{"id":"https://openalex.org/W4416144259","doi":"https://doi.org/10.1145/3721201.3725434","title":"Extracting Causal Relational Rules for Medical Question-Answering Tasks using Large Language Models","display_name":"Extracting Causal Relational Rules for Medical Question-Answering Tasks using Large Language Models","publication_year":2025,"publication_date":"2025-06-24","ids":{"openalex":"https://openalex.org/W4416144259","doi":"https://doi.org/10.1145/3721201.3725434"},"language":null,"primary_location":{"id":"doi:10.1145/3721201.3725434","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3721201.3725434","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3721201.3725434","source":null,"license":"cc-by-nd","license_id":"https://openalex.org/licenses/cc-by-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies","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/10.1145/3721201.3725434","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050831323","display_name":"Md. Sohanur Rahman","orcid":"https://orcid.org/0009-0004-1976-9909"},"institutions":[{"id":"https://openalex.org/I45438204","display_name":"The University of Texas at San Antonio","ror":"https://ror.org/01kd65564","country_code":"US","type":"education","lineage":["https://openalex.org/I45438204"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Md Sohanur Rahman","raw_affiliation_strings":["University of Texas at San Antonio, San Antonio, TX, USA"],"affiliations":[{"raw_affiliation_string":"University of Texas at San Antonio, San Antonio, TX, USA","institution_ids":["https://openalex.org/I45438204"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062592273","display_name":"Y Zhang","orcid":"https://orcid.org/0000-0002-3811-3284"},"institutions":[{"id":"https://openalex.org/I45438204","display_name":"The University of Texas at San Antonio","ror":"https://ror.org/01kd65564","country_code":"US","type":"education","lineage":["https://openalex.org/I45438204"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuexia Zhang","raw_affiliation_strings":["University of Texas at San Antonio, San Antonio, TX, USA"],"affiliations":[{"raw_affiliation_string":"University of Texas at San Antonio, San Antonio, TX, USA","institution_ids":["https://openalex.org/I45438204"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018773113","display_name":"Anthony Rios","orcid":"https://orcid.org/0000-0003-1781-3975"},"institutions":[{"id":"https://openalex.org/I45438204","display_name":"The University of Texas at San Antonio","ror":"https://ror.org/01kd65564","country_code":"US","type":"education","lineage":["https://openalex.org/I45438204"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anthony Rios","raw_affiliation_strings":["University of Texas at San Antonio, San Antonio, TX, USA"],"affiliations":[{"raw_affiliation_string":"University of Texas at San Antonio, San Antonio, TX, USA","institution_ids":["https://openalex.org/I45438204"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061285256","display_name":"Ke Yang","orcid":"https://orcid.org/0000-0002-1617-5986"},"institutions":[{"id":"https://openalex.org/I45438204","display_name":"The University of Texas at San Antonio","ror":"https://ror.org/01kd65564","country_code":"US","type":"education","lineage":["https://openalex.org/I45438204"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ke Yang","raw_affiliation_strings":["University of Texas at San Antonio, San Antonio, TX, USA"],"affiliations":[{"raw_affiliation_string":"University of Texas at San Antonio, San Antonio, TX, USA","institution_ids":["https://openalex.org/I45438204"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5050831323"],"corresponding_institution_ids":["https://openalex.org/I45438204"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18621846,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"464","last_page":"469"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.7669000029563904,"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":0.7669000029563904,"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":0.05559999868273735,"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"}},{"id":"https://openalex.org/T13629","display_name":"Text Readability and Simplification","score":0.020400000736117363,"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/workflow","display_name":"Workflow","score":0.6784999966621399},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5192000269889832},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.3878999948501587},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.3709999918937683},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.365200012922287},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.3375999927520752}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7950000166893005},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.6784999966621399},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5551999807357788},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5192000269889832},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4869999885559082},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39469999074935913},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3878999948501587},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.3709999918937683},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.365200012922287},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3375999927520752},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32499998807907104},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.31929999589920044},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2921999990940094},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.2851000130176544},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.2827000021934509},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.27390000224113464}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3721201.3725434","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3721201.3725434","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3721201.3725434","source":null,"license":"cc-by-nd","license_id":"https://openalex.org/licenses/cc-by-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3721201.3725434","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3721201.3725434","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3721201.3725434","source":null,"license":"cc-by-nd","license_id":"https://openalex.org/licenses/cc-by-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320310677","display_name":"University of Texas at San Antonio","ror":"https://ror.org/01kd65564"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4416144259.pdf"},"referenced_works_count":8,"referenced_works":["https://openalex.org/W3160638507","https://openalex.org/W4307935822","https://openalex.org/W4308590518","https://openalex.org/W4323644324","https://openalex.org/W4385567149","https://openalex.org/W4404610986","https://openalex.org/W4404782447","https://openalex.org/W4405920904"],"related_works":[],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"have":[4,107],"demonstrated":[5],"remarkable":[6],"capabilities":[7],"in":[8,92],"understanding,":[9],"summarizing,":[10],"and":[11,111],"extracting":[12,93],"various":[13],"topics":[14],"from":[15,46],"free-form":[16],"text.":[17],"However,":[18],"most":[19],"domain-specific":[20],"extraction":[21],"using":[22],"LLMs":[23],"still":[24],"relies":[25],"on":[26,81],"an":[27],"annotated":[28],"corpus":[29],"that":[30,72,96],"is":[31],"often":[32],"expensive":[33],"to":[34,41,59,119],"achieve.":[35],"We":[36,66,106],"propose":[37],"a":[38,56,69,121],"prompting-based":[39],"framework":[40,53,91,110],"extract":[42],"causal":[43],"relational":[44],"rules":[45,79,95,115],"medical":[47],"question-answering":[48],"text":[49],"without":[50],"annotation.":[51],"Our":[52],"also":[54,67],"integrates":[55],"self-judging":[57],"workflow":[58],"enhance":[60,98],"the":[61,74,77,87,99,109],"quality":[62,75],"of":[63,76,89,101,124],"extracted":[64,78],"rules.":[65],"present":[68],"pilot":[70],"study":[71],"evaluates":[73],"based":[80],"human":[82],"labels.":[83],"The":[84],"results":[85],"demonstrate":[86],"efficacy":[88],"our":[90],"high-quality":[94],"can":[97],"performance":[100],"downstream":[102],"LLMs-driven":[103],"QA":[104],"tasks.":[105],"made":[108],"dataset":[112],"with":[113],"enhanced":[114],"available":[116],"as":[117],"open-source":[118],"encourage":[120],"wide":[122],"range":[123],"applications.":[125]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-11-12T00:00:00"}
