title: Artificial neural networks: Predicting head CT findings in elderly patients presenting with minor head injury after a fall.
abstract: OBJECTIVE
  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.
authors.affiliation: Brody School of Medicine, East Carolina University, 600 Moye Blvd, Greenville, NC 27834, USA. Electronic address: dusenberrymw@gmail.com.
authors.forename: Michael W
authors.initials: MW
authors.lastname: Dusenberry
authors.affiliation: Department of Emergency Medicine, Brody School of Medicine, East Carolina University, 600 Moye Blvd, Greenville, NC 27834, USA. Electronic address: browncha@ecu.edu.
authors.forename: Charles K
authors.initials: CK
authors.lastname: Brown
authors.affiliation: Department of Emergency Medicine, Brody School of Medicine, East Carolina University, 600 Moye Blvd, Greenville, NC 27834, USA. Electronic address: brewerk@ecu.edu.
authors.forename: Kori L
authors.initials: KL
authors.lastname: Brewer
mesh_terms: D000058:Accidental Falls; D000368:Aged; D003362:Cost-Benefit Analysis; D006259:Craniocerebral Trauma / Q000000981:diagnostic imaging* / Q000191:economics / Q000209:etiology; D020000:Decision Support Systems, Clinical; D005260:Female; D005544:Forecasting; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D012044:Regression Analysis; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed / Q000009:adverse effects / Q000191:economics / Q000592:standards*; D014193:Trauma Centers / Q000191:economics / Q000592:standards* / Q000706:statistics & numerical data; D014481:United States
keywords: Elderly; Falls; Head Injury; Minor; Neural Network Models