You are working as a Machine Learning Engineer for a healthcare company that has developed a model to predict patient readmission risk. The model achieves 92% accuracy but stakeholders are concerned about the lack of transparency in its predictions. Your task is to implement and compare at least 4 different model interpretability techniques for a black-box model (e.g., XGBoost, Random Forest, or Neural Network). Techniques should include both global and local interpretability methods. Specifically, implement SHAP, LIME, Partial Dependence Plots, and Accumulated Local Effects. For each technique, explain its mathematical foundation, strengths, and limitations. Create a comprehensive analysis of model feature importance and provide explanations for individual predictions on 5 specific patient cases. Also develop guidelines for healthcare professionals on how to use these explanations in their decision-making process.