📚 Syllabus & Course Curriculum
AI & Machine Learning in Healthcare
Module-by-module breakdown of Advanced AI in Clinical Analytics, from foundations to a certified capstone project.
Outline
Clinical Data Foundations and Modeling Approaches
Outline
Chapter 1.1: Clinical Data Types – EHRs, Lab Data, Imaging, Claims • Chapter 1.2: Data Quality, Standardization (FHIR, HL7), and Governance • Chapter 1.3: Data Preprocessing for AI: Missingness, Imputation, Labeling • Chapter 1.4: Ethical and Legal Considerations in Clinical Data Use
Outline
Chapter 2.1: Risk Prediction Models (Readmission, Mortality, LOS) • Chapter 2.2: Feature Engineering for Clinical Use Cases • Chapter 2.3: Handling Longitudinal and Time-Series Clinical Data • Chapter 2.4: Evaluation Metrics: AUROC, Precision/Recall, Calibration
Outline
Deep Learning, NLP, and Imaging in Clinical Contexts
Outline
Chapter 3.1: Deep Neural Networks for Structured EHR Data • Chapter 3.2: Time-Series Models: RNNs, LSTMs, Transformers in Healthcare • Chapter 3.3: Multi-modal Models: Combining Text, Labs, and Images • Chapter 3.4: Transfer Learning and Pretrained Models in Clinical Tasks
Outline
Chapter 4.1: Information Extraction from Clinical Notes • Chapter 4.2: Named Entity Recognition and ICD Code Prediction • Chapter 4.3: Clinical Imaging Models: Radiology, Pathology, Ophthalmology • Chapter 4.4: Annotating and Validating NLP/Imaging Models
Outline
Deployment, Fairness, and Real-World Integration
Earn government-registered certification in Advanced AI in Clinical Analytics
e-Certificate and e-Marksheet issued on successful completion.
