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With the timely formation of personalized intervention plans based onhigh-dimensional heterogeneous time series information becoming an importantchallenge in the medical field today, 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 interventionstrategies based on Group Relative Policy Optimization (GRPO) and Time-SeriesData Fusion. First, by incorporating relative policy constraints among thegroups during policy gradient updates, we adaptively balance individual andgroup gains. To improve the robustness and interpretability of decision-making,a multi-layer neural network structure is employed to group-code patientcharacteristics. Second, for the rapid multi-modal fusion of multi-sourceheterogeneous time series, a multi-channel neural network combined with aself-attention mechanism is used for dynamic feature extraction. Key featurescreening and aggregation are achieved through a differentiable gating network.Finally, a collaborative search process combining a genetic algorithm and MonteCarlo tree search is proposed to find the ideal intervention strategy,achieving global optimization. Experimental results show significantimprovements in accuracy, coverage, and decision-making benefits compared withexisting methods.

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