{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/113146"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/113146","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Long short-term memory recurrent neural networks for classification of acute hypotensive episodes","abstract":"An acute hypotensive episode (AHE) is a life-threatening condition durich which a patient's mean arterial blood pressure drops below 60 mmHG for a period of 30 minutes. This thesis presents the development and evaluation of a series of Long short-term memory recurrent neural network (LSTM RNN) models which predict whether a patient will experience an AHE or not based on a time series of mean arterial blood pressure (ABP). 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