],"construct":[168],"single":[170],"co-occurrence":[171,176],"based":[173],"on":[174],"diagnoses":[178],"patient's":[181],"history.":[183],"Experimental":[184],"results":[185],"show":[186],"embeddings":[190],"our":[193],"model,":[194],"DUal-GRAph":[195],"Representation":[196],"Learning":[197],"(DUGRA),":[198],"outperform":[199],"current":[201],"terms":[205],"prediction":[208],"accuracy.":[209]},"counts_by_year":[{"year":2021,"cited_by_count":2}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}
