Synthetic Data in Healthcare: Methods, Challenges, and Use Cases


Machine learning (ML) has the potential to improve healthcare by augmenting clinical workflows and providing decision support for clinical practitioners across many disease areas. However, research in this field is impeded by the lack of access to high-quality data at scale. Advances in generative modeling paves the way for developing tools that can synthesize realistic multi-modal clinical data that can be used for ML model development without compromising patient privacy. In this talk, we explore different ML methods for synthesizing healthcare data, highlight the peculiar challenges associated with generative modeling in a clinical context, and discuss various use cases of synthetic clinical data in ML-based health research.


Ahmed Alaa is an Assistant Professor of Computational Precision Health at UC Berkeley and UCSF, with affiliations in the EECS and Statistics departments at UC Berkeley. Previously, he was a postdoctoral associate at Massachusetts Institute of Technology (MIT CSAIL and IMES) and the Broad Institute of MIT and Harvard University. He was also a joint postdoctoral scholar at Cambridge University, Cambridge Center for AI in Medicine and the University of California, Los Angeles (UCLA). He obtained his Ph.D. in Electrical and Computer Engineering from UCLA, where he received the 2021 Edward K. Rice Outstanding Doctoral Student Award from the UCLA Samueli School of Engineering. His research interests include machine learning for healthcare, computer vision for medical imaging, clinical informatics, statistics, and causal inference.

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