Titre du document / Document title

Applying an artificial Neural network to warfarin maintenance dose prediction

Auteur(s) / Author(s)

SOLOMON Idit ; MAHARSHAK Nitsan ; CHECHIK Gal ; LEIBOVICI Leonard ; LUBETSKY Aharon ; HALKIN Hillel ; EZRA David ; ASH Nachman ;

Résumé / 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.

Revue / Journal Title

IMAJ   ISSN 1565-1088 

Source / Source

2004, vol. 6, n^o12, pp. 732-735 [4 page(s) (article)]

Langue / Language

Anglais

Editeur / Publisher

Israel Medical Association, Ramat Gan, ISRAEL  (1999) (Revue)

Localisation / Location

INIST-CNRS, Cote INIST : 27280, 35400012256708.0030