Application of MAML Meta-Learning in Rapid Adaptation and Effectiveness Evaluation of Personalized Nursing Plans

Personalized nursing plans are important for improving patient outcomes, satisfaction, and efficient use of medical resources, yet significant individual differences make rapid plan adaptation and early effectiveness evaluation difficult. Existing methods usually require long-term follow-up or large numbers of labeled samples, limiting practical deployment. To address these issues, this paper adopts a framework based on Model-Agnostic Meta-Learning to model personalized nursing adjustment as a few-shot learning task. A multi-nursing dataset is constructed, a shared network generation scheme is designed, and cross-task features are extracted through MAML. A dynamic weight module is introduced to fuse short-term and long-term data for real-time prediction. Simulation experiments show that MAML achieves a median improvement rate of 75.0% in key physiological indicators, which is 12.7% higher than CNN, and more than 60% of patients obtain scores above 8 points. The prediction accuracy based on 24-hour early data reaches 89.7%, and that based on 48-hour data reaches 92.8%, outperforming comparative methods. These results demonstrate that MAML enables rapid adaptation and efficient evaluation of personalized nursing plans under limited data conditions.