  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.
