AI Techniques for Predictive Health Analytics
( 25 Modules )

Module #1
Introduction to Predictive Health Analytics
Overview of predictive analytics in healthcare, importance, and applications

Module #2
AI and Machine Learning Fundamentals
Basic concepts of AI, machine learning, and deep learning

Module #3
Healthcare Data Overview
Types of healthcare data, data sources, and data preprocessing techniques

Module #4
Data Quality and Preprocessing
Data cleaning, normalization, feature selection, and feature engineering

Module #5
Supervised Learning for Predictive Analytics
Basics of supervised learning, regression, and classification

Module #6
Unsupervised Learning for Patient Segmentation
Clustering, dimensionality reduction, and patient segmentation

Module #7
Introduction to Deep Learning for Healthcare
Basic concepts of deep learning, neural networks, and convolutional neural networks

Module #8
Deep Learning for Medical Imaging
Applications of deep learning in medical imaging, computer vision, and image analysis

Module #9
Natural Language Processing for Healthcare
Introduction to NLP, text processing, and sentiment analysis in healthcare

Module #10
Predictive Modeling for Disease Diagnosis
Building predictive models for disease diagnosis using supervised learning

Module #11
Predictive Modeling for Patient Outcome Prediction
Building predictive models for patient outcome prediction using supervised learning

Module #12
Survival Analysis for Predictive Analytics
Introduction to survival analysis, Kaplan-Meier estimates, and Cox proportional hazards model

Module #13
Time Series Analysis for Health Analytics
Introduction to time series analysis, ARIMA, and prophet for health analytics

Module #14
Unsupervised Anomaly Detection for Healthcare
Unsupervised anomaly detection methods, including isolation forest and local outlier factor

Module #15
Supervised Anomaly Detection for Healthcare
Supervised anomaly detection methods, including one-class SVM and supervised isolation forest

Module #16
Explainable AI for Healthcare
Introduction to explainable AI, model interpretability, and feature importance

Module #17
Ethical Considerations in AI for Healthcare
Ethical considerations, fairness, and transparency in AI for healthcare

Module #18
Deploying AI Models in Healthcare
Deploying AI models in healthcare, model serving, and model monitoring

Module #19
Case Study:Predictive Analytics for Diabetes Management
Real-world case study on applying predictive analytics for diabetes management

Module #20
Case Study:AI for Cancer Diagnosis and Treatment
Real-world case study on applying AI for cancer diagnosis and treatment

Module #21
Case Study:Patient Flow Prediction using Time Series Analysis
Real-world case study on applying time series analysis for patient flow prediction

Module #22
Case Study:Predictive Modeling for Hospital Readmission
Real-world case study on applying predictive modeling for hospital readmission prediction

Module #23
Special Topics in AI for Healthcare
Special topics, including AI for healthcare access, AI for surgical robotics, and AI for personal genomics

Module #24
Best Practices for AI in Healthcare
Best practices for AI in healthcare, model validation, and model updating

Module #25
Course Wrap-Up & Conclusion
Planning next steps in AI Techniques for Predictive Health Analytics career

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AI Techniques for Predictive Health Analytics ( 25 Modules )

AI Techniques for Predictive Health Analytics
( 25 Modules )