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• W3200728430 abstract "Length of stay (LoS) in the intensive care unit (ICU) is a general outcome measure used as an indicator of both quality of care and resource use. Existing LoS prediction methods usually model the problem as a binary classification or regression problem. This paper proposes to address this problem under a sequential learning framework using an embedded representation of patients. First, we exploit electronic health records (EHR) to learn patient distributed representation. Second, we formulate the LoS prediction task as a Deep Bayesian contextual bandit problem, which sequentially predicts LoS based on contextual distributed information about patients, with the goal of maximizing overall prediction outcomes. Experimental results show that our model has superior performance than other sequential learning approaches in the LoS task using the publicly available MIMIC-III ICU dataset. We also evaluate the robustness of our framework under different patient representations." @default.
• W3200728430 created "2021-09-27" @default.
• W3200728430 creator A5009911024 @default.
• W3200728430 creator A5018396266 @default.
• W3200728430 date "2021-07-18" @default.
• W3200728430 modified "2023-09-25" @default.
• W3200728430 title "Deep Thompson Sampling for Length of Stay Prediction" @default.
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• W3200728430 doi "https://doi.org/10.1109/ijcnn52387.2021.9533667" @default.
• W3200728430 hasPublicationYear "2021" @default.
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