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Predictive modeling for clinical trial completion: assessing the phase success: enhancing trial predictions through uncertainty quantification

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Resumo: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.
Autores principais:Rossi, Ginevra
Assunto:Clinical trials Health Care Artificial Intelligence Machine learning methods Predictive modeling Model interpretability Selective classification
Ano:2025
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso aberto
Instituição associada:Universidade Nova de Lisboa
Idioma:inglês
Origem:Repositório Institucional da UNL

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