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Learn how Geisinger, one of the first healthcare systems to install a DGX-1 inside a clinical network, is developing and implementing machine learning solutions to improve patient care. We will show how we leveraged the DGX-1 to analyze large clinical datasets to tackle clinically relevant problems. Specific applications include: (1) using 46,583 clinically acquired 3D computed tomography images of the brain to develop and implement a deep learning model to efficiently prioritize radiology worklists for quicker diagnosis of intracranial hemorrhage, (2) using deep learning to analyze more than 200,000 echocardiographic videos of the heart to accurately predict patient survival, (3) analyzing more than 1 million 12-lead electrocardiographic tracings from the heart to predict future clinically relevant events, and (4) using machine learning to optimize evidence-based care delivery for a population of more than 10,000 patients with heart failure.
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