{"id":"https://openalex.org/W4406259725","doi":"https://doi.org/10.1109/bibm62325.2024.10821868","title":"Mitigating Feature Homogenization in Deep Graph Architectures for Clinical Data Representation","display_name":"Mitigating Feature Homogenization in Deep Graph Architectures for Clinical Data Representation","publication_year":2024,"publication_date":"2024-12-03","ids":{"openalex":"https://openalex.org/W4406259725","doi":"https://doi.org/10.1109/bibm62325.2024.10821868"},"language":"en","primary_location":{"id":"doi:10.1109/bibm62325.2024.10821868","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm62325.2024.10821868","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 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/A5109747579","display_name":"Suyang Xi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210134189","display_name":"Xiamen University Malaysia","ror":"https://ror.org/0331wa828","country_code":"MY","type":"education","lineage":["https://openalex.org/I191208505","https://openalex.org/I4210134189"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Suyang Xi","raw_affiliation_strings":["Xiamen University Malaysia,School of Electrical Engineering and Artificial Intelligence,Selangor,Malaysia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen University Malaysia,School of Electrical Engineering and Artificial Intelligence,Selangor,Malaysia","institution_ids":["https://openalex.org/I4210134189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042490998","display_name":"Bolin Yang","orcid":"https://orcid.org/0000-0001-5423-4978"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bolin Yang","raw_affiliation_strings":["University of Chinese Academy of Sciences,College of Life Sciences,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences,College of Life Sciences,China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023122827","display_name":"Zhenghan Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenghan Chen","raw_affiliation_strings":["Microsoft,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft,China","institution_ids":["https://openalex.org/I4210113369"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0668,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.80789522,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"5801","last_page":"5808"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9735999703407288,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9735999703407288,"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":0.9550999999046326,"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"}},{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9513000249862671,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6853272318840027},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5449312329292297},{"id":"https://openalex.org/keywords/homogenization","display_name":"Homogenization (climate)","score":0.5057332515716553},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5005402565002441},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42053475975990295},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.333281934261322},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3209790587425232}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6853272318840027},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5449312329292297},{"id":"https://openalex.org/C2778722038","wikidata":"https://www.wikidata.org/wiki/Q17030643","display_name":"Homogenization (climate)","level":3,"score":0.5057332515716553},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5005402565002441},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42053475975990295},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.333281934261322},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3209790587425232},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C130217890","wikidata":"https://www.wikidata.org/wiki/Q47041","display_name":"Biodiversity","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm62325.2024.10821868","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm62325.2024.10821868","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 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":39,"referenced_works":["https://openalex.org/W102708294","https://openalex.org/W1888005072","https://openalex.org/W2078440929","https://openalex.org/W2119717200","https://openalex.org/W2132075088","https://openalex.org/W2396881363","https://openalex.org/W2517194566","https://openalex.org/W2604314403","https://openalex.org/W2610332124","https://openalex.org/W2622701666","https://openalex.org/W2728059831","https://openalex.org/W2756570351","https://openalex.org/W2774837955","https://openalex.org/W2798843374","https://openalex.org/W2907492528","https://openalex.org/W2962756421","https://openalex.org/W2977897270","https://openalex.org/W3006180822","https://openalex.org/W3021393704","https://openalex.org/W3035294872","https://openalex.org/W3035531049","https://openalex.org/W3091309561","https://openalex.org/W3121601851","https://openalex.org/W3152893301","https://openalex.org/W3174340663","https://openalex.org/W3175001161","https://openalex.org/W3193696571","https://openalex.org/W4221143523","https://openalex.org/W6608344535","https://openalex.org/W6631964550","https://openalex.org/W6636510571","https://openalex.org/W6678830454","https://openalex.org/W6692071231","https://openalex.org/W6744560608","https://openalex.org/W6746836310","https://openalex.org/W6754615649","https://openalex.org/W6769589523","https://openalex.org/W6780183200","https://openalex.org/W6810831694"],"related_works":["https://openalex.org/W2104620528","https://openalex.org/W140119233","https://openalex.org/W2757910772","https://openalex.org/W4249473811","https://openalex.org/W2394422079","https://openalex.org/W1492688905","https://openalex.org/W2524071042","https://openalex.org/W2550220886","https://openalex.org/W2025913943","https://openalex.org/W2514464965"],"abstract_inverted_index":{"This":[0],"research":[1],"redefines":[2],"International":[3],"Classification":[4],"of":[5,18,69,82],"Diseases":[6],"(ICD)":[7],"coding":[8,28,65],"as":[9],"a":[10,58,111,119],"sophisticated":[11],"multi-label":[12],"prediction":[13],"problem,":[14],"requiring":[15],"the":[16,67,79,103,128,138],"assignment":[17],"multiple":[19],"codes":[20],"to":[21,101],"detailed":[22,134],"discharge":[23],"summaries.":[24],"Current":[25],"automated":[26],"ICD":[27,64],"techniques":[29],"face":[30],"challenges":[31],"in":[32,75,84,91],"effectively":[33],"classifying":[34],"medical":[35],"diagnostic":[36],"texts":[37],"that":[38,62,126,161],"involve":[39],"complex":[40],"and":[41,173],"sparse":[42],"label":[43],"distributions,":[44],"especially":[45],"when":[46],"model":[47,98,144],"parameters":[48],"are":[49],"adjusted":[50],"using":[51,147],"traditional":[52],"backpropagation":[53],"methods.":[54],"We":[55],"present":[56],"LGG-NRGrand,":[57],"novel":[59],"adversarial":[60,149,154],"framework":[61],"approaches":[63],"through":[66],"generation":[68],"labeled":[70],"graphs.":[71],"A":[72],"significant":[73],"challenge":[74],"this":[76],"field":[77],"is":[78,99,145],"widespread":[80],"issue":[81],"Over-Smoothing":[83,129],"deep":[85,121],"graph":[86,108,122,135],"neural":[87,123],"networks,":[88],"which":[89],"results":[90,159],"uniform":[92],"or":[93],"indistinct":[94],"node":[95],"representations.":[96],"Our":[97],"designed":[100],"improve":[102],"capacity":[104],"for":[105],"learning":[106,140],"heterogeneous":[107],"representations":[109],"within":[110],"layered":[112],"network":[113,124],"architecture.":[114],"Specifically,":[115],"we":[116],"introduce":[117],"NRGrand,":[118],"single-relational":[120],"structure":[125],"mitigates":[127],"problem":[130],"while":[131],"capturing":[132],"more":[133],"features":[136],"during":[137],"representation":[139],"phase.":[141],"The":[142],"LGG-NRGrand":[143,162],"trained":[146],"an":[148,153],"reinforcement":[150],"framework,":[151],"employing":[152],"domain":[155],"adaptation":[156],"technique.":[157],"Experimental":[158],"indicate":[160],"surpasses":[163],"current":[164],"methods":[165],"on":[166],"key":[167],"evaluation":[168],"metrics,":[169],"including":[170],"micro-F1,":[171],"micro-AUC,":[172],"P@K.":[174]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}