QTL – ML Pattern Recognition

Context: Global pharmaceutical company with 2,000+ clinical studies requiring advanced pattern recognition to support regulatory compliance and patient safety.

Challenge: Manual review of adverse event (AE) reports was slow and could miss subtle patterns. Needed ML models to automatically detect unusual patterns in AE reporting for early fraud detection and regulatory submission quality.

Solution: Developed and validated ML-based models for pattern recognition in adverse event reporting:

• Applied classification and clustering techniques to identify unusual AE distributions
• Integrated with QTL framework to flag potential data fabrication
• Validated models against historical data to ensure regulatory‑grade accuracy

Outcome: Provided a scalable, automated solution for AE pattern recognition, directly contributing to regulatory compliance and clinical safety insights.