title: Revisiting Warfarin Dosing Using Machine Learning Techniques.
abstract: 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.
authors.affiliation: Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Room 4209, SEL West Building, 950 South Halsted Street, Chicago, IL 60607, USA.
authors.forename: Ashkan
authors.initials: A
authors.lastname: Sharabiani
authors.affiliation: Department of Pharmacotherapy, University of Utah, 30 South 2000 East, Room 4929, Salt Lake City, UT 84112, USