{"id":"https://openalex.org/W4416034979","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.396","title":"LIFTED: Multimodal Clinical Trial Outcome Prediction via Large Language Models and Mixture-of-Experts","display_name":"LIFTED: Multimodal Clinical Trial Outcome Prediction via Large Language Models and Mixture-of-Experts","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416034979","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.396"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.396","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.396","pdf_url":"https://aclanthology.org/2025.findings-emnlp.396.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2025","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.findings-emnlp.396.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101923965","display_name":"Wenhao Zheng","orcid":"https://orcid.org/0000-0001-8227-8820"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenhao Zheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Liaoyaqi Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liaoyaqi Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Dongshen Peng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dongshen Peng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100569815","display_name":"Hongxia Xu","orcid":"https://orcid.org/0009-0003-9933-3998"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hongxia Xu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012646445","display_name":"Yun Li","orcid":"https://orcid.org/0009-0007-5228-5406"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yun Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077961759","display_name":"Hongtu Zhu","orcid":"https://orcid.org/0000-0002-6781-2690"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hongtu Zhu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003226543","display_name":"Tianfan Fu","orcid":"https://orcid.org/0000-0002-5574-2541"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tianfan Fu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5051534896","display_name":"Huaxiu Yao","orcid":"https://orcid.org/0000-0002-8691-9629"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huaxiu Yao","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":null,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7503","last_page":"7517"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.46959999203681946,"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":0.46959999203681946,"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":0.16660000383853912,"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"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.10589999705553055,"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/clinical-trial","display_name":"Clinical trial","score":0.5059999823570251},{"id":"https://openalex.org/keywords/outcome","display_name":"Outcome (game theory)","score":0.4968000054359436},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.2754000127315521},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.2736999988555908},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.266400009393692}],"concepts":[{"id":"https://openalex.org/C535046627","wikidata":"https://www.wikidata.org/wiki/Q30612","display_name":"Clinical trial","level":2,"score":0.5059999823570251},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.4968000054359436},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.427700012922287},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40720000863075256},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3862000107765198},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.31520000100135803},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2928999960422516},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.2793000042438507},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C2779473830","wikidata":"https://www.wikidata.org/wiki/Q1540899","display_name":"MEDLINE","level":2,"score":0.257999986410141}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.findings-emnlp.396","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.396","pdf_url":"https://aclanthology.org/2025.findings-emnlp.396.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2025","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.396","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.396","pdf_url":"https://aclanthology.org/2025.findings-emnlp.396.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416034979.pdf","grobid_xml":"https://content.openalex.org/works/W4416034979.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Clinical":[0],"trials":[1],"are":[2],"pivotal":[3],"yet":[4],"costly":[5],"processes,":[6],"often":[7],"spanning":[8],"multiple":[9],"years":[10],"and":[11,23,53,137],"requiring":[12],"substantial":[13],"expenses,":[14],"motivating":[15],"predictive":[16],"models":[17],"to":[18,30,50,55],"identify":[19],"likely-to-fail":[20],"drugs":[21],"early":[22],"save":[24],"resources.Recent":[25],"approaches":[26],"leverage":[27],"deep":[28],"learning":[29],"integrate":[31],"multimodal":[32,67],"data":[33,74],"for":[34],"clinical":[35,116],"outcome":[36],"prediction;":[37],"however,":[38],"they":[39],"rely":[40],"heavily":[41],"on":[42],"manually":[43],"designed":[44],"modalityspecific":[45],"encoders,":[46],"limiting":[47],"their":[48],"adaptability":[49],"new":[51],"modalities":[52],"ability":[54],"effectively":[56],"share":[57],"information":[58],"across":[59,91,119],"modalities.To":[60],"address":[61],"these":[62,101],"challenges,":[63],"we":[64],"propose":[65],"a":[66],"mixtureof-experts":[68],"(LIFTED)":[69],"framework.Specifically,":[70],"LIFTED":[71,109,134],"transforms":[72],"modality-specific":[73],"into":[75],"natural":[76],"language":[77],"descriptions,":[78],"encoded":[79],"via":[80],"unified,":[81],"noise-resilient":[82],"encoders.A":[83],"sparse":[84],"Mixture-of-Experts":[85],"mechanism":[86],"then":[87],"identifies":[88],"shared":[89],"patterns":[90],"modalities,":[92],"extracting":[93],"consistent":[94],"representations.Finally,":[95],"another":[96],"mixture-of-experts":[97],"module":[98],"dynamically":[99],"integrates":[100],"modality":[102],"representations,":[103],"emphasizing":[104],"critical":[105],"information.Experiments":[106],"show":[107],"that":[108],"significantly":[110],"outperforms":[111],"baseline":[112],"methods":[113],"in":[114],"predicting":[115],"trial":[117],"outcomes":[118],"all":[120],"phases,":[121],"highlighting":[122],"the":[123],"effectiveness":[124],"of":[125],"our":[126],"proposed":[127],"approach.Step":[128],"I":[129],"Modality":[130],"preprocessing":[131],"Step":[132],"II":[133],"model":[135],"training":[136],"predictionSelect":[138]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-08T00:00:00"}