Aiming at the limitations of traditional medical decision system in
processing large-scale heterogeneous medical data and realizing highly
personalized recommendation, this paper introduces a personalized medical
decision algorithm utilizing graph neural network (GNN). This research
innovatively integrates graph neural network technology into the medical and
health field, aiming to build a high-precision representation model of patient
health status by mining the complex association between patients' clinical
characteristics, genetic information, living habits. In this study, medical
data is preprocessed to transform it into a graph structure, where nodes
represent different data entities (such as patients, diseases, genes, etc.) and
edges represent interactions or relationships between entities. The core of the
algorithm is to design a novel multi-scale fusion mechanism, combining the
historical medical records, physiological indicators and genetic
characteristics of patients, to dynamically adjust the attention allocation
strategy of the graph neural network, so as to achieve highly customized
analysis of individual cases. In the experimental part, this study selected
several publicly available medical data sets for validation, and the results
showed that compared with traditional machine learning methods and a single
graph neural network model, the proposed personalized medical decision
algorithm showed significantly superior performance in terms of disease
prediction accuracy, treatment effect evaluation and patient risk
stratification.

利用图神经网络（GNN）算法，该研究在医学和健康领域创新性地整合了图神经网络技术，并通过挖掘患者的临床特征、基因信息和生活习惯之间的复杂关联，构建了高精度的患者健康状况表示模型。实验证实，相比传统机器学习方法和单一图神经网络模型，该个性化医学决策算法在疾病预测准确性、治疗效果评估和患者风险分层等方面表现出显著优越性。