{"id":"https://openalex.org/W7126049363","doi":"https://doi.org/10.1109/bibm66473.2025.11356704","title":"DiffFNN-Med: Task-Adaptive Fuzzy Neural Networks for Interpretable Medical Drug Recommendation","display_name":"DiffFNN-Med: Task-Adaptive Fuzzy Neural Networks for Interpretable Medical Drug Recommendation","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126049363","doi":"https://doi.org/10.1109/bibm66473.2025.11356704"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356704","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356704","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":"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/A5123026091","display_name":"Zhao Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I202334528","display_name":"Beijing Electronic Science and Technology Institute","ror":"https://ror.org/01xdzh226","country_code":"CN","type":"education","lineage":["https://openalex.org/I202334528"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhao Wang","raw_affiliation_strings":["Beijing Electronic Science and Technology Institute,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Electronic Science and Technology Institute,Beijing,China","institution_ids":["https://openalex.org/I202334528"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123024095","display_name":"Maosheng He","orcid":null},"institutions":[{"id":"https://openalex.org/I202334528","display_name":"Beijing Electronic Science and Technology Institute","ror":"https://ror.org/01xdzh226","country_code":"CN","type":"education","lineage":["https://openalex.org/I202334528"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Maosheng He","raw_affiliation_strings":["Beijing Electronic Science and Technology Institute,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Electronic Science and Technology Institute,Beijing,China","institution_ids":["https://openalex.org/I202334528"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhiqiang Liang","orcid":null},"institutions":[{"id":"https://openalex.org/I202334528","display_name":"Beijing Electronic Science and Technology Institute","ror":"https://ror.org/01xdzh226","country_code":"CN","type":"education","lineage":["https://openalex.org/I202334528"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiqiang Liang","raw_affiliation_strings":["Beijing Electronic Science and Technology Institute,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Electronic Science and Technology Institute,Beijing,China","institution_ids":["https://openalex.org/I202334528"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123038677","display_name":"Yifan He","orcid":null},"institutions":[{"id":"https://openalex.org/I202334528","display_name":"Beijing Electronic Science and Technology Institute","ror":"https://ror.org/01xdzh226","country_code":"CN","type":"education","lineage":["https://openalex.org/I202334528"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifan He","raw_affiliation_strings":["Beijing Electronic Science and Technology Institute,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Electronic Science and Technology Institute,Beijing,China","institution_ids":["https://openalex.org/I202334528"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110492323","display_name":"Xiuze Dong","orcid":null},"institutions":[{"id":"https://openalex.org/I202334528","display_name":"Beijing Electronic Science and Technology Institute","ror":"https://ror.org/01xdzh226","country_code":"CN","type":"education","lineage":["https://openalex.org/I202334528"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiuze Dong","raw_affiliation_strings":["Beijing Electronic Science and Technology Institute,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Electronic Science and Technology Institute,Beijing,China","institution_ids":["https://openalex.org/I202334528"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I202334528"],"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":"5509","last_page":"5516"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.600600004196167,"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":0.600600004196167,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.12470000237226486,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.027499999850988388,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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/interpretability","display_name":"Interpretability","score":0.954200029373169},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6004999876022339},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.5415999889373779},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.28929999470710754},{"id":"https://openalex.org/keywords/expert-system","display_name":"Expert system","score":0.28630000352859497},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.28450000286102295}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.954200029373169},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.713100016117096},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6122999787330627},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6080999970436096},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6004999876022339},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.5415999889373779},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3734000027179718},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.28929999470710754},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C195975749","wikidata":"https://www.wikidata.org/wiki/Q1475705","display_name":"Fuzzy control system","level":3,"score":0.2793000042438507},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C103637391","wikidata":"https://www.wikidata.org/wiki/Q5308921","display_name":"Drug repositioning","level":3,"score":0.2720000147819519},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.25699999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356704","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356704","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":[{"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals","score":0.42276012897491455}],"awards":[{"id":"https://openalex.org/G7275232469","display_name":null,"funder_award_id":"3282024046","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1670263352","https://openalex.org/W1974758710","https://openalex.org/W2085752173","https://openalex.org/W2113072832","https://openalex.org/W2154896031","https://openalex.org/W2159583324","https://openalex.org/W2516809705","https://openalex.org/W2558999090","https://openalex.org/W2767891136","https://openalex.org/W2785947426","https://openalex.org/W2786016794","https://openalex.org/W2809156537","https://openalex.org/W2826750344","https://openalex.org/W2987928072","https://openalex.org/W3000082418","https://openalex.org/W3045200674","https://openalex.org/W3047010778","https://openalex.org/W4252684946","https://openalex.org/W4411235396"],"related_works":[],"abstract_inverted_index":{"Medical":[0],"AI":[1],"requires":[2],"both":[3],"accuracy":[4],"and":[5,60,81,86,121],"interpretability":[6,44],"for":[7,19,119],"clinical":[8,126],"adoption.":[9],"This":[10,113],"paper":[11],"systematically":[12],"analyzes":[13],"Differentiable":[14],"Fuzzy":[15],"Neural":[16],"Networks":[17],"(DiffFNN)":[18],"medical":[20,28],"drug":[21,57],"recommendation,":[22],"investigating":[23],"accuracy-interpretability":[24],"trade-offs":[25],"across":[26],"diverse":[27],"tasks.":[29],"We":[30],"extend":[31],"DiffFNN":[32],"with":[33,99],"a":[34,69,103,116],"neural":[35],"enhancement":[36],"module":[37],"that":[38],"learns":[39],"complementary":[40],"features":[41],"while":[42],"preserving":[43],"through":[45],"selective":[46],"weighting.":[47],"The":[48],"model":[49,93],"is":[50],"evaluated":[51],"on":[52,75,84],"adverse":[53],"reaction":[54],"prediction":[55,63],"(MedDRA),":[56],"repurposing":[58],"(SCMFDD),":[59],"therapeutic":[61],"target":[62],"(TTD).":[64],"Evaluation":[65],"reveals":[66],"task-dependent":[67],"performance:":[68],"statistically":[70],"significant":[71],"23.1":[72],"%":[73],"improvement":[74],"MedDRA":[76],"against":[77],"the":[78,92,107],"strongest":[79],"baseline,":[80],"competitive":[82],"performance":[83],"SCMFDD":[85],"TTD.":[87],"Our":[88],"analysis":[89],"shows":[90],"how":[91],"adaptively":[94],"balances":[95],"interpretable,":[96],"predefined":[97],"atoms":[98],"learned":[100],"features,":[101],"providing":[102],"quantitative":[104],"look":[105],"at":[106],"performance-interpretability":[108],"spectrum":[109],"in":[110,125],"hybrid":[111],"models.":[112],"work":[114],"establishes":[115],"methodological":[117],"foundation":[118],"applying":[120],"evaluating":[122],"fuzzy-neural":[123],"systems":[124],"contexts.":[127]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-01-30T00:00:00"}