Clinical Trial Monitoring Through Advanced Machine Learning and Deep Learning

Music City Center 

Traditional clinical trial monitoring relies heavily on manual site visits and source data verification (SDV), making the process both time-consuming and costly. We introduce a machine learning (ML) and deep learning (DL) enabled system that automates real-time risk assessment based on risk-based monitoring (RBM) and quality tolerance limits (QTL) principles. By integrating high-dimensional, diverse data streams and types, our approach proactively identifies and predicts potential issues that could compromise patient safety and data integrity. Unlike static, threshold-based methods, our models adapt dynamically to accumulating trial data, reducing manual SDV and streamlining resource allocation. Preliminary findings suggest accelerated detection of high-risk sites and data anomalies, improved patient safety outcomes, and significant operational efficiencies.

Clinical trial monitoring

Quality tolerance limits

Machine learning

Deep learning

Patient safety

Data integrity 

Biopharmaceutical Section