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Subject Syllabus

AL504 (A) - AI in Health Care

AIML · Semester 5

Course Modules

Learn topic-wise with Hyper AI and revise using mapped PYQs.

Medical image diagnosis Eye disease and cancer diagnosis Building and training a model for medical diagnosis Training, prediction, and loss Image classification and class imbalance Generating more samples Model testing

Sensitivity, specificity, and evaluation metrics Accuracy in terms of conditional probability Confusion matrix ROC curve and threshold Image segmentation on MRI images Medical image segmentation MRI data and image registration Segmentation 2D U-Net and 3D U-Net Data augmentation and loss function for segmentation Different populations and diagnostic technology External validation

Medical prognosis Atrial fibrillation Liver disease mortality Risk of heart disease Evaluating prognostic models Concordant pairs, risk ties, and permissible pairs Prognosis with tree-based models Decision trees for prognosis Fixing overfitting Different distributions Missing data and imputation

Survival model and survival function Collecting time data Estimating the survival function Building a risk model using linear and tree-based models Hazard functions Relative risk Individual versus baseline hazard Survival trees Nelson-Aalen estimator

Analyzing data from a randomized control trial Average treatment effect Conditional average treatment effect T-learner S-learner C-for-benefit