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Predictive modeling for clinical trial completion: assessing the phase success - what-if scenario approach on the enrollment
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This thesis develops predictive models, HINT Basic and HINT Plus, to forecast clinical trial phase outcomes. Integrating multimodal data and advanced machine learning techniques, these models evaluate the impact of variables like enrollment on trial success, and include what-if analyses to assess potential changes in trial parameters. The findings demonstrate how predictive analytics can enhance decision-making, optimize resource allocation, and expedite drug development, thereby improving clinical trial efficiency and supporting the broader goal of advancing healthcare outcomes.
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Clinical trials Health Care Artificial Intelligence Machine learning methods Predictive modeling What-if Analysis