{"id":"https://openalex.org/W4388674332","doi":"https://doi.org/10.1109/bhi58575.2023.10313362","title":"SiaKey: A Method for Improving Few-shot Learning with Clinical Domain Information","display_name":"SiaKey: A Method for Improving Few-shot Learning with Clinical Domain Information","publication_year":2023,"publication_date":"2023-10-15","ids":{"openalex":"https://openalex.org/W4388674332","doi":"https://doi.org/10.1109/bhi58575.2023.10313362"},"language":"en","primary_location":{"id":"doi:10.1109/bhi58575.2023.10313362","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bhi58575.2023.10313362","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)","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/A5028914229","display_name":"Zhuochun Li","orcid":null},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zhuochun Li","raw_affiliation_strings":["University of Pittsburgh,School of Computing and Information,Pittsburgh,USA","School of Computing and Information, University of Pittsburgh, Pittsburgh, USA"],"affiliations":[{"raw_affiliation_string":"University of Pittsburgh,School of Computing and Information,Pittsburgh,USA","institution_ids":["https://openalex.org/I170201317"]},{"raw_affiliation_string":"School of Computing and Information, University of Pittsburgh, Pittsburgh, USA","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038948994","display_name":"Khushboo Thaker","orcid":"https://orcid.org/0000-0003-3619-9376"},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Khushboo Thaker","raw_affiliation_strings":["University of Pittsburgh,School of Computing and Information,Pittsburgh,USA","School of Computing and Information, University of Pittsburgh, Pittsburgh, USA"],"affiliations":[{"raw_affiliation_string":"University of Pittsburgh,School of Computing and Information,Pittsburgh,USA","institution_ids":["https://openalex.org/I170201317"]},{"raw_affiliation_string":"School of Computing and Information, University of Pittsburgh, Pittsburgh, USA","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026188630","display_name":"Daqing He","orcid":"https://orcid.org/0000-0002-4645-8696"},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daqing He","raw_affiliation_strings":["University of Pittsburgh,School of Computing and Information,Pittsburgh,USA","School of Computing and Information, University of Pittsburgh, Pittsburgh, USA"],"affiliations":[{"raw_affiliation_string":"University of Pittsburgh,School of Computing and Information,Pittsburgh,USA","institution_ids":["https://openalex.org/I170201317"]},{"raw_affiliation_string":"School of Computing and Information, University of Pittsburgh, Pittsburgh, USA","institution_ids":["https://openalex.org/I170201317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5028914229"],"corresponding_institution_ids":["https://openalex.org/I170201317"],"apc_list":null,"apc_paid":null,"fwci":0.3497,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.66621826,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9990000128746033,"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":0.9990000128746033,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9990000128746033,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9968000054359436,"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":0.8689117431640625},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6759058237075806},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6507590413093567},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5432480573654175},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.5428680181503296},{"id":"https://openalex.org/keywords/health-records","display_name":"Health records","score":0.5375807881355286},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5115686058998108},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.506030261516571},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.49100640416145325},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.45993566513061523},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.45170557498931885},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4339669346809387},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.426126629114151},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.10984373092651367},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.10218197107315063}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8689117431640625},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6759058237075806},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6507590413093567},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5432480573654175},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.5428680181503296},{"id":"https://openalex.org/C3019952477","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Health