Optimization of Clinical Trial Strategies for Anti-HER2 Drugs Based on Bayesian Optimization and Deep Learning

The optimization of clinical trial strategies for anti-HER2 drugs is imperative for the acceleration of the development of effective cancer therapeutics. This study proposes a sophisticated framework that integrates Bayesian optimization and deep learning to enhance the design and execution of clinical trials of anti-HER2 drugs. Utilizing established Bayesian models, the proposed approach integrates deep neural networks to model complex patient response dynamics and to fuse diverse clinical and genomic data. The study introduces a novel hybrid acquisition function that enables adaptive experimental design and real-time decision-making by leveraging the uncertainty quantization of Bayesian optimization and the feature extraction capability of deep learning. Furthermore, the model employs multi-task learning to simultaneously optimize multiple trial objectives, such as efficacy, safety, and cost-effectiveness, thus providing a comprehensive optimization strategy. Ensuring the robustness and preventing overfitting in high-dimensional data environments is achieved by employing ensemble methods and regularization techniques. The experimental results demonstrated that the optimized trial strategy presented in this study enhanced the prediction accuracy of treatment outcomes by 25% in comparison with conventional trial design methods.