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AI for Drug Adverse Effect Detection
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== <span style="color: #FFFFFF;">Creating</span> == Building a pharmacovigilance AI system: (1) Data: download FAERS quarterly files + EHR data if available + social media stream. (2) NLP pipeline: extract drug-ADE pairs from all text sources; MedDRA code mapping; deduplicate. (3) Signal detection: apply disproportionality (ROR, PRR) weekly on FAERS; SCCS on EHR data quarterly. (4) Alert ranking: prioritize signals by: strength (ROR + 95% CI), clinical seriousness (death, hospitalization), novelty (not in current label). (5) Signal validation: pharmacist/pharmacologist reviews top signals; literature search; regulatory decision. (6) Reporting: submit validated new signals to FDA MedWatch; update internal drug safety database. [[Category:Artificial Intelligence]] [[Category:Pharmacovigilance]] [[Category:Drug Safety]] </div>
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