title: Applying an artificial neural network to warfarin maintenance dose prediction.
abstract: BACKGROUND
  Oral anticoagulation with warfarin can lead to life-threatening events as a result of either over-anticoagulation or undertreatment. One of the main contributors to an undesirable warfarin effect is the need to adjust its daily dose for a specific patient. The dose is adjusted empirically based on the experience of the clinician, a method that is often imprecise. There is currently no other well-accepted method for predicting the maintenance dose of warfarin.
  
  
  OBJECTIVE
  To describe the application of an artificial neural network to the problem of warfarin maintenance dose prediction.
  
  
  METHODS
  We designed a neural network that predicts the maintenance dose of warfarin. Data on 148 patients attending a large anticoagulant clinic were collected by file review. Using correlational analysis of the patients' data we selected the best input variables. The network was trained by using the back-propagation algorithm on a subset of our data and the results were validated against the rest of the data. We used a multivariate linear regression to create a comparable model.
  
  
  RESULTS
  The neural network generated reasonable predictions of the maintenance dose (r = 0.823). The results of the linear regression model were similar (r = 0.800).
  
  
  CONCLUSIONS
  Neural networks can be applied successfully for warfarin maintenance dose prediction. The results are promising, but further investigation is needed.
authors.affiliation: Department of Ophthalmology, Rabin Medical Center, Petah Tiqva, Israel.
authors.forename: Idit
authors.initials: I
authors.lastname: Solomon
authors.forename: Nitsan
authors.initials: N
authors.lastname: Maharshak
authors.forename: Gal
authors.initials: G
authors.lastname: Chechik
authors.forename: Leonard
authors.initials: L
authors.lastname: Leibovici
authors.forename: Aharon
authors.initials: A
authors.lastname: Lubetsky
authors.forename: Hillel
authors.initials: H
authors.lastname: Halkin
authors.forename: David
authors.initials: D
authors.lastname: Ezra
authors.forename: Nachman
authors.initials: N
authors.lastname: Ash
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000925:Anticoagulants / Q000008:administration & dosage*; D004305:Dose-Response Relationship, Drug; D004360:Drug Therapy, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D014859:Warfarin / Q000008:administration & dosage*
chemical_list: D000925:Anticoagulants; D014859:Warfarin