{"id":"https://openalex.org/W7125908079","doi":"https://doi.org/10.1109/smc58881.2025.11342849","title":"MoViE: A Mixture-Of-Experts Multimodal Fusion Model for Cardiovascular Disease Detection","display_name":"MoViE: A Mixture-Of-Experts Multimodal Fusion Model for Cardiovascular Disease Detection","publication_year":2025,"publication_date":"2025-10-05","ids":{"openalex":"https://openalex.org/W7125908079","doi":"https://doi.org/10.1109/smc58881.2025.11342849"},"language":null,"primary_location":{"id":"doi:10.1109/smc58881.2025.11342849","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11342849","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 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/A5026381160","display_name":"Guodao Zhang","orcid":"https://orcid.org/0000-0002-6264-5854"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Guodao Zhang","raw_affiliation_strings":["Hangzhou Dianzi University,Institute of Intelligent Media Computing,Hangzhou,China"],"affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University,Institute of Intelligent Media Computing,Hangzhou,China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124112788","display_name":"Cunnan Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cunnan Wei","raw_affiliation_strings":["Hangzhou Dianzi University,School of Computer Science,Hangzhou,China"],"affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University,School of Computer Science,Hangzhou,China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041187887","display_name":"Yanjie Lu","orcid":"https://orcid.org/0000-0001-7203-1445"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanjie Lu","raw_affiliation_strings":["Hangzhou Dianzi University,Institute of Intelligent Media Computing,Hangzhou,China"],"affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University,Institute of Intelligent Media Computing,Hangzhou,China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018174894","display_name":"Hongyu Sun","orcid":"https://orcid.org/0000-0002-5926-4495"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Sun","raw_affiliation_strings":["Hangzhou Dianzi University,Institute of Intelligent Media Computing,Hangzhou,China"],"affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University,Institute of Intelligent Media Computing,Hangzhou,China","institution_ids":["https://openalex.org/I50760025"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5026381160"],"corresponding_institution_ids":["https://openalex.org/I50760025"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.71970168,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"782","last_page":"787"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9437000155448914,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular 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/T11021","display_name":"ECG Monitoring and Analysis","score":0.9437000155448914,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular 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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.012900000438094139,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.009200000204145908,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4959999918937683},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4593999981880188},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4187999963760376},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.39660000801086426},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.3921000063419342},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.3831000030040741},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.3727000057697296},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.335999995470047},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33410000801086426}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6722000241279602},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6148999929428101},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4959999918937683},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4668999910354614},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4593999981880188},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4187999963760376},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4180999994277954},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.39660000801086426},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.3921000063419342},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.3831000030040741},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.3727000057697296},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.335999995470047},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33410000801086426},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.3188999891281128},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.3125999867916107},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.31040000915527344},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.30660000443458557},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2987000048160553},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.296099990606308},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.29109999537467957},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C3020493868","wikidata":"https://www.wikidata.org/wiki/Q55631277","display_name":"Real world data","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.2793000042438507},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2728999853134155},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2535000145435333},{"id":"https://openalex.org/C3019952477","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Health records","level":3,"score":0.2535000145435333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc58881.2025.11342849","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11342849","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 Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.4929097890853882}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2945020349","https://openalex.org/W3186223381","https://openalex.org/W4304777638","https://openalex.org/W4312212137","https://openalex.org/W4321367639","https://openalex.org/W4322753871","https://openalex.org/W4381487215","https://openalex.org/W4397032058","https://openalex.org/W4399766684","https://openalex.org/W4401388637","https://openalex.org/W4402544459","https://openalex.org/W4402631159","https://openalex.org/W4403332058","https://openalex.org/W4406611481","https://openalex.org/W4409190085"],"related_works":[],"abstract_inverted_index":{"Cardiovascular":[0],"disease":[1,171],"(CVD)":[2],"remains":[3],"one":[4],"of":[5,58,163],"the":[6,52,55,59,78,133,161],"leading":[7],"global":[8],"health":[9,39],"challenges,":[10],"calling":[11],"for":[12,43,136,169],"accurate":[13],"and":[14,37,74,86,146,157],"scalable":[15],"detection":[16,139],"methods.":[17],"This":[18],"work":[19],"presents":[20],"MoViE,":[21],"a":[22,48,66,101,113,120,166],"multimodal":[23,147],"fusion":[24,115],"model":[25,79],"based":[26],"on":[27,132],"sparsely":[28],"activated":[29],"experts,":[30,76],"designed":[31],"to":[32,80,105,126],"effectively":[33],"fuse":[34],"electrocardiogram":[35],"(ECG)":[36],"electronic":[38],"record":[40],"(EHR)":[41],"data":[42],"CVD":[44],"detection.":[45,172],"MoViE":[46,142,164],"adopts":[47],"dual-branch":[49],"architecture.":[50],"In":[51],"ECG":[53],"branch,":[54],"feed-forward":[56],"layers":[57],"Vision":[60],"Transformer":[61],"(ViT)":[62],"are":[63],"replaced":[64],"with":[65],"Mixture-Of-Experts":[67],"(MoE)":[68],"module":[69,116],"that":[70,141],"incorporates":[71],"top-k":[72],"routing":[73],"shared":[75],"enabling":[77],"capture":[81],"both":[82],"fine-grained":[83],"waveform":[84],"features":[85],"long-range":[87],"temporal":[88],"dependencies.":[89],"The":[90],"EHR":[91],"branch":[92],"encodes":[93],"structured":[94],"clinical":[95],"records":[96],"using":[97],"TF-IDF,":[98],"followed":[99],"by":[100],"multilayer":[102],"perceptron":[103],"(MLP)":[104],"extract":[106],"semantic":[107],"representations.":[108],"To":[109],"support":[110],"modality":[111],"interaction,":[112],"MoE-based":[114],"is":[117],"introduced,":[118],"where":[119],"gating":[121],"network":[122],"adaptively":[123],"selects":[124],"experts":[125],"combine":[127],"complementary":[128],"features.":[129],"Extensive":[130],"experiments":[131],"MIMIC-IV-ECG":[134],"dataset":[135],"myocardial":[137],"infarction":[138],"demonstrate":[140],"outperforms":[143],"existing":[144],"unimodal":[145],"mainstream":[148],"baselines,":[149],"achieving":[150],"87.98%":[151],"Accuracy,":[152],"90.70%":[153],"AUC,":[154],"66.15%":[155],"F1-score,":[156],"59.12%":[158],"MCC,":[159],"highlighting":[160],"potential":[162],"as":[165],"general":[167],"framework":[168],"intelligent":[170]},"counts_by_year":[],"updated_date":"2026-01-29T23:17:01.242718","created_date":"2026-01-29T00:00:00"}