UCLA

Clinicians are routinely faced with the practical challenge of integrating high-dimensional data in order to make the most appropriate clinical decision from a large set of possible actions for a given patient. Current clinical decisions continue to rely on clinical practice guidelines, which are aimed at a representative patient rather than an individual patient who may display other characteristics. Unfortunately, if it were necessary to learn everything from the limited medical data, the problem would be completely intractable because of the high-dimensional feature space and large number of medical decisions. My thesis aims to design and analyze algorithms that learn and exploit the structure in the medical data -- for instance, structures among the features (relevance relations) or decisions (correlations). The proposed algorithms have much in common with the works in online and counterfactual learning literature but unique challenges in the medical informatics lead to numerous key differences from existing state of the art literature in Machine Learning (ML) and require key innovations to deal with large number of features and treatments, heterogeneity of the patients, sequential decision-making, and so on.