Predictive Analytics for Patient Health full syllabus

Module 1: Introduction to Predictive Analytics in Healthcare

• What is Predictive Analytics?
• Role of AI & Machine Learning in healthcare
• Real-world use-cases: Readmission prediction, early disease detection, etc.
• Benefits & challenges in medical settings

Module 2: Healthcare Data Essentials

• Types of Healthcare Data: EHR (Electronic Health Records), lab reports, vital signs
• Understanding ICD, CPT codes & medical terminologies
• Data sources: Hospitals, insurance companies, wearables
• Data privacy, HIPAA compliance (overview)

Module 3: Data Preprocessing for Health Models

• Cleaning real-world medical datasets
• Handling missing & imbalanced patient data
• Feature engineering: Age, gender, blood pressure, glucose levels, etc.
• Temporal features: How time impacts patient health trends

Module 4: Exploratory Data Analysis (EDA) for Patient Profiles

• Visualizing patient histories (line plots, histograms)
• Pattern detection in chronic illness data
• Identifying high-risk patients from trends

Module 5: Machine Learning Models for Health Predictions

• Logistic Regression for disease risk prediction
• Decision Trees & Random Forest for patient classification
• Support Vector Machines for diagnostic grouping
• k-NN for patient similarity analysis

Module 6: Predictive Models for Specific Use Cases

• Predicting hospital readmission (30-day window)
• Chronic disease forecasting (e.g., diabetes, heart disease)
• ICU mortality prediction
• Length of stay estimation in hospitals

Module 7: Model Evaluation & Metrics in Medical Context

• Accuracy vs. Precision vs. Recall vs. F1 in healthcare
• ROC, AUC & confusion matrix explained for doctors
• Dealing with false positives & false negatives in medical cases

Module 8: Real-Time Health Monitoring Systems

• Wearables data (like smartwatches, glucose monitors)
• Streaming health data using IoT + AI
• Alert systems for abnormal vitals

Module 9: Deployment & Integration in Hospital Systems

• Building dashboards for doctors (using Power BI/Tableau)
• Integrating AI with hospital EHR systems
• Interpretable ML models for clinical decision-making

Module 10: Ethics, Fairness & Responsible Use

• Bias in health data: Gender, race, age
• Ensuring fairness in life-and-death decisions
• Explainable AI (XAI) in healthcare
• Legal & ethical issues in using predictive tools

Capstone Project (Optional but Recommended)

• Choose one real-world patient dataset (from Kaggle, PhysioNet, etc.)
• Build, train, and evaluate a model to predict a medical condition
• Present your findings in a report/dashboard

Tools & Languages Covered

• Python (pandas, scikit-learn, matplotlib, seaborn)
• Jupyter Notebooks
• Power BI or Tableau (for visualizations)
• Google Colab (for cloud-based model training)