{"id":"https://openalex.org/W7131677060","doi":"https://doi.org/10.48550/arxiv.2602.21216","title":"EQ-5D Classification Using Biomedical Entity-Enriched Pre-trained Language Models and Multiple Instance Learning","display_name":"EQ-5D Classification Using Biomedical Entity-Enriched Pre-trained Language Models and Multiple Instance Learning","publication_year":2026,"publication_date":"2026-01-30","ids":{"openalex":"https://openalex.org/W7131677060","doi":"https://doi.org/10.48550/arxiv.2602.21216"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.21216","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126935177","display_name":"Zhyar Rzgar K Rostam","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rostam, Zhyar Rzgar K","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126887962","display_name":"G\u00e1bor Kert\u00e9sz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kert\u00e9sz, G\u00e1bor","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22148516,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.26179999113082886,"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":0.26179999113082886,"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/T10804","display_name":"Health Systems, Economic Evaluations, Quality of Life","score":0.2614000141620636,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.07069999724626541,"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/language-model","display_name":"Language model","score":0.6581000089645386},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.5665000081062317},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5464000105857849},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5285999774932861},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.4778999984264374},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.4528000056743622},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.42399999499320984},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.41850000619888306}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.734499990940094},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.6581000089645386},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6460000276565552},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6297000050544739},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.5665000081062317},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5464000105857849},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5285999774932861},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.4778999984264374},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46880000829696655},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.4528000056743622},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.42399999499320984},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.41850000619888306},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.36039999127388},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.33059999346733093},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.30730000138282776},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.29840001463890076},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.29750001430511475},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.29280000925064087},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C189708586","wikidata":"https://www.wikidata.org/wiki/Q1504425","display_name":"Systematic review","level":3,"score":0.2824999988079071},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25850000977516174}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.21216","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.21216","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.21216","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.21216","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.8098596334457397}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,139],"EQ-5D":[1,79],"(EuroQol":[2],"5-Dimensions)":[3],"is":[4,43,129],"a":[5,110,132],"standardized":[6],"instrument":[7],"for":[8,74],"the":[9,25,32,102,153],"evaluation":[10],"of":[11,14,28,37,40,54,134],"health-related":[12],"quality":[13],"life.":[15],"In":[16,48],"health":[17],"economics,":[18],"systematic":[19,184],"literature":[20,42],"reviews":[21],"(SLRs)":[22],"depend":[23],"on":[24],"correct":[26],"identification":[27],"publications":[29],"that":[30,168],"use":[31],"EQ-5D,":[33],"but":[34],"manual":[35],"screening":[36,182],"large":[38],"volumes":[39],"scientific":[41],"time-consuming,":[44],"error-prone,":[45],"and":[46,57,96,104,148,160,175],"inconsistent.":[47],"this":[49],"study,":[50],"we":[51,108],"investigate":[52],"fine-tuning":[53],"general-purpose":[55],"(BERT)":[56],"domain-specific":[58],"(SciBERT,":[59],"BioBERT)":[60],"pre-trained":[61],"language":[62],"models":[63,73,92],"(PLMs),":[64],"enriched":[65,135],"with":[66,93,116],"biomedical":[67],"entity":[68,169],"information":[69,122],"extracted":[70],"through":[71],"scispaCy":[72,91],"each":[75,127],"statement,":[76],"to":[77,119],"improve":[78],"detection":[80],"from":[81],"abstracts.":[82],"We":[83],"conduct":[84],"nine":[85],"experimental":[86],"setups,":[87],"including":[88],"combining":[89],"three":[90,94],"PLMs,":[95],"evaluate":[97],"their":[98],"performance":[99],"at":[100,152],"both":[101],"sentence":[103],"study":[105],"levels.":[106],"Furthermore,":[107],"explore":[109],"Multiple":[111],"Instance":[112],"Learning":[113],"(MIL)":[114],"approach":[115],"attention":[117],"pooling":[118],"aggregate":[120],"sentence-level":[121],"into":[123],"study-level":[124],"predictions,":[125],"where":[126],"abstract":[128],"represented":[130],"as":[131],"bag":[133],"sentences":[136],"(by":[137],"scispaCy).":[138],"findings":[140],"indicate":[141],"consistent":[142],"improvements":[143],"in":[144,183],"F1-scores":[145],"(reaching":[146],"0.82)":[147],"nearly":[149],"perfect":[150],"recall":[151],"study-level,":[154],"significantly":[155,171],"exceeding":[156],"classical":[157],"bag-of-words":[158],"baselines":[159],"recently":[161],"reported":[162],"PLM":[163],"baselines.":[164],"These":[165],"results":[166],"show":[167],"enrichment":[170],"improves":[172],"domain":[173],"adaptation":[174],"model":[176],"generalization,":[177],"enabling":[178],"more":[179],"accurate":[180],"automated":[181],"reviews.":[185]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-02-27T00:00:00"}