AI for Pharmacovigilance and Drug Safety Monitoring Fundamentals Training by Tonex
This 2-day intermediate-level training explores the integration of Artificial Intelligence (AI) and Natural Language Processing (NLP) into pharmacovigilance practices, streamlining drug safety monitoring, adverse event recognition, and compliance reporting. Designed to help professionals transition into data-driven safety surveillance, the course emphasizes practical use cases such as automated ICSR triaging and AI-powered signal detection. Cybersecurity plays a critical role in this domain, safeguarding sensitive patient data, preserving data integrity in real-world evidence pipelines, and protecting AI algorithms from manipulation. Participants will gain insights into secure, intelligent automation that meets both safety and compliance standards in the pharmaceutical sector.
Audience:
- Pharmacovigilance professionals
- Safety data analysts
- Clinical pharmacologists
- Regulatory compliance officers
- Data scientists in healthcare
- Cybersecurity professionals
Learning Objectives:
- Understand the role of AI/NLP in pharmacovigilance
- Learn techniques for automating adverse event detection
- Explore unsupervised methods for signal detection
- Apply AI tools for triaging and case assessment
- Integrate real-world evidence with ML pipelines
- Address cybersecurity and regulatory challenges
Course Modules:
Module 1: Introduction to AI in PV
- AI fundamentals for drug safety
- Evolution of pharmacovigilance systems
- Importance of data quality in AI
- Overview of AI-powered PV lifecycle
- Regulatory landscape and AI fit
- AI risk-benefit considerations
Module 2: Adverse Event Monitoring
- Literature-based signal scanning
- AI-driven medical record mining
- Adverse event ontology mapping
- Data ingestion and transformation
- NLP for keyword extraction
- Reducing false positives
Module 3: NLP for ICSR Triaging
- ICSR data structure basics
- Rule-based vs. AI-driven triage
- Sentiment and intent recognition
- Named entity recognition (NER)
- Auto-classification of seriousness
- Alert generation workflows
Module 4: Signal Detection Techniques
- Basics of unsupervised learning
- Clustering for novel signals
- Outlier analysis with AI
- Dimensionality reduction use cases
- Time-series event detection
- Post-hoc validation protocols
Module 5: AI in Risk and Compliance
- AI for risk mitigation planning
- Identifying compliance gaps
- Cross-border data governance
- Secure data handling protocols
- Algorithm auditability frameworks
- AI in FDA/EMA submissions
Module 6: Real-World Evidence Integration
- RWE sources and AI pipelines
- Data harmonization for ML models
- Longitudinal data analysis
- Bias mitigation in RWE
- Cybersecurity of patient datasets
- AI validation for real-world use
Enroll in AI for Pharmacovigilance and Drug Safety Monitoring Fundamentals Training by Tonex to elevate your pharmacovigilance strategy using secure, intelligent systems. Gain in-demand skills to automate, detect, and respond to drug safety issues while ensuring regulatory and cybersecurity compliance.