{"id":"https://openalex.org/W4406613192","doi":"https://doi.org/10.1109/smc54092.2024.10831806","title":"CASSTIMP: Cascaded Architecture for Symptom Status Tracking with Inquiry-Aware Attention and Multi-Perception Pooling","display_name":"CASSTIMP: Cascaded Architecture for Symptom Status Tracking with Inquiry-Aware Attention and Multi-Perception Pooling","publication_year":2024,"publication_date":"2024-10-06","ids":{"openalex":"https://openalex.org/W4406613192","doi":"https://doi.org/10.1109/smc54092.2024.10831806"},"language":"en","primary_location":{"id":"doi:10.1109/smc54092.2024.10831806","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc54092.2024.10831806","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 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/A5101315521","display_name":"Haowen Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haowen Yu","raw_affiliation_strings":["Fudan University,Academy for Engineering &#x0026; Technology,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Fudan University,Academy for Engineering &#x0026; Technology,Shanghai,China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058367079","display_name":"M. H. Li","orcid":"https://orcid.org/0009-0000-6244-6081"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingcheng Li","raw_affiliation_strings":["Fudan University,Academy for Engineering &#x0026; Technology,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Fudan University,Academy for Engineering &#x0026; Technology,Shanghai,China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100414909","display_name":"Lihua Zhang","orcid":"https://orcid.org/0000-0003-2543-1547"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lihua Zhang","raw_affiliation_strings":["Fudan University,Academy for Engineering &#x0026; Technology,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Fudan University,Academy for Engineering &#x0026; Technology,Shanghai,China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101315521"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24061065,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3056","last_page":"3061"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9830999970436096,"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":0.9830999970436096,"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":0.9477999806404114,"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/pooling","display_name":"Pooling","score":0.8723924160003662},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.6942941546440125},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.675520658493042},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6723186373710632},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43462464213371277},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.43024903535842896},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.2556471526622772}],"concepts":[{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.8723924160003662},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.6942941546440125},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.675520658493042},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6723186373710632},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43462464213371277},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.43024903535842896},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.2556471526622772},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc54092.2024.10831806","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc54092.2024.10831806","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 Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5299999713897705,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G6240222859","display_name":null,"funder_award_id":"2021ZD0113502","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2114315281","https://openalex.org/W2146241755","https://openalex.org/W2565516711","https://openalex.org/W2806237610","https://openalex.org/W3021636956","https://openalex.org/W3141797743","https://openalex.org/W4206377173","https://openalex.org/W4210819377","https://openalex.org/W4282931663","https://openalex.org/W4312065988","https://openalex.org/W6762494002","https://openalex.org/W6767034639","https://openalex.org/W6767075311","https://openalex.org/W6770205404","https://openalex.org/W6779062772","https://openalex.org/W6785794352"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W147410782","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W3022252430","https://openalex.org/W4287804464","https://openalex.org/W3103989898","https://openalex.org/W3211292372","https://openalex.org/W803346624"],"abstract_inverted_index":{"Symptom":[0],"status":[1,77,176],"tracking":[2,177],"poses":[3],"a":[4,46,112],"significant":[5],"challenge":[6],"due":[7],"to":[8,86,103,130],"the":[9,95,118,147,161],"intricate":[10],"nature":[11],"of":[12],"symptom":[13,74,175],"identification":[14],"and":[15,34,43,63,76,134,141,152,178],"inference":[16],"from":[17,160],"medical":[18],"doctor-patient":[19],"dialogues.":[20],"Numerous":[21],"prior":[22],"studies":[23],"in":[24,55,66,97,117,122,174],"this":[25,51,123],"domain":[26],"have":[27],"relied":[28],"on":[29],"approaches":[30],"involving":[31],"multi-label":[32,88],"classification":[33,38,89],"multi-task":[35,71],"learning.":[36],"Multi-label":[37],"methods":[39],"typically":[40],"consider":[41],"symptoms":[42],"statuses":[44],"within":[45],"unified":[47],"label":[48],"space.":[49],"Nevertheless,":[50],"approach":[52,126],"frequently":[53],"results":[54,165],"sparse":[56],"predictions,":[57],"eroding":[58],"semantic":[59],"relationships":[60],"among":[61],"labels":[62],"causing":[64],"instability":[65],"prediction":[67,75,78],"outcomes.":[68],"In":[69],"contrast,":[70],"learning":[72],"segregates":[73],"into":[79],"separate":[80],"tasks,":[81],"thereby":[82],"improving":[83],"performance":[84],"relative":[85],"conventional":[87],"methods.":[90],"Nonetheless,":[91],"despite":[92],"these":[93,108],"advancements,":[94],"imbalance":[96],"training":[98],"task":[99],"weights":[100],"persists,":[101],"leading":[102],"suboptimal":[104],"performance.":[105],"To":[106],"tackle":[107],"challenges,":[109],"we":[110],"employ":[111],"cascaded":[113],"model":[114],"structure":[115],"rooted":[116],"Question-Answering":[119],"(QA)":[120],"paradigm":[121],"study.":[124],"Our":[125],"utilizes":[127],"dialogue":[128],"content":[129],"create":[131],"context-inquiry":[132],"pairs":[133],"introduces":[135],"two":[136],"novel":[137],"modules:":[138],"inquiry-aware":[139],"attention":[140,145],"multi-perception":[142,155],"pooling.":[143],"Inquiry-aware":[144],"enhances":[146],"contextual":[148],"relationship":[149],"between":[150],"inquiries":[151],"dialogues,":[153],"while":[154],"pooling":[156],"extracts":[157],"diverse":[158],"semantics":[159],"dialogue.":[162],"The":[163],"experimental":[164],"unequivocally":[166],"demonstrate":[167],"our":[168],"method's":[169],"efficiency,":[170],"surpassing":[171],"state-of-the-art":[172],"techniques":[173],"indicating":[179],"its":[180],"superior":[181],"effectiveness.":[182]},"counts_by_year":[],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}
