Publicação
Predictive modelling for clinical trial completion: assessing the phase success - a what-if scenario approach on the enrolment
| Resumo: | 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. |
|---|---|
| Autores principais: | Favita, Sara Sofia Almeida |
| Assunto: | Clinical trials Health Care Artificial Intelligence Machine learning methods Predictive modeling What-if Analysis |
| 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 |
| Resumo: | 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. |
|---|