@article{ref1,
title="Artificial neural networks: predicting head CT findings in elderly patients presenting with minor head injury after a fall",
journal="American journal of emergency medicine",
year="2016",
author="Dusenberry, Michael W. and Brown, Charles K. and Brewer, Kori L.",
volume="35",
number="2",
pages="260-267",
abstract="OBJECTIVES: To construct an artificial neural network (ANN) model that can predict the presence of acute CT findings with both high sensitivity and high specificity when applied to the population of patients≥age 65years who have incurred minor head injury after a fall. <br><br>METHODS: An ANN was created in the Python programming language using a population of 514 patients ≥ age 65 years presenting to the ED with minor head injury after a fall. The patient dataset was divided into three parts: 60% for &quot;training&quot;, 20% for &quot;cross validation&quot;, and 20% for &quot;testing&quot;. Sensitivity, specificity, positive and negative predictive values, and accuracy were determined by comparing the model's predictions to the actual correct answers for each patient. <br><br>RESULTS: On the &quot;cross validation&quot; data, the model attained a sensitivity (&quot;recall&quot;) of 100.00%, specificity of 78.95%, PPV (&quot;precision&quot;) of 78.95%, NPV of 100.00%, and accuracy of 88.24% in detecting the presence of positive head CTs. On the &quot;test&quot; data, the model attained a sensitivity of 97.78%, specificity of 89.47%, PPV of 88.00%, NPV of 98.08%, and accuracy of 93.14% in detecting the presence of positive head CTs. <br><br>CONCLUSIONS: ANNs show great potential for predicting CT findings in the population of patients ≥ 65 years of age presenting with minor head injury after a fall. As a good first step, the ANN showed comparable sensitivity, predictive values, and accuracy, with a much higher specificity than the existing decision rules in clinical usage for predicting head CTs with acute intracranial findings.<br><br>Copyright © 2016 Elsevier Inc. All rights reserved.<p /> <p>Language: en</p>",
language="en",
issn="0735-6757",
doi="10.1016/j.ajem.2016.10.065",
url="http://dx.doi.org/10.1016/j.ajem.2016.10.065"
}