Programme Parameters
Advanced Postgrad
8 Weeks (80 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI in Personalized Medicine explores how machine learning turns the promise of tailored treatment into practice. You learn to integrate the data that defines an individual — genomic, clinical, imaging and lifestyle — and build models that predict disease risk, forecast treatment response, and support therapy selection matched to a patient. The course connects these to real precision-medicine applications in oncology and beyond, and to the fairness, privacy and validation demands of decisions about individual care. You finish able to reason about an AI approach to personalised medicine. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI in personalized medicine — integrating genomic, clinical and lifestyle data to predict risk, guide treatment and tailor care to the individual.
📋 Course Objectives
1. Integrate genomic, clinical and lifestyle data.
2. Predict individual disease risk.
3. Forecast treatment response and guide therapy.
4. Apply models to precision-medicine use cases.
5. Address fairness, privacy and validation.
👥 Who Should Enroll?
• Clinical and biomedical professionals
• Bioinformatics and health data scientists
• Precision-medicine and biotech teams
• Students of medical AI
🚀 Key Learning Outcomes
• An understanding of AI in personalised medicine.
• A multi-data integration perspective.
• A patient-centred, responsible approach.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Stratification and the Personalisation Claim
• Stratified, precision and personalised medicine distinguished honestly
• Biomarkers: prognostic versus predictive, and why the distinction decides use
• Evidence thresholds before a marker changes treatment
Multi-Omic and Clinical Integration
• Combining genomic, transcriptomic and clinical data at patient level
• Batch effects, missing modalities and cohort heterogeneity
• Longitudinal records and treatment-response labelling
Treatment Response and Subtype Discovery
• Subtype discovery and the instability of unsupervised clusters across cohorts
• Response prediction and the confounding of treatment assignment
• Survival modelling with censoring handled correctly
Pharmacogenomics and Dosing
• Star-allele calling and CPIC-guided dose adjustment
• Drug-gene and drug-drug interaction in clinical decision support
• Companion diagnostics and their regulatory coupling to a therapy
Equity, Cost and Implementation
• Ancestry representation and the portability of markers across populations
• Cost-effectiveness and reimbursement for stratified therapy
• Clinician communication of probabilistic, individualised results
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Microsoft Excel |
| Covered Tool / Platform | Relevant Online Databases |
Frequently Asked Questions
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