Applying an artificial neural network to warfarin maintenance dose prediction

Idit Solomon, Nitsan Maharshak, Gal Chechik, Leonard Leibovici, Aharon Lubetsky, Hillel Halkin, David Ezra, Nachman Ash

Research output: Contribution to journalReview articlepeer-review

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). Conclusion: Neural networks can be applied successfully for warfarin maintenance dose prediction. The results are promising, but further investigation is needed.

Original languageEnglish
Pages (from-to)732-735
Number of pages4
JournalIsrael Medical Association Journal
Volume6
Issue number12
StatePublished - Dec 2004

Keywords

  • Computer-assisted therapy
  • Dose prediction
  • Drug therapy
  • Neural network
  • Warfarin

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