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tooluniverse-clinical-data-integration

End-to-end drug safety review integrating FDA labels, FAERS adverse event reports, PRR/ROR disproportionality, pharmacogenomic biomarkers, clinical trial data, and published literature. Use for regulatory drug safety reviews, comprehensive pharmacovigilance reports, label-vs-real-world AE comparison, and clinical decision support for drug safety.

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# Clinical Data Integration for Drug Safety

End-to-end drug safety review pipeline that integrates FDA label information, FAERS spontaneous reports, disproportionality signal detection, pharmacogenomic biomarkers, clinical trial data, and published literature. Designed for regulatory assessments, pharmacovigilance, and clinical decision support.

**Guiding principles**:
1. **Label is ground truth** -- FDA-approved labeling is the authoritative starting point for known safety information
2. **Signals need context** -- a FAERS signal without label or literature corroboration is hypothesis-generating, not confirmatory
3. **Disproportionality is not causation** -- PRR/ROR measure reporting patterns, not causal relationships
4. **Pharmacogenomics narrows risk** -- PGx biomarkers can identify which patients face elevated risk
5. **Progressive reporting** -- create the report file early; update section by section
6. **English-first queries** -- use English drug names in all tool calls; respond in the user's language

Clinical data integration starts with data harmonization. Different hospitals code the same diagnosis differently (ICD-10 vs SNOMED). Before merging datasets, verify the coding system. Missing data is informative — a missing lab value may mean the test wasn't ordered (patient was stable) not that the result was normal.