Module #1 Introduction to Disease Prevention Overview of the importance of disease prevention, goals and objectives of the course
Module #2 Types of Predictive Models Introduction to machine learning, types of predictive models, and their applications in disease prevention
Module #3 Data Sources for Disease Prevention Overview of data sources for disease prevention, including EHRs, claims data, and wearable devices
Module #4 Data Preprocessing for Predictive Modeling Data preprocessing techniques for predictive modeling, including data cleaning, transformation, and feature selection
Module #5 Supervised Learning for Disease Prediction Introduction to supervised learning, including regression and classification models for disease prediction
Module #6 Unsupervised Learning for Disease Subtyping Introduction to unsupervised learning, including clustering and dimensionality reduction for disease subtyping
Module #7 Introduction to Deep Learning for Disease Diagnosis Introduction to deep learning, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for disease diagnosis
Module #8 Predictive Modeling for Infectious Diseases Predictive modeling techniques for infectious diseases, including SIR models and spatial modeling
Module #9 Predictive Modeling for Chronic Diseases Predictive modeling techniques for chronic diseases, including risk scoring and survival analysis
Module #10 Assessment of Model Performance Metrics and techniques for assessing the performance of predictive models, including confusion matrices and ROC curves
Module #11 Model Interpretation and Explainability Techniques for interpreting and explaining predictive models, including SHAP values and feature importance
Module #12 Ethical Considerations in Disease Prevention Ethical considerations in disease prevention, including bias, fairness, and transparency
Module #13 Case Study:Predicting Cardiovascular Disease Real-world application of predictive modeling for cardiovascular disease prevention
Module #14 Case Study:Predicting Diabetes Real-world application of predictive modeling for diabetes prevention
Module #15 Case Study:Predicting Cancer Real-world application of predictive modeling for cancer prevention
Module #16 Implementing Predictive Models in Clinical Practice Strategies for implementing predictive models in clinical practice, including integration with EHRs
Module #17 Evaluating the Impact of Predictive Models Methods for evaluating the impact of predictive models on disease prevention outcomes
Module #18 Future Directions in Disease Prevention Emerging trends and future directions in disease prevention, including personalized medicine and genomics
Module #19 Special Considerations in Disease Prevention Special considerations in disease prevention, including global health and health disparities
Module #20 Collaboration and Communication in Disease Prevention Importance of collaboration and communication in disease prevention, including stakeholder engagement and health literacy
Module #21 Policy and Regulatory Considerations Policy and regulatory considerations in disease prevention, including HIPAA and GDPR
Module #22 Predictive Modeling for Population Health Predictive modeling techniques for population health, including spatial analysis and hot spot detection
Module #23 Predictive Modeling for Personalized Medicine Predictive modeling techniques for personalized medicine, including precision medicine and pharmacogenomics
Module #24 Advanced Topics in Predictive Modeling Advanced topics in predictive modeling, including transfer learning and ensemble methods
Module #25 Course Wrap-Up & Conclusion Planning next steps in Predictive Models for Disease Prevention career
