Modules

Module 1: Introduction to Predictive Analytics in Healthcare

• Defining predictive analytics and AI

• Use cases in patient risk scoring, readmission prediction, disease outbreak

• Introduction to predictive model lifecycle

• Practical Exercise: Reviewing successful case studies of AI in healthcare

Module 2: Data Preparation and Feature Engineering for Health Models

• Data cleaning, selection, and transformation

• Handling missing and imbalanced health data

• Feature extraction and variable selection

• Practical Exercise: Preparing a dataset for patient outcome prediction

Module 3: Machine Learning Algorithms for Health Forecasting

• Regression, classification, clustering models

• Time series analysis in epidemiology

• Evaluating model performance (ROC, precision, recall, F1-score)

• Practical Exercise: Building a predictive model to identify high-risk patients

Module 4: Ethics, Privacy, and Bias in Healthcare AI

• Algorithmic bias and mitigation strategies

• Patient data rights and informed consent

• AI governance and regulatory frameworks

• Practical Exercise: Analyzing a case study involving ethical AI challenges

Module 5: Implementation and Decision Support Systems

• Integrating AI models into healthcare workflows

• Communicating model results to clinicians and stakeholders

• Real-time clinical decision support systems

• Practical Exercise: Designing a workflow for AI-supported decision making in hospitals