{"id":"https://openalex.org/W7126044583","doi":"https://doi.org/10.1109/bibm66473.2025.11356887","title":"Multi-Modal Follow-Up Data-Guided Aggregated Representation for Predicting Gout Recurrence Risk","display_name":"Multi-Modal Follow-Up Data-Guided Aggregated Representation for Predicting Gout Recurrence Risk","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126044583","doi":"https://doi.org/10.1109/bibm66473.2025.11356887"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356887","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356887","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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/A5124176276","display_name":"Baisong Li","orcid":null},"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":true,"raw_author_name":"Baisong Li","raw_affiliation_strings":["Tsinghua University,Tsinghua Medicine,China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,Tsinghua Medicine,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124190764","display_name":"Ruohan Liu","orcid":null},"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":"Ruohan Liu","raw_affiliation_strings":["Tsinghua University,Department of Automation,China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100758587","display_name":"Ling Zheng","orcid":"https://orcid.org/0000-0002-2746-2171"},"institutions":[{"id":"https://openalex.org/I4210127901","display_name":"Fuzhou Maternity and Child Health Care Hospital","ror":"https://ror.org/02sjprm39","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210127901"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ling Zheng","raw_affiliation_strings":["Fujian Maternity and Child Health Hospital,Computer Science Department,China"],"affiliations":[{"raw_affiliation_string":"Fujian Maternity and Child Health Hospital,Computer Science Department,China","institution_ids":["https://openalex.org/I4210127901"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124177480","display_name":"Qian Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I200296433","display_name":"Chinese Academy of Medical Sciences & Peking Union Medical College","ror":"https://ror.org/02drdmm93","country_code":"CN","type":"education","lineage":["https://openalex.org/I200296433"]},{"id":"https://openalex.org/I2801228662","display_name":"Peking Union Medical College Hospital","ror":"https://ror.org/04jztag35","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I2801228662"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Wang","raw_affiliation_strings":["Peking Union Medical College Hospital, Peking Union Medical College,Chinese Academy of Medical Sciences,Department of Rheumatology and Clinical Immunology,China"],"affiliations":[{"raw_affiliation_string":"Peking Union Medical College Hospital, Peking Union Medical College,Chinese Academy of Medical Sciences,Department of Rheumatology and Clinical Immunology,China","institution_ids":["https://openalex.org/I200296433","https://openalex.org/I2801228662"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124188599","display_name":"Xuegong Zhang","orcid":null},"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":"Xuegong Zhang","raw_affiliation_strings":["Tsinghua University,Department of Automation,China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074195858","display_name":"Hairong Lv","orcid":"https://orcid.org/0000-0003-1568-6861"},"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":"Hairong Lv","raw_affiliation_strings":["Tsinghua University,Department of Automation,China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation,China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5124176276"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.73735409,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4768","last_page":"4775"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11139","display_name":"Gout, Hyperuricemia, Uric Acid","score":0.98580002784729,"subfield":{"id":"https://openalex.org/subfields/2727","display_name":"Nephrology"},"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/T11139","display_name":"Gout, Hyperuricemia, Uric Acid","score":0.98580002784729,"subfield":{"id":"https://openalex.org/subfields/2727","display_name":"Nephrology"},"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/T10293","display_name":"Thyroid Disorders and Treatments","score":0.0010000000474974513,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"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/T13919","display_name":"Medicinal plant effects and applications","score":0.0007999999797903001,"subfield":{"id":"https://openalex.org/subfields/2707","display_name":"Complementary and alternative medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5776000022888184},{"id":"https://openalex.org/keywords/gout","display_name":"Gout","score":0.4986000061035156},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4481000006198883},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.39340001344680786},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.36970001459121704},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.3571999967098236}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6922000050544739},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6825000047683716},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6482999920845032},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5776000022888184},{"id":"https://openalex.org/C2780402116","wikidata":"https://www.wikidata.org/wiki/Q133087","display_name":"Gout","level":2,"score":0.4986000061035156},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4481000006198883},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.39340001344680786},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.36970001459121704},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.3571999967098236},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2996000051498413},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.29490000009536743},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2759000062942505},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356887","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356887","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.448883056640625,"display_name":"No poverty","id":"https://metadata.un.org/sdg/1"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1994439773","https://openalex.org/W2177870565","https://openalex.org/W2525984666","https://openalex.org/W2906295032","https://openalex.org/W2912689160","https://openalex.org/W2965373594","https://openalex.org/W2993447762","https://openalex.org/W3008467625","https://openalex.org/W3011057444","https://openalex.org/W3034280373","https://openalex.org/W3116515397","https://openalex.org/W3174396451","https://openalex.org/W4221046078","https://openalex.org/W4293576329","https://openalex.org/W4321600112","https://openalex.org/W4361298490","https://openalex.org/W4379229569","https://openalex.org/W4379600756","https://openalex.org/W4384561707","https://openalex.org/W4388010083","https://openalex.org/W4396896348","https://openalex.org/W4399809670","https://openalex.org/W4400409956","https://openalex.org/W4404658388","https://openalex.org/W4414304175"],"related_works":[],"abstract_inverted_index":{"Gout":[0],"recurrence":[1],"is":[2,16,55,144],"common":[3],"in":[4],"real-world":[5,64],"settings.":[6],"While":[7],"traditional":[8,116],"machine":[9,96,117],"learning":[10,62,97,118],"methods":[11,119],"are":[12],"applicable,":[13],"their":[14],"performance":[15],"often":[17],"limited":[18],"by":[19],"a":[20,43,72,82,154],"lack":[21],"of":[22,28,50,141],"diverse":[23],"data":[24,105],"modalities,":[25],"insufficient":[26],"understanding":[27],"inter-modality":[29],"interactions,":[30],"and":[31,76,106,134,153],"poor":[32],"model":[33],"generalizability.":[34],"To":[35],"address":[36],"these":[37],"challenges,":[38],"this":[39],"work":[40],"proposes":[41],"ARL-GRP,":[42],"novel":[44],"framework":[45,54],"for":[46],"forecasting":[47],"the":[48,139],"risk":[49,80,132],"gout":[51,79],"recurrence.":[52],"This":[53],"built":[56],"upon":[57],"three":[58],"essential":[59],"modules:":[60],"continuous":[61],"utilising":[63],"multimodel":[65],"follow-up":[66],"data,":[67],"feature":[68],"representation":[69],"aggregation":[70],"employing":[71],"pretrained":[73],"large":[74],"encoder,":[75],"predicting":[77],"recurrent":[78],"using":[81,150],"multilayer":[83],"perceptron.":[84],"The":[85],"experimental":[86],"comparison":[87],"demonstrates":[88,126],"that":[89],"our":[90,142],"proposed":[91],"approach":[92],"generally":[93],"outperforms":[94],"conventional":[95],"techniques.":[98],"ARL-GRP":[99],"can":[100],"effectively":[101],"combine":[102],"structured":[103],"clinical":[104,136],"unstructured":[107],"medical":[108],"narratives":[109],"into":[110],"unified":[111],"patient":[112],"representations,":[113],"significantly":[114],"outperforming":[115],"(Accuracy:":[120],"0.931,":[121],"AUC:":[122],"0.969).":[123],"Our":[124],"method":[125,143],"strong":[127],"predictive":[128],"capability,":[129],"enabling":[130],"precise":[131],"assessment":[133],"personalised":[135],"decision-making.":[137],"Furthermore,":[138],"effectiveness":[140],"also":[145],"consolidated":[146],"through":[147],"additional":[148],"analysis":[149],"ROC":[151],"curves":[152],"heatmap.":[155]},"counts_by_year":[],"updated_date":"2026-02-01T03:34:12.195049","created_date":"2026-01-30T00:00:00"}