Citation

Dusenberry MW, Brown CK, Brewer KL. Am. J. Emerg. Med. 2016; 35(2): 260-267.

Affiliation

Department of Emergency Medicine, Brody School of Medicine, East Carolina University, 600 Moye Blvd, Greenville, NC 27834, USA. Electronic address: brewerk@ecu.edu.

Copyright

(Copyright © 2016, Elsevier Publishing)

DOI

10.1016/j.ajem.2016.10.065

PMID

27876174

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.

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 "training", 20% for "cross validation", and 20% for "testing". 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.

RESULTS: On the "cross validation" data, the model attained a sensitivity ("recall") of 100.00%, specificity of 78.95%, PPV ("precision") of 78.95%, NPV of 100.00%, and accuracy of 88.24% in detecting the presence of positive head CTs. On the "test" 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.

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.

Copyright © 2016 Elsevier Inc. All rights reserved.

Language: en