AL-504 (A) RGPV B.Tech
AI in Health Care (AHC)
Previous year papers and the official syllabus are free to read, no account needed.
Previous Year Papers
1 paper, latest Nov 2023
Syllabus, notes and important questions for AL-504 (A)
Syllabus units
Topics as the official RGPV syllabus lists them.
Unit 1 Disease detection with computer vision 8 topics
- Medical image diagnosis
- Eye disease diagnosis
- Cancer diagnosis using imaging
- Building and training a model for medical diagnosis
- Training, prediction, and loss functions
- Image classification and class imbalance handling
- Generating additional samples for training (data augmentation)
- Model testing and validation
Unit 2 Evaluating models 15 topics
- Sensitivity and specificity as evaluation metrics
- Accuracy expressed as conditional probability
- Confusion matrix construction and interpretation
- Receiver operating characteristic (ROC) curve analysis
- Threshold selection for classification decisions
- Image segmentation on MRI images
- Medical image segmentation techniques
- MRI data acquisition and image registration
- Segmentation algorithms
- 2D U‑Net architecture
- 3D U‑Net architecture
- Data augmentation strategies for segmentation
- Loss functions specific to segmentation tasks
- Assessment across different populations and diagnostic technologies
- External validation of segmentation models
Unit 3 Linear prognostic models 14 topics
- Medical prognosis fundamentals
- Atrial fibrillation risk modeling
- Liver disease mortality prediction
- Risk assessment for heart disease
- Evaluation of prognostic models
- Concordant pair analysis
- Risk tie handling
- Permissible pair identification
- Tree‑based prognostic modeling
- Decision trees applied to prognosis
- Techniques to mitigate overfitting in trees
- Modeling with different statistical distributions
- Missing data handling in prognostic datasets
- Imputation methods for missing values
Unit 4 Survival Models and Time 11 topics
- Survival analysis concepts
- Survival function definition
- Collection of time‑to‑event data
- Estimation of the survival function
- Risk modeling using linear models
- Risk modeling using tree‑based models
- Hazard function formulation
- Relative risk estimation
- Individual versus baseline hazard comparison
- Survival trees methodology
- Nelson‑Aalen estimator for cumulative hazard
Unit 5 Medical Treatment Effect Estimation 6 topics
- Analysis of randomized controlled trial data
- Average treatment effect (ATE) estimation
- Conditional average treatment effect (CATE) estimation
- T‑Learner methodology
- S‑Learner methodology
- C‑for‑benefit approach