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Recurrent Neural Network Mortality prediction and Deep Reinforcement Learning in Manufacturing Scheduling


Neural networks are a powerful tool which have shown usefulness in a number of challenging domains. We have developed novel methods which incorporate neural networks in two important domains. The first domain considered is in mortality prediction in intensive care units. For this problem, a model involving long short term memory recurrent neural networks was developed. This model was trained and tested using the Medical Information Mart for Intensive Care (MIMIC iii) data set. Our method generates a mortality trajectory after each input health measurement consisting of a sequence of mortality predictions. Through testing we demonstrate that our model is capable of providing effective mortality predictions in this data set. The next domain considered is production scheduling in semiconductor manufacturing facilities. We developed two methods for training scheduling policies that utilize deep reinforcement learning. The first involves a Deep Q-Network, and the second is a novel method we developed known as the Predictron Deep Q-Network. A factory simulation model was developed. Our methods were demonstrated to outperform common industry methods on a set of simulated factory environments based on real world systems.

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