使用人工神经网络预测儿科急诊科的住院情况。
Predicting hospital admission in a pediatric Emergency Department using an Artificial Neural Network.
作者信息
Leegon Jeffrey, Jones Ian, Lanaghan Kevin, Aronsky Dominik
机构信息
Dept. of Informatics, University of Edinburgh, Edinburgh, UK.
出版信息
AMIA Annu Symp Proc. 2006;2006:1004.
Abstract
Hospital admission delays in the Emergency Department (ED) reduce capacity and contribute to the ED's diversion problem. We evaluated the accuracy of an Artificial Neural Network for the early prediction of hospital admission using data from 43,077 pediatric ED encounters. We used 9 variables commonly available in the ED setting. The area under the receiver operating characteristic curve was 0.897 (95% CI: 0.887-0.896). The instrument demonstrated high accuracy and may be used to alert clinicians to initiate admission processes earlier during a patient's ED encounter.
摘要
急诊科的住院延迟会降低容量，并导致急诊科的分流问题。我们使用来自43077例儿科急诊科就诊的数据，评估了用于早期预测住院的人工神经网络的准确性。我们使用了急诊科常见的9个变量。受试者工作特征曲线下面积为0.897（95%置信区间：0.887 - 0.896）。该工具显示出较高的准确性，可用于提醒临床医生在患者急诊科就诊期间更早地启动住院流程。
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