{"id":"https://openalex.org/W4414034840","doi":"https://doi.org/10.1145/3705328.3748022","title":"TreatRAG: A Framework for Personalized Treatment Recommendation","display_name":"TreatRAG: A Framework for Personalized Treatment Recommendation","publication_year":2025,"publication_date":"2025-09-06","ids":{"openalex":"https://openalex.org/W4414034840","doi":"https://doi.org/10.1145/3705328.3748022"},"language":"en","primary_location":{"id":"doi:10.1145/3705328.3748022","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3705328.3748022","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3705328.3748022","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth ACM Conference on Recommender Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3705328.3748022","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5063534489","display_name":"C. Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I184565670","display_name":"Georgetown University","ror":"https://ror.org/05vzafd60","country_code":"US","type":"education","lineage":["https://openalex.org/I184565670"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chao-Chin Liu","raw_affiliation_strings":["Georgetown University, Washington DC, USA"],"raw_orcid":"https://orcid.org/0009-0002-0960-2211","affiliations":[{"raw_affiliation_string":"Georgetown University, Washington DC, USA","institution_ids":["https://openalex.org/I184565670"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030183320","display_name":"Hao-Ren Yao","orcid":"https://orcid.org/0000-0002-7043-685X"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hao-Ren Yao","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, USA"],"raw_orcid":"https://orcid.org/0000-0002-7043-685X","affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102933179","display_name":"Der\u2010Chen Chang","orcid":"https://orcid.org/0000-0003-1581-1589"},"institutions":[{"id":"https://openalex.org/I184565670","display_name":"Georgetown University","ror":"https://ror.org/05vzafd60","country_code":"US","type":"education","lineage":["https://openalex.org/I184565670"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Der-Chen Chang","raw_affiliation_strings":["Georgetown University, Washington DC, USA"],"raw_orcid":"https://orcid.org/0000-0003-1581-1589","affiliations":[{"raw_affiliation_string":"Georgetown University, Washington DC, USA","institution_ids":["https://openalex.org/I184565670"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062591304","display_name":"Ophir Frieder","orcid":"https://orcid.org/0000-0001-5076-8171"},"institutions":[{"id":"https://openalex.org/I184565670","display_name":"Georgetown University","ror":"https://ror.org/05vzafd60","country_code":"US","type":"education","lineage":["https://openalex.org/I184565670"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ophir Frieder","raw_affiliation_strings":["Georgetown University, Washington DC, USA"],"raw_orcid":"https://orcid.org/0000-0001-5076-8171","affiliations":[{"raw_affiliation_string":"Georgetown University, Washington DC, USA","institution_ids":["https://openalex.org/I184565670"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5063534489"],"corresponding_institution_ids":["https://openalex.org/I184565670"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11863138,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"690","last_page":"695"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9987999796867371,"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":0.9987999796867371,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9987999796867371,"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":0.9980000257492065,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/automatic-summarization","display_name":"Automatic summarization","score":0.6168000102043152},{"id":"https://openalex.org/keywords/jaccard-index","display_name":"Jaccard index","score":0.6031000018119812},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5529999732971191},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4724999964237213},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.44830000400543213},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4447000026702881},{"id":"https://openalex.org/keywords/unified-medical-language-system","display_name":"Unified Medical Language System","score":0.3262999951839447}],"concepts":[{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.6168000102043152},{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.6031000018119812},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5738000273704529},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5529999732971191},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4724999964237213},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.44830000400543213},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4447000026702881},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34150001406669617},{"id":"https://openalex.org/C69505689","wikidata":"https://www.wikidata.org/wiki/Q455338","display_name":"Unified Medical Language System","level":2,"score":0.3262999951839447},{"id":"https://openalex.org/C105002631","wikidata":"https://www.wikidata.org/wiki/Q4833645","display_name":"Subject-matter