Program Objectives:

• Equip participants with foundational knowledge of AI and ML technologies in healthcare analytics.
• Explore predictive modeling, natural language processing (NLP), and their applications in clinical documentation and patient care.
• Address the ethical considerations and privacy laws relevant to deploying AI solutions in healthcare settings.
• Combine theoretical knowledge with practical applications through case studies and a capstone project.

What you will learn?

MODULE 1 : Introduction to Healthcare Analytics

• Overview of healthcare analytics.
• Data types and sources in healthcare.
• Introduction to health informatics.
• Key challenges and opportunities in healthcare data analysis.

MODULE 2 : Fundamentals of AI and Machine Learning

• Basics of artificial intelligence.
• Introduction to machine learning and deep learning.
• Supervised vs. unsupervised learning in healthcare.
• Tools and technologies for AI development (Python, TensorFlow, etc.).

MODULE 3 : Predictive Modeling in Healthcare

• Understanding predictive analytics in healthcare.
• Techniques for disease outbreak prediction.
• Patient risk stratification models.
• Machine learning algorithms for healthcare predictions.

MODULE 4 : Natural Language Processing for Clinical Documentation

• Introduction to NLP and its applications in healthcare.
• Text mining and analysis of clinical notes.
• Sentiment analysis for patient feedback.
• Implementing NLP projects using Python and NLTK.

MODULE 5 : Data Ethics and Privacy in Healthcare

• Ethical considerations in AI applications.
• Data privacy laws and regulations (HIPAA, GDPR).
• Ensuring fairness and avoiding bias in AI models.
• Case studies on ethical dilemmas in healthcare analytics.