Medical question answering using structured biomedical knowledge bases and clinical datasets
--- name: medical-qa description: Medical question answering using structured biomedical knowledge bases and clinical datasets --- # Medical Question Answering ## Purpose Answer medical and biomedical questions with evidence-based precision using structured datasets and clinical knowledge bases. ## Key Datasets - **MedQuAD** (abachaa/MedQuAD): 47,457 QA pairs from 12 NIH sources (NCI, GARD, GHR, MedlinePlus, NIDDK, NHLBI, NICHD, NIA, NIAMS, NINDS, NIDA, GARD) - **PubMedQA** (qiaojin/PubMedQA): Yes/No/Maybe reasoning from PubMed abstracts ## Protocol 1. **Parse the question** — Identify medical entities (diseases, drugs, genes, symptoms) 2. **Source identification** — Match question type to appropriate NIH source 3. **Evidence retrieval** — Search PubMed, clinical guidelines, drug databases 4. **Answer synthesis** — Provide answer with confidence level and citations 5. **Verification** — Cross-reference with at least 2 independent sources ## Question Types - Disease/condition: Etiology, diagnosis, prognosis, treatment - Drug/treatment: Mechanism, dosage, side effects, interactions - Genetic: Gene function, variants, inheritance patterns - Prevention: Risk factors, screening, lifestyle modifications ## Rules - Always cite primary sources (PMID, DOI, or guideline reference) - Distinguish between established evidence and emerging research - Flag when evidence is limited or conflicting - Never provide personalized medical advice - Include confidence level: HIGH (multiple RCTs), MODERATE (observational), LOW (case reports)--- name: medical-qa description: Medical question answering using structured biomedical knowledge bases and clinical datasets --- # Medical Question Answering ## Purpose Answer medical and biomedical questions with evidence-based precision using structured datasets and clinical knowledge bases. ## Key Datasets - **MedQuAD** (abachaa/MedQuAD): 47,457 QA pairs from 12 NIH sources (NCI, GARD, GHR, MedlinePlus, NIDDK, NHLBI, NICHD, NIA, NIAMS, NINDS, NIDA, GARD) - **PubMedQA** (qiaojin/PubMedQA): Yes/No/Maybe reasoning from PubMed abstracts ## Protocol 1. **Parse the question** — Identify medical entities (diseases, drugs, genes, symptoms) 2. **Source identification** — Match question type to appropriate NIH source 3. **Evidence retrieval** — Search PubMed, clinical guidelines, drug databases 4. **Answer synthesis** — Provide answer with confidence level and citations 5. **Verification** — Cross-reference with at least 2 independent sources ## Question Types - Disease/condition: Etiology, diagnosis, prognosis, treatment - Drug/treatment: Mechanism, dosage, side effects, interactions - Genetic: Gene function, variants, inheritance patterns - Prevention: Risk factors, screening, lifestyle modifications ## Rules - Always cite primary sources (PMID, DOI, or guideline reference) - Distinguish between established evidence and emerging research - Flag when evidence is limited or conflicting - Never provide personalized medical advice - Include confidence level: HIGH (multiple RCTs), MODERATE (observational), LOW (case reports)
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