How Machine Learning Drives Clinical Trial Efficiency
Clinical trial data management is increasingly challenging as studies grow in complexity. Quickly accessing and analyzing study data is vital for assessing trial progress and patient safety.
In this paper, we explore real-time data access and analysis for proactive study management. We investigate using adverse event (AE) data to monitor safety and discuss a clinical analytics platform that supports collaboration and data review workflows.
Additionally, we demonstrate a new Machine-Learning (ML) algorithm using bootstrapping to predict site performance based on AE data. This aids in identifying underperforming sites, enabling sponsors to take timely action for optimal performance and potential trial design improvements.
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