Research on Personalized Medical Intervention Strategy Generation System based on Group Relative Policy Optimization and Time-Series Data Fusion

With the timely formation of personalized intervention plans based on high-dimensional heterogeneous time series information has become an important challenge in the medical field today . As electronic medical records, wearables and other multi-source medical data are increasingly generated and diversified. In this work, we develop a system to generate personalized medical intervention strategies based on Group Relative Policy Optimization (GRPO) and Time-Series Data Fusion: First by incorporating relative policy constraints among the groups during policy gradient updates adaptive balance the individual gain and group gain distribution. To improve the robustness and interpretability of decision-making, the multi-layer neural network structure was employed to group code the patient characteristics. Secondly, for the rapid multi-modal fusion of multi -source heterogeneous time series, a multi -channel neural network combined with self -attention mechanism was employed for dynamic feature extraction, the key feature screening and aggregation were further achieved through the differentiable gating network. Finally, a collaborative search process was proposed to find the ideal candidate intervention strategy based on the combination of genetic algorithm and Monte Carlo tree search so that a global optimization of the candidate intervention strategies was achieved, which greatly enhanced the accuracy of the system as well a s the system's personalization level. The experimental results show that model achieves significant improvement in aspects of accuracy, coverage and decision -making benefits of intervention effect compared with existing methods.