records","level":3,"score":0.5375807881355286},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5115686058998108},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.506030261516571},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.49100640416145325},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.45993566513061523},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.45170557498931885},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4339669346809387},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.426126629114151},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.10984373092651367},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.10218197107315063},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bhi58575.2023.10313362","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bhi58575.2023.10313362","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.7400000095367432,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W2131571251","https://openalex.org/W2168041406","https://openalex.org/W2250539671","https://openalex.org/W2338784100","https://openalex.org/W2396881363","https://openalex.org/W2890179025","https://openalex.org/W2891177506","https://openalex.org/W2896457183","https://openalex.org/W2911489562","https://openalex.org/W2913969639","https://openalex.org/W2914431807","https://openalex.org/W2963716420","https://openalex.org/W2985884876","https://openalex.org/W2989464093","https://openalex.org/W3011574394","https://openalex.org/W3034942609","https://openalex.org/W3035204084","https://openalex.org/W3088409176","https://openalex.org/W3136730859","https://openalex.org/W3215098936","https://openalex.org/W4205511145","https://openalex.org/W4206313681","https://openalex.org/W4214607237","https://openalex.org/W4250526791","https://openalex.org/W4288089799","https://openalex.org/W4366761428","https://openalex.org/W4385163750","https://openalex.org/W6679713772","https://openalex.org/W6755207826","https://openalex.org/W6769627184","https://openalex.org/W6783596713","https://openalex.org/W6785847369"],"related_works":["https://openalex.org/W3170999895","https://openalex.org/W4389374014","https://openalex.org/W4312414840","https://openalex.org/W4287117940","https://openalex.org/W34092691","https://openalex.org/W2131513867","https://openalex.org/W4389989304","https://openalex.org/W2036486025","https://openalex.org/W2952937263","https://openalex.org/W4206276646"],"abstract_inverted_index":{"Supervised":[0],"Natural":[1],"Language":[2,57],"Processing":[3],"(NLP)":[4],"models":[5],"can":[6,23,79],"achieve":[7],"high":[8],"accuracy,":[9],"but":[10],"they":[11,78],"often":[12],"require":[13],"a":[14,74,86,97,130],"significant":[15],"amount":[16,88,132],"of":[17,89,117,133,178],"annotated":[18],"data":[19],"for":[20,32,114],"training,":[21],"which":[22,103],"be":[24],"expensive":[25],"and":[26,43,107,111,150,168],"time-consuming.":[27],"This":[28,121],"is":[29,123],"especially":[30],"true":[31],"clinical":[33,49,82],"NLP,":[34],"where":[35],"annotations":[36],"on":[37,171,195],"large-scale":[38],"electronic":[39],"health":[40],"records":[41],"(EHRs)":[42],"online":[44,118,126,173],"posts":[45,127],"necessitate":[46],"specialists":[47],"with":[48,84,164],"expertise.":[50],"On":[51],"the":[52,115,154],"other":[53],"hand,":[54],"fine-tuning":[55],"Pretrained":[56],"Models":[58],"(PLMs)":[59],"may":[60],"yield":[61],"poor":[62],"performance":[63,191],"due":[64],"to":[65,137,193],"limited":[66],"training":[67],"data.":[68,91],"Few-Shot":[69],"Learning":[70],"(FSL)":[71],"methods":[72],"offer":[73],"promising":[75],"solution":[76],"as":[77],"significantly":[80],"improve":[81],"NLP":[83],"only":[85],"small":[87],"labeled":[90],"In":[92],"this":[93],"paper,":[94],"we":[95,146,161],"introduce":[96],"novel":[98],"FSL":[99,197],"technique":[100],"named":[101],"SiaKey,":[102],"utilizes":[104],"Siamese":[105],"Networks":[106],"integrates":[108],"Keyphrases":[109,142],"Extraction":[110],"Domain":[112],"Knowledge,":[113],"task":[116,122],"post":[119],"classification.":[120],"challenging":[124],"since":[125],"typically":[128],"contain":[129],"greater":[131],"irrelevant":[134],"information":[135,149],"compared":[136,192],"traditional":[138],"EHRs.":[139],"By":[140],"incorporating":[141],"using":[143],"domain":[144],"knowledge,":[145],"extract":[147],"essential":[148],"reduce":[151],"distractions,":[152],"enhancing":[153],"classification":[155],"process.":[156],"To":[157],"evaluate":[158],"SiaKey\u2019s":[159,182],"performance,":[160],"conducted":[162],"tests":[163],"5,":[165],"10,":[166],"15,":[167],"20-shot":[169],"learning":[170],"health-related":[172],"post-classification":[174],"tasks.":[175,198],"The":[176],"results":[177],"our":[179],"experiments":[180],"demonstrate":[181],"effectiveness":[183],"in":[184],"capturing":[185],"text":[186],"features,":[187],"showcasing":[188],"its":[189],"superior":[190],"BioBERT":[194],"similar":[196]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2025-12-19T19:40:27.379048","created_date":"2025-10-10T00:00:00"}