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