Long, B.; Lai, S.-W.; Wu, J.; Bellur, S. Predicting Phase 1 Lymphoma Clinical Trial Durations Using Machine Learning: An In-Depth Analysis and Broad Application Insights. Clin. Pract.2024, 14, 69-88.
Long, B.; Lai, S.-W.; Wu, J.; Bellur, S. Predicting Phase 1 Lymphoma Clinical Trial Durations Using Machine Learning: An In-Depth Analysis and Broad Application Insights. Clin. Pract. 2024, 14, 69-88.
Long, B.; Lai, S.-W.; Wu, J.; Bellur, S. Predicting Phase 1 Lymphoma Clinical Trial Durations Using Machine Learning: An In-Depth Analysis and Broad Application Insights. Clin. Pract.2024, 14, 69-88.
Long, B.; Lai, S.-W.; Wu, J.; Bellur, S. Predicting Phase 1 Lymphoma Clinical Trial Durations Using Machine Learning: An In-Depth Analysis and Broad Application Insights. Clin. Pract. 2024, 14, 69-88.
Abstract
Lymphoma diagnoses in the U.S. are substantial, with an estimated 89,380 new cases in 2023, necessitating innovative treatment approaches. Phase 1 clinical trials play a pivotal role in this context. We developed a binary predictive model to assess trial adherence to expected average durations, analyzing 1,089 completed Phase 1 lymphoma trials from clinicaltrials.gov. Using machine learning, the Random Forest model demonstrated high efficacy with an accuracy of 0.7248 and ROC-AUC of 0.7701 for lymphoma trials. Importantly, this model maintained an accuracy of 0.7405 when applied to lung cancer trials, showcasing its versatility. A key insight is the correlation between higher predicted probabilities and extended trial durations, offering nuanced insights beyond binary predictions. Our research contributes to enhanced clinical research planning and potential improvements in patient outcomes in oncology.
Public Health and Healthcare, Public Health and Health Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
