Affiliation:
1. From the Department of Anesthesiology and Perioperative Care (C.K.L., M.C.), Department of Computer Sciences (C.K.L., P.B.), and Department of Bioengineering (M.C.), University of California Irvine, Irvine, California; and Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, California (I.H., E.G., M.C.).
Abstract
Abstract
Editor’s Perspective
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 networks were then compared to ASA Physical Status, logistic regression, and other published clinical scores including the Surgical Apgar, Preoperative Score to Predict Postoperative Mortality, Risk Quantification Index, and the Risk Stratification Index.
Results
In-hospital mortality in the training and test sets were 0.81% and 0.73%. The deep neural network with a reduced feature set and ASA Physical Status classification had the highest area under the receiver operating characteristics curve, 0.91 (95% CI, 0.88 to 0.93). The highest logistic regression area under the curve was found with a reduced feature set and ASA Physical Status (0.90, 95% CI, 0.87 to 0.93). The Risk Stratification Index had the highest area under the receiver operating characteristics curve, at 0.97 (95% CI, 0.94 to 0.99).
Conclusions
Deep neural networks can predict in-hospital mortality based on automatically extractable intraoperative data, but are not (yet) superior to existing methods.
Publisher
Ovid Technologies (Wolters Kluwer Health)
Subject
Anesthesiology and Pain Medicine
Cited by
139 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献