Master Study AI

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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