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Predictive modeling for clinical trial completion: assessing the phase success: enhancing trial predictions through uncertainty quantification
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| 2.53 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
This study investigates predictive modeling of clinical trial completion using the HINTBasic
and HINTPlus models. By integrating multimodal datasets, the models predict clinical trial
phase success. It provides interpretability insights into the HINTPlus model's decision-making
process. To enhance reliability, a selective classification technique addresses uncertainty
quantification. Our findings support informed decision-making, optimize resource allocation,
and accelerate drug development in clinical trials.
Descrição
Palavras-chave
Clinical trials Health Care Artificial Intelligence Machine learning methods Predictive modeling Model interpretability Selective classification