Enhancing healthcare analytics with prompt based probabilistic graphical model

Predicting clinical outcomes is essential for effective healthcare management. Electronic medical records (EMRs) contain rich temporal and relational structures, yet conventional models often struggle to capture these patterns with interpretability. This study proposes the prompt-based pre-trained graph model (PPGM), which combines graph neural networks with prompt learning in a two-stage framework: pre-training on patient graphs and fine-tuning with gated mechanisms for edges, nodes, and labels. By preserving the intrinsic relationships in EMRs, PPGM improves accuracy in predicting outcomes such as mortality and length of stay, while enabling transparent, interpretable reasoning. The approach enhances the integration of structured medical knowledge with machine learning, offering a scalable framework for data-driven clinical decision-making across diverse healthcare settings.