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Predictive modelling for clinical trial completion: assessing the phase success – incorporating missing value imputation

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Detalhes bibliográficos
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. Imputation methods are incorporated to improve data consistency and model performance.
Autores principais:Cheng, Júlia Zhou
Assunto:Clinical trials Health Care Artificial Intelligence Machine Learning Methods Predictive modeling Missing value imputation
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
Descrição
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. Imputation methods are incorporated to improve data consistency and model performance.