Predictive Analytics in Medicine: Forecasting Health Outcomes with AI
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Course Modules:
Module 1: Introduction to Predictive Analytics in Healthcare
What is predictive analytics?
Common use cases: readmission risk, sepsis detection, treatment success prediction
Overview of AI tools in healthcare forecasting
Module 2: Data Sources and Clinical Features
EHRs, lab results, imaging data, wearable sensors
Structured vs. unstructured data in medicine
Temporal features and longitudinal patient tracking
Module 3: Machine Learning Models for Prediction
Logistic regression, decision trees, random forests
Support Vector Machines (SVM), ensemble models
Neural networks and deep learning (optional)
Module 4: Model Evaluation and Fairness
Performance metrics: AUC-ROC, precision, recall, F1 score
Calibration and reliability curves
Bias detection and fairness in medical predictions
Module 5: Use Cases and Implementation
Predicting ICU readmission
Early detection of chronic diseases (e.g., diabetes, heart failure)
Personalized treatment response prediction
Module 6: Capstone Project – Build a Predictive Model
Choose a public medical dataset (e.g., MIMIC-III, UCI health datasets)
Clean, engineer features, and train a prediction model
Submit accuracy report, model summary, and ethical review
Tools & Technologies Used:
Python (Pandas, Scikit-learn, XGBoost, TensorFlow)
Jupyter Notebook / Google Colab
Matplotlib / Seaborn for visualization
Optional: SHAP or LIME for model explainability
Target Audience:
Healthcare professionals and medical researchers
AI/ML students focused on healthcare
Engineers developing clinical decision support systems
Public health analysts and medical data scientists
Global Learning Benefits:
Anticipate patient needs with data-driven insights
Improve clinical decision-making and reduce medical risks
Learn ethical modeling for high-stakes environments
Build a portfolio-ready healthcare AI application
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