Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality.

ABSTRACT:

What we already know about this topic

WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: The authors tested the hypothesis that deep neural networks trained on intraoperative features can predict postoperative in-hospital mortality.

Methods

The data used to train and validate the algorithm consists of 59,985 patients with 87 features extracted at the end of surgery. Feed-forward networks with a logistic output were trained using stochastic gradient descent with momentum. The deep neural networks were trained on 80% of the data, with 20% reserved for testing. The authors assessed improvement of the deep neural network by adding American Society of Anesthesiologists (ASA) Physical Status Classification and robustness of the deep neural network to a reduced feature set. The n

SUBMITTER: Lee CK 

PROVIDER: S-EPMC6148401 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature