Safety Signal Detection Using AI- From Strategy to Implementation

Music City Center 

Our novel drug development method addresses patient adverse reactions using clinical trial data to explore undetected safety signals. Unlike other models, our technique confirms signals through expert opinion, literature search, and trial exploration, enhancing accuracy and scalability.

Our AI system employs an APRIORI (unsupervised) model in association rule mining, identifying associations between events using support, confidence, lift, Fisher exact test, and correlation.

The model requires high-quality patient-level adverse event data, minimizing bias and balancing demographics. Compliance with HIPAA, anonymization, secure storage, encryption, and ethical standards are crucial.

Pooled breast cancer study data will be used to discover and cross-check signals across therapeutic areas, ensuring algorithm reliability. This tool benefits the pharmaceutical industry by enabling early signal detection, reducing safety physicians' workload, and improving data integrity.

Implementing this safety signal detection system has regulatory implications, including stricter reporting, audits, streamlined processes, and timely safety assessment and enhances pharmacovigilance process.

Safety Signal

Pharmacovigilance

ChatGPT

Association Rule Mining

Generative AI

Artificial Intelligence 

Section on Statistical Learning and Data Science