Module #1 Introduction to Predictive Modeling in Healthcare Overview of predictive modeling, its importance in healthcare, and course objectives
Module #2 Fundamentals of Predictive Analytics Basic concepts of predictive analytics, types of predictive models, and evaluation metrics
Module #3 Healthcare Data Sources and Types Overview of healthcare data sources, data types, and data quality issues
Module #4 Data Preprocessing for Predictive Modeling Data cleaning, preprocessing, and feature engineering techniques for healthcare data
Module #5 Supervised Learning Fundamentals Introduction to supervised learning, regression, and classification techniques
Module #6 Linear Regression for Healthcare Predictive Modeling Applying linear regression to healthcare data, including model interpretation and evaluation
Module #7 Logistic Regression for Healthcare Predictive Modeling Applying logistic regression to healthcare data, including model interpretation and evaluation
Module #8 Decision Trees and Random Forests Introduction to decision trees and random forests, including model building and evaluation
Module #9 Support Vector Machines (SVMs) in Healthcare Applying SVMs to healthcare data, including model building and evaluation
Module #10 Unsupervised Learning Fundamentals Introduction to unsupervised learning, clustering, and dimensionality reduction techniques
Module #11 K-Means Clustering for Healthcare Data Applying k-means clustering to healthcare data, including model evaluation and interpretation
Module #12 Hierarchical Clustering for Healthcare Data Applying hierarchical clustering to healthcare data, including model evaluation and interpretation
Module #13 Dimensionality Reduction Techniques Introduction to dimensionality reduction techniques, including PCA, t-SNE, and autoencoders
Module #14 Predictive Modeling for Disease Diagnosis Applying predictive modeling to disease diagnosis, including case studies and best practices
Module #15 Predictive Modeling for Patient Outcome Prediction Applying predictive modeling to patient outcome prediction, including case studies and best practices
Module #16 Predictive Modeling for Healthcare Resource Allocation Applying predictive modeling to healthcare resource allocation, including case studies and best practices
Module #17 Model Evaluation and Validation Evaluating and validating predictive models, including metrics and techniques
Module #18 Model Deployment and Integration Deploying and integrating predictive models into healthcare systems, including best practices
Module #19 Ethical Considerations in Healthcare Predictive Modeling Ethical considerations and challenges in healthcare predictive modeling, including bias and fairness
Module #20 Regulatory Considerations in Healthcare Predictive Modeling Regulatory considerations and compliance in healthcare predictive modeling, including HIPAA and FDA regulations
Module #21 Predictive Modeling Tools and Technologies Overview of popular tools and technologies for predictive modeling in healthcare, including R, Python, and SQL
Module #22 Case Studies in Healthcare Predictive Modeling Real-world case studies of predictive modeling applications in healthcare, including disease diagnosis and patient outcome prediction
Module #23 Best Practices in Healthcare Predictive Modeling Best practices and lessons learned in healthcare predictive modeling, including model development, deployment, and maintenance
Module #24 Future of Predictive Modeling in Healthcare Emerging trends and opportunities in healthcare predictive modeling, including AI, machine learning, and precision medicine
Module #25 Course Wrap-Up & Conclusion Planning next steps in Predictive Modeling in Healthcare career
