The invention relates to the technical field of
drug safety evaluation, in particular to a multi-center combined evaluation method and
system for
drug safety based on a graph neural network and
federated learning. Known and unknown
drug interaction is systematically predicted based on a drug multi-relation
knowledge graph and a graph neural network, a key path of DDI is identified through a graph attention mechanism, a
molecular mechanism of the interaction is revealed, and
natural language interpretation is generated. And meanwhile, through
privacy protection and multi-center cooperation, a federal
learning architecture is utilized to break data islands and improve the external effectiveness of an
evaluation conclusion on the premise of protecting patient privacy and meeting data compliance requirements. The heterogeneity of data of different mechanisms is effectively evaluated through distribution deviation detection, deviation caused by blind extrapolation is avoided, a real-world evidence methodology report and a data
traceability auditing clue are automatically generated, the requirement of a supervision mechanism for real-world evidence quality is met,
medicine supervision decision is supported, and
medicine research, development and review are accelerated.