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Machine learning for medicine: Predicting, pre-empting and treating disease

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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:

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