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AMIA Annu Symp Proc · Jan 2006
Predicting hospital admission in a pediatric Emergency Department using an Artificial Neural Network.
- Jeffrey Leegon, Ian Jones, Kevin Lanaghan, and Dominik Aronsky.
- Dept. of Informatics, University of Edinburgh, Edinburgh, UK.
- AMIA Annu Symp Proc. 2006 Jan 1:1004.
AbstractHospital 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.
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