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Computational Methods for the Imputation and Prediction of Digital Health Data


Advances in both technology and medicine have enabled monumental progress toward the realization of precision medicine. In particular, machine learning algorithms -- powered by electronic health records, genomic information, wearable sensors, and medical images -- are positioned to become an integral part of the clinical workflow. While a tremendous amount of biomedical data is being generated and collected on a daily basis, plenty of data are still not routinely captured due to invasiveness, inconvenience, or cost. In this dissertation, we first describe the development and validation of a machine learning model that uses pre-operative data readily available in the electronic health record to predict post-operative in-hospital mortality. We then present multiple novel computational methods for accurately imputing unobserved health data using several different types of observed data, including physiological waveforms, genomics, and videos.

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