2025/04/25 by Dingxin Lu, Lu, Dingxin, Shurui Wu +3 · 6 citations
Computer Science · Health Professions · #Advanced Technologies in Various Fields #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare
paper · pdf · doi:10.48550/arxiv.2504.18631
openalex publication_date 2025/04/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With the timely formation of personalized intervention plans based on high-dimensional heterogeneous time series information becoming an important challenge 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 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, we adaptively balance individual and group gains. To improve the robustness and interpretability of decision-making, a multi-layer neural network structure is employed to group-code patient characteristics. Second, for the rapid multi-modal fusion of multi-source heterogeneous time series, a multi-channel neural network combined with a self-attention mechanism is used for dynamic feature extraction. Key feature screening and aggregation are achieved through a differentiable gating network. Finally, a collaborative search process combining a genetic algorithm and Monte Carlo tree search is proposed to find the ideal intervention strategy, achieving global optimization. Experimental results show significant improvements in accuracy, coverage, and decision-making benefits compared with existing methods.