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Predictive modelling for clinical trial completion: assessing the phase success - a what-if scenario approach on the enrolment
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| 1.49 MB | Adobe PDF |
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Resumo(s)
This thesis develops predictive models, HINTBasic and HINTPlus, 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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Palavras-chave
Clinical trials Health Care Artificial Intelligence Machine learning methods Predictive modeling What-if Analysis