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Revolutionizing Healthcare
Revolutionizing Healthcare: Session Archive
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MICCAI 2024: Data-centric & Dynamic learning
NeurIPS 2023: Data-Centric AI for reliable and responsible AI Tutorial
IJCAI-23: Data-Centric AI Tutorial
AAAI-23: Synthetic Data Tutorial
AAAI-22: Time series in healthcare
Individualized treatment effect inference
ICML 2021: Synthetic Healthcare Data Generation and Assessment
ICML 2020: Machine Learning for Healthcare: Challenges, Methods, and Frontiers
Research pillars
AI4Science
Automated machine learning
Causal deep learning
Clustering
Data-Centric AI
Digital Twins
Causal Effect Inference
Interpretable Machine Learning
Next-gen clinical trials
Quantitative epistemology
Self-supervised, semi-supervised, and multi-view learning
Survival analysis, competing risks, and comorbidities
Synthetic data
Overview
Privacy challenge
Time series
Uncertainty quantification
Impact
Our lab’s impact
AutoPrognosis 2.0
SynthCity
TemporAI
CliMB
DC-Check
What is Data-Centric AI?
DC-Check: about the Tool
Resources
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Overview
Demonstrator: Synthetic Data
Demonstrator: Survival Analysis
Early detection and diagnosis
Early detection and diagnosis
A Foundational Framework for Personalized Early and Timely Diagnosis
The future of clinical trials
Cardiovascular Disease
Pharmacology
Machine Learning meets Pharmacology
Revolutionising Pharmacological Predictions with Synthetic Model Combination
Alzheimer’s
Cancer
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Prostate Cancer
Breast cancer
COVID-19
Our projects
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Policy Impact Predictor
Cystic fibrosis
Organ transplantation
AI Methods for Biological Discovery
Hub for Healthcare
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Cambridge Summer Schools 2026
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Category - Tutorial
MICCAI 2024: Data-centric & Dynamic learning
Mihaela van der Schaar
Nabeel Seedat
October 3, 2024
The lab
Mihaela van der Schaar
Research Team
Funding
Clinical partners
Publications
Big ideas
News
Videos
Events
Software
Engagement sessions
Revolutionizing Healthcare
Revolutionizing Healthcare: Session Archive
Inspiration Exchange
Exploring human and machine intelligence
WeCREATE
2025 open house
2024 open house
2023 open house
2022 open house
2021 open house
Cambridge Summer Schools 2025
Tutorials
MICCAI 2024: Data-centric & Dynamic learning
NeurIPS 2023: Data-Centric AI for reliable and responsible AI Tutorial
IJCAI-23: Data-Centric AI Tutorial
AAAI-23: Synthetic Data Tutorial
AAAI-22: Time series in healthcare
Individualized treatment effect inference
ICML 2021: Synthetic Healthcare Data Generation and Assessment
ICML 2020: Machine Learning for Healthcare: Challenges, Methods, and Frontiers
Research pillars
AI4Science
Automated machine learning
Causal deep learning
Clustering
Data-Centric AI
Digital Twins
Causal Effect Inference
Interpretable Machine Learning
Next-gen clinical trials
Quantitative epistemology
Self-supervised, semi-supervised, and multi-view learning
Survival analysis, competing risks, and comorbidities
Synthetic data
Overview
Privacy challenge
Time series
Uncertainty quantification
Impact
Our lab’s impact
AutoPrognosis 2.0
SynthCity
TemporAI
CliMB
DC-Check
What is Data-Centric AI?
DC-Check: about the Tool
Resources
Demonstrators
Overview
Demonstrator: Synthetic Data
Demonstrator: Survival Analysis
Early detection and diagnosis
Early detection and diagnosis
A Foundational Framework for Personalized Early and Timely Diagnosis
The future of clinical trials
Cardiovascular Disease
Pharmacology
Machine Learning meets Pharmacology
Revolutionising Pharmacological Predictions with Synthetic Model Combination
Alzheimer’s
Cancer
Our projects
Prostate Cancer
Breast cancer
COVID-19
Our projects
Announcements
Policy Impact Predictor
Cystic fibrosis
Organ transplantation
AI Methods for Biological Discovery
Hub for Healthcare
Connect
Ph.D. applications
Cambridge Summer Schools 2026
Mihaela van der Schaar
Nabeel Seedat