Revisiting Warfarin Dosing Using Machine Learning Techniques
Joint Authors
Sharabiani, Ashkan
Bress, Adam
Douzali, Elnaz
Darabi, Houshang
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-06-04
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Determining the appropriate dosage of warfarin is an important yet challenging task.
Several prediction models have been proposed to estimate a therapeutic dose for patients.
The models are either clinical models which contain clinical and demographic variables or pharmacogenetic models which additionally contain the genetic variables.
In this paper, a new methodology for warfarin dosing is proposed.
The patients are initially classified into two classes.
The first class contains patients who require doses of >30 mg/wk and the second class contains patients who require doses of ≤30 mg/wk.
This phase is performed using relevance vector machines.
In the second phase, the optimal dose for each patient is predicted by two clinical regression models that are customized for each class of patients.
The prediction accuracy of the model was 11.6 in terms of root mean squared error (RMSE) and 8.4 in terms of mean absolute error (MAE).
This was 15% and 5% lower than IWPC and Gage models (which are the most widely used models in practice), respectively, in terms of RMSE.
In addition, the proposed model was compared with fixed-dose approach of 35 mg/wk, and the model proposed by Sharabiani et al.
and its outperformance were proved in terms of both MAE and RMSE.
American Psychological Association (APA)
Sharabiani, Ashkan& Bress, Adam& Douzali, Elnaz& Darabi, Houshang. 2015. Revisiting Warfarin Dosing Using Machine Learning Techniques. Computational and Mathematical Methods in Medicine،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1057930
Modern Language Association (MLA)
Sharabiani, Ashkan…[et al.]. Revisiting Warfarin Dosing Using Machine Learning Techniques. Computational and Mathematical Methods in Medicine No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1057930
American Medical Association (AMA)
Sharabiani, Ashkan& Bress, Adam& Douzali, Elnaz& Darabi, Houshang. Revisiting Warfarin Dosing Using Machine Learning Techniques. Computational and Mathematical Methods in Medicine. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1057930
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1057930