📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of AI for Healthcare Applications, from foundations to a certified capstone project.
Clinical Context
• EHR structure, coding systems and the messiness of real clinical data
• FHIR and interoperability standards for extracting usable datasets
• Confounding by indication and other traps in observational health data
Prediction
• Deterioration, readmission and sepsis prediction as canonical problems
• Calibration over discrimination: why AUC alone misleads clinicians
• Prospective versus retrospective evaluation and label timing
Language
• Information extraction from notes, discharge summaries and referrals
• De-identification and its residual re-identification risk
• Ambient documentation tools and the verification burden they shift
Deployment
• Workflow integration and alert design that avoids fatigue
• Silent deployment and shadow evaluation before clinical influence
• Monitoring for drift as case mix and practice patterns change
Governance
• Software as a medical device: regulatory classification and evidence
• Clinical safety cases and post-market surveillance duties
• Equity auditing across demographic groups and access conditions
Earn government-registered certification in AI for Healthcare Applications
e-Certificate and e-Marksheet issued on successful completion.
