Scalable and data efficient deep reinforcement learning methods for healthcare applications
Artificial intelligence driven medical devices have created the potential for significant breakthroughs in healthcare technology. Healthcare applications using reinforcement learning are still very sparse as the medical domain is very complex and decision making requires domain expertise. High volumes of data generated from medical devices – a key input for delivering on the promise of AI, suffers from both noise and lack of ground truth. The cost of data increases as it is cleaned and annotated. Unlike other data sets, medical data annotation, which is critical for accurate ground truth, requires ...
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Saripalli, Venkata Ratnam
Anderson, Charles W.