Background: Heparin is a widely used anticoagulant in clinic. However, improper dosing can increase the risk of thromboembolic events, potentially leading to life-threatening complications. Clinic monitoring of heparin is very important for its use safety.
View Article and Find Full Text PDFThis paper focuses on predicting the length of stay for patients on the first day of admission and propose a predictive model named DGLoS. In order to capture the influence of various complex factors on the length of stay as well as the dependencies among various factors, DGLoS uses a deep neural network to model both the patient information and diagnostic information. Targeting at different attribution types, we utilize different coding methods to convert raw data to the input features.
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