{"id":"https://openalex.org/W4408345594","doi":"https://doi.org/10.1109/icassp49660.2025.10888956","title":"Hybrid Feature Fusion for Enhancing Medical Document Embedding","display_name":"Hybrid Feature Fusion for Enhancing Medical Document Embedding","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408345594","doi":"https://doi.org/10.1109/icassp49660.2025.10888956"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10888956","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10888956","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5031030335","display_name":"Yi Zhu","orcid":"https://orcid.org/0000-0002-5586-3773"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Zhu","raw_affiliation_strings":["South China Normal University,Guangdong,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China Normal University,Guangdong,China","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100341795","display_name":"Xiangyang Liu","orcid":"https://orcid.org/0000-0002-2481-3909"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangyang Liu","raw_affiliation_strings":["South China Normal University,Guangdong,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China Normal University,Guangdong,China","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043537106","display_name":"Tianqi Pang","orcid":"https://orcid.org/0009-0005-2639-7813"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianqi Pang","raw_affiliation_strings":["South China Normal University,Guangdong,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China Normal University,Guangdong,China","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080363048","display_name":"Xuncan Xiao","orcid":null},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuncan Xiao","raw_affiliation_strings":["South China Normal University,Guangdong,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China Normal University,Guangdong,China","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100330729","display_name":"Xiaofan Zhang","orcid":"https://orcid.org/0000-0001-5081-3972"},"institutions":[{"id":"https://openalex.org/I4391012619","display_name":"Shanghai Artificial Intelligence Laboratory","ror":"https://ror.org/03wkvpx79","country_code":null,"type":"facility","lineage":["https://openalex.org/I4391012619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaofan Zhang","raw_affiliation_strings":["Shanghai AI Lab,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai AI Lab,Shanghai,China","institution_ids":["https://openalex.org/I4391012619"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064015146","display_name":"Chenyou Fan","orcid":"https://orcid.org/0000-0002-9835-8507"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenyou Fan","raw_affiliation_strings":["South China Normal University,Guangdong,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China Normal University,Guangdong,China","institution_ids":["https://openalex.org/I187400657"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.7836999893188477,"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/T10028","display_name":"Topic Modeling","score":0.7836999893188477,"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":0.7802000045776367,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.7322999835014343,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6651175022125244},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.6119129657745361},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5958366394042969},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5446313619613647},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5316028594970703},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4135951101779938}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6651175022125244},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.6119129657745361},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5958366394042969},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5446313619613647},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5316028594970703},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4135951101779938},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49660.2025.10888956","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10888956","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2970474271","https://openalex.org/W3024308166","https://openalex.org/W3092288641","https://openalex.org/W3156332541","https://openalex.org/W4291023040","https://openalex.org/W4294969208","https://openalex.org/W4309674289","https://openalex.org/W4317898419","https://openalex.org/W4385654096","https://openalex.org/W4386958277","https://openalex.org/W4389921502","https://openalex.org/W4396530167","https://openalex.org/W4400771220","https://openalex.org/W4402351542","https://openalex.org/W6739901393","https://openalex.org/W6746023985","https://openalex.org/W6755207826","https://openalex.org/W6755977528","https://openalex.org/W6774314701","https://openalex.org/W6777615688","https://openalex.org/W6844194202","https://openalex.org/W6847303613","https://openalex.org/W6849921291","https://openalex.org/W6851775633","https://openalex.org/W6852800892","https://openalex.org/W6854866820","https://openalex.org/W6866539314","https://openalex.org/W6867176674"],"related_works":["https://openalex.org/W2081900870","https://openalex.org/W2037549926","https://openalex.org/W2345479200","https://openalex.org/W2183306018","https://openalex.org/W2849310602","https://openalex.org/W3006008237","https://openalex.org/W2419146053","https://openalex.org/W4388890789","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"Despite":[0],"the":[1,31,39,74,82,87],"strong":[2],"capabilities":[3],"of":[4,77],"large":[5],"language":[6],"models":[7,111],"in":[8,21,30],"generative":[9],"tasks,":[10,50],"issues":[11],"related":[12],"to":[13,62,72,99],"information":[14],"unreliability":[15],"and":[16,28],"hallucinations":[17],"pose":[18],"significant":[19],"challenges":[20],"high-precision":[22],"fields,":[23],"such":[24],"as":[25],"drug":[26],"analysis":[27],"recommendations":[29],"medical":[32,47],"domain.":[33],"In":[34],"this":[35],"work,":[36],"we":[37],"introduce":[38],"HFFN":[40,66,103],"model,":[41],"a":[42,57,100],"retrieval-augmented":[43],"framework":[44],"designed":[45],"for":[46],"document":[48],"retrieval":[49,64],"which":[51],"combines":[52],"an":[53],"embedding":[54,110],"backbone":[55],"with":[56,85],"Hybrid":[58],"Feature":[59],"Fusion":[60],"module":[61],"enhance":[63],"quality.":[65],"improves":[67,104],"model":[68],"representation":[69],"by":[70,112],"learning":[71],"control":[73],"weight":[75],"parameters":[76],"nonlinear":[78],"features,":[79],"thereby":[80],"avoiding":[81],"instability":[83],"associated":[84],"using":[86],"same":[88],"activation":[89],"function":[90],"across":[91,107],"different":[92],"datasets.":[93],"Experimental":[94],"results":[95],"demonstrate":[96],"that,":[97],"compared":[98],"single-hidden-layer":[101],"MLP,":[102],"NDCG":[105],"score":[106],"various":[108],"baseline":[109],"2.3%-15.1%.":[113]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}