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Diversity-Promoting and Large-Scale Machine Learning for Healthcare
[Technical Report, Doctoral Thesis]
Carnegie Mellon University
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In healthcare, a tsunami of medical data has emerged, including electronic health records, images, literature, etc. These data are heterogeneous and noisy, which renders clinical decision-makings time-consuming, error-prone, and suboptimal. In this thesis, we develop machine learning ML models and systems for distilling high value patterns from unstructured clinical data and making informed and real-time medical predictions and recommendations, to aid physicians in improving the efficiency of workflow and the quality of patient care.
[A, Approved For Public Release]