title: 'Neural network' algorithm to predict severity in epidermolysis bullosa simplex.
abstract: OBJECTIVE
  There are various genotypic variations known for the four phenotypic presentations of epidermolysis bullosa simplex (EBS). A neural network algorithm may be used to find the relationship between the various factors responsible for a particular phenotypic presentation. We assessed the value of neural network to predict the prognosis of epidermolysis bullosa simplex.
  
  
  METHODS
  Cases of EBS in which sequencing had been performed to identify the position and type of mutation were collected by literature search and the resulting data was analyzed using neural network algorithm.
  
  
  RESULTS
  The statistical prediction had an accuracy rate of 78%.
  
  
  CONCLUSIONS
  Neural networks can identify hidden patterns in a huge database without the intervention of a skilled statistician. It has the potential to change the way we analyze clinical and experimental data at present.
authors.affiliation: Department of Dermatology, Atlas Star Medical Centre, Dubai, United Arab Emirates. beapen@emirates.net.ae
authors.forename: Bell Raj
authors.initials: BR
authors.lastname: Eapen
mesh_terms: D000465:Algorithms; D016110:Epidermolysis Bullosa Simplex / Q000175:diagnosis*; D006801:Humans; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests