If you have a question about this talk, please contact jo de bono.
In this talk, I will give an overview of our research work on developing state-of-the-art machine learning & AI theory and methods aimed at providing actionable intelligence to patients, clinicians, medical researchers, and healthcare providers. This will include a discussion of our work on automated machine learning for the design of models for predicting clinical risk (AutoPrognosis), on leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks (RadialGAN) and on the fundamental theory and methods for causal inference and individualized treatment effects. More about our work can be found at:
http://medianetlab.ee.ucla.edu