Module #1 Introduction to Machine Learning in Healthcare Overview of machine learning, its applications in healthcare, and the importance of healthcare outcomes research
Module #2 Healthcare Data Primer Introduction to healthcare data sources, types, and formats, including EHRs, claims data, and registries
Module #3 Data Preprocessing for Healthcare Handling missing values, data normalization, and feature scaling in healthcare data
Module #4 Supervised Learning Fundamentals Introduction to supervised learning, regression, and classification, with a focus on healthcare applications
Module #5 Unsupervised Learning Fundamentals Introduction to unsupervised learning, clustering, and dimensionality reduction, with a focus on healthcare applications
Module #6 Machine Learning for Healthcare Outcomes Prediction Using machine learning to predict healthcare outcomes, such as readmission rates and disease progression
Module #7 Feature Engineering for Healthcare Data Techniques for constructing and selecting relevant features from healthcare data
Module #8 Deep Learning for Healthcare Outcomes Analysis Introduction to deep learning, including neural networks and convolutional neural networks, for healthcare outcomes analysis
Module #9 Natural Language Processing for Healthcare Using NLP to extract insights from clinical text data, including clinical notes and patient feedback
Module #10 Machine Learning for Disease Diagnosis Using machine learning to aid in disease diagnosis, including image analysis and biomarker discovery
Module #11 Machine Learning for Personalized Medicine Using machine learning to tailor treatment plans to individual patients, including precision medicine and genomics
Module #12 Evaluating Model Performance in Healthcare Metrics and techniques for evaluating the performance of machine learning models in healthcare
Module #13 Addressing Bias in Healthcare Machine Learning Identifying and mitigating bias in machine learning models, including addressing racial and socioeconomic disparities
Module #14 Interpretable Machine Learning for Healthcare Techniques for making machine learning models more interpretable and transparent in healthcare
Module #15 Machine Learning for Healthcare Policy and Decision-Making Using machine learning to inform healthcare policy and decision-making, including cost-effectiveness analysis
Module #16 Case Studies in Healthcare Outcomes Research Real-world examples of machine learning applications in healthcare outcomes research
Module #17 Machine Learning for Healthcare Quality Improvement Using machine learning to improve healthcare quality, including identifying areas for improvement and monitoring outcomes
Module #18 Implementing Machine Learning in Healthcare Settings Practical considerations for implementing machine learning models in healthcare settings, including data integration and stakeholder engagement
Module #19 Ethical Considerations for Machine Learning in Healthcare Ethical issues surrounding the use of machine learning in healthcare, including patient autonomy and data privacy
Module #20 Machine Learning for Healthcare Operations Using machine learning to optimize healthcare operations, including supply chain management and resource allocation
Module #21 Machine Learning for Patient Engagement Using machine learning to improve patient engagement, including personalized messaging and patient education
Module #22 Specialized Machine Learning Topics in Healthcare Advanced topics in machine learning for healthcare, including transfer learning and federated learning
Module #23 Machine Learning for Healthcare Research and-development Using machine learning to accelerate healthcare research and development, including drug discovery and clinical trials
Module #24 Course Wrap-Up & Conclusion Planning next steps in Machine Learning in Healthcare Outcomes Research career