expert","level":3,"score":0.319599986076355},{"id":"https://openalex.org/C2779473830","wikidata":"https://www.wikidata.org/wiki/Q1540899","display_name":"MEDLINE","level":2,"score":0.3160000145435333},{"id":"https://openalex.org/C107327155","wikidata":"https://www.wikidata.org/wiki/Q330268","display_name":"Decision support system","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C63527458","wikidata":"https://www.wikidata.org/wiki/Q5133829","display_name":"Clinical decision support system","level":3,"score":0.28690001368522644},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.2858999967575073},{"id":"https://openalex.org/C2779328685","wikidata":"https://www.wikidata.org/wiki/Q1475557","display_name":"Patient safety","level":3,"score":0.28279998898506165},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.26440000534057617},{"id":"https://openalex.org/C2982822547","wikidata":"https://www.wikidata.org/wiki/Q179661","display_name":"Medical treatment","level":2,"score":0.2538999915122986}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3705328.3748022","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3705328.3748022","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3705328.3748022","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth ACM Conference on Recommender Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3705328.3748022","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3705328.3748022","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3705328.3748022","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth ACM Conference on Recommender Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4414034840.pdf","grobid_xml":"https://content.openalex.org/works/W4414034840.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W2073924861","https://openalex.org/W2083062358","https://openalex.org/W2744140371","https://openalex.org/W2964068143","https://openalex.org/W2965570621","https://openalex.org/W2972339569","https://openalex.org/W2972541642","https://openalex.org/W3159197440","https://openalex.org/W3192905713","https://openalex.org/W4210389067","https://openalex.org/W4220894606","https://openalex.org/W4290927771","https://openalex.org/W4297253404","https://openalex.org/W4385381606","https://openalex.org/W4388022708","https://openalex.org/W4389519219","https://openalex.org/W4391826804","https://openalex.org/W4391940656","https://openalex.org/W4392004240","https://openalex.org/W4395067201","https://openalex.org/W4400524786","https://openalex.org/W4403754412","https://openalex.org/W4409167012","https://openalex.org/W6960062145"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4390273403","https://openalex.org/W4386781444","https://openalex.org/W2150182025","https://openalex.org/W3092950680","https://openalex.org/W3197542405","https://openalex.org/W2056712470","https://openalex.org/W3125580266"],"abstract_inverted_index":{"Medication":[0],"recommendation":[1,74],"is":[2],"a":[3,84,171],"critical":[4,85],"function":[5],"of":[6,48,181],"clinical":[7,25,79,90,121,184],"decision":[8,185],"support":[9],"systems,":[10],"directly":[11],"influencing":[12],"patient":[13,56,98],"safety":[14],"and":[15,30,52,92,110,134,154,174],"treatment":[16,38],"efficacy.While":[17],"large":[18],"language":[19],"models":[20],"(LLMs)":[21],"show":[22],"promise":[23],"in":[24,42,119,183],"tasks":[26],"such":[27],"as":[28],"summarization":[29],"question":[31],"answering,":[32],"their":[33,46],"ability":[34],"to":[35,45,54,71,115,142,147,152,158,177],"make":[36],"accurate":[37],"predictions":[39,118],"remains":[40],"limited,":[41],"part,":[43],"due":[44],"lack":[47],"specialized":[49],"medical":[50],"knowledge":[51],"exposure":[53],"real-world":[55],"data.We":[57],"introduce":[58],"TreatRAG,":[59],"an":[60],"interpretable,":[61],"model-agnostic":[62,168],"retrieval-augmented":[63],"generation":[64],"(RAG)":[65],"framework":[66,125,169],"aimed":[67],"at":[68],"early-stage":[69],"development":[70],"enhance":[72],"medication":[73],"accuracy":[75],"using":[76,105,130],"publicly":[77],"available":[78],"data;":[80],"thus,":[81],"TreatRAG":[82],"forms":[83],"foundational":[86],"step":[87],"toward":[88],"future":[89],"validation":[91],"domain":[93],"expert":[94],"involvement.TreatRAG":[95],"retrieves":[96],"similar":[97],"cases,":[99],"i.e.,":[100],"so":[101],"called":[102],"\"digital":[103],"twins\",":[104],"interpretable":[106,175],"N-gram":[107],"Jaccard":[108],"similarity":[109],"augments":[111],"the":[112,127,165,179],"input":[113],"prompt":[114],"ground":[116],"LLM":[117],"real":[120],"scenarios.We":[122],"evaluate":[123],"our":[124],"on":[126],"MIMIC-IV":[128],"dataset":[129],"BioGPT,":[131],"BioMistral,":[132],"Phi3,":[133],"Flan-T5.TreatRAG-enhanced":[135],"BioGPT":[136],"improves":[137],"its":[138],"F1-score":[139],"from":[140,145,150,156],"0.14":[141],"0.34,":[143],"BioMistral":[144],"0.22":[146],"0.54,":[148],"Phi-3":[149],"0.09":[151],"0.16,":[153],"Flan-T5":[155],"0.23":[157],"0.30,":[159],"while":[160],"also":[161],"lowering,":[162],"often":[163],"significantly,":[164],"hallucination":[166],"rate.Our":[167],"offers":[170],"flexible,":[172],"effective,":[173],"solution":[176],"advance":[178],"reliability":[180],"LLMs":[182],"support.":[186]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}