title: Artificial neural networks for predicting failure to survive following in-hospital cardiopulmonary resuscitation.
abstract: BACKGROUND
  Neural networks are an artificial intelligence technique that uses a set of nonlinear equations to mimic the neuronal connections of biological systems. They have been shown to be useful for pattern recognition and outcome prediction applications, and have the potential to bring artificial intelligence techniques to the personal computers of practicing physicians, assisting them with a variety of medical decisions. It is proposed that such an artificial neural network can be trained, using information available at the time of admission to the hospital, to predict failure to survive following in-hospital cardiopulmonary resuscitation (CPR).
  
  
  METHODS
  The age, sex, heart rate, and 21 other clinical variables were collected on a consecutive series of 218 adult patients undergoing CPR at a 295-bed public acute-care hospital. The data set was divided into two groups. A neural network was trained to predict failure to survive to discharge following CPR, using one group as the training set and the other as the testing set. The procedure was then reversed, and the results of the two networks were combined to form an aggregate network.
  
  
  RESULTS
  The trained aggregate neural network had a sensitivity of 52.1% and a positive predictive value of 97% for the prediction of failure to survive following CPR. The relative risk of actually failing to survive to discharge following CPR for a patient predicted not to survive was 11.3 (95% CI 3.3 to 38.2).
  
  
  CONCLUSIONS
  Predicting failure to survive following CPR is but one possible application of neural network technology. It demonstrates how this technique can assist physicians in medical decision making. Future work should attempt to improve the positive predictive value of the neural network, to consider combining it with an expert system, and to compare it with other predictive tools. Once validated, the network can be distributed as a separate application for use by practicing physicians.
authors.affiliation: Department of Family Medicine, Wayne State University, Detroit, MI 48201.
authors.forename: M H
authors.initials: MH
authors.lastname: Ebell
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001185:Artificial Intelligence; D016887:Cardiopulmonary Resuscitation; D005188:False Negative Reactions; D005189:False Positive Reactions; D005260:Female; D005845:Georgia; D006744:Hospital Bed Capacity, 100 to 299; D017052:Hospital Mortality; D006779:Hospitals, Public; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011379:Prognosis; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D015996:Survival Rate; D017211:Treatment Failure