https://www.selleckchem.com/products/nms-873.html
BACKGROUND The main goal of this study is to explore the use of features representing patient-level electronic health record (EHR) data, generated by the unsupervised deep learning algorithm autoencoder, in predictive modeling. Since autoencoder features are unsupervised, this paper focuses on their general lower-dimensional representation of EHR information in a wide variety of predictive tasks. METHODS We compare the model with autoencoder features to traditional models logistic model with least absolute shrinkage and selection operat
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