Pharmacovigilance
AI-Powered Drug Safety & Adverse Event Detection
We harness the power of artificial intelligence to revolutionize drug safety monitoring and adverse event detection across the entire pharmaceutical lifecycle. Our deep learning systems employ recurrent neural networks and attention mechanisms to process electronic health records, spontaneous reporting databases, and real-world evidence from diverse sources including social media and wearable devices.
We have developed advanced natural language processing models specifically trained on medical terminology to extract adverse drug reactions from unstructured clinical narratives with high sensitivity and specificity. Our ensemble learning approaches combine multiple AI models to detect rare safety signals that traditional statistical methods might miss, while our temporal pattern recognition algorithms identify delayed or cumulative drug effects.
Our Capabilities
Adverse Event Detection
Real-time monitoring of spontaneous reporting databases, EHRs, and social media for early safety signal detection.
Natural Language Processing
Medical NLP models extract adverse reactions from unstructured clinical notes with high accuracy.
Causal Inference
Advanced algorithms establish drug-event causality relationships from observational data.
Signal Prioritization
Machine learning models rank and prioritize safety signals based on clinical significance and severity.
Temporal Pattern Analysis
Algorithms detect delayed or cumulative adverse effects that may not be apparent in short-term studies.
Explainable AI
Interpretable models provide transparent insights into drug-event relationships for regulatory decisions.
Featured Research
Losartan 50mg Adverse Effects Analysis
Multi-modal machine learning analysis of Losartan adverse effects using FDA FAERS data. This comprehensive study employs ensemble methods to identify high-risk adverse events and predict severity outcomes.
Key Findings: Identified top 10 high-risk adverse events ranked by propensity score, achieving 75% accuracy with ensemble voting methods.
View Full Analysis →Regulatory Excellence
Through causal inference networks and explainable AI frameworks, we not only flag potential safety concerns but also provide interpretable insights into drug-event relationships, enabling regulatory agencies and pharmaceutical companies to make faster, more informed decisions that protect patient safety.