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Machine Learning Approach to Predicting Patient Enrollment, Managing Risk & Accelerating Timelines in Study Feasibility
Failure to enroll patients is one of the leading causes of clinical trial delays. As life science organizations adopt more data-driven approaches to determining trial feasibility and seek to improve operational performance, the use of machine learning and predictive models can accelerate cycle times, improve site selection, provide more accurate enrollment forecasts to base decisions on, and reduce manual effort involved in scenario modelling.
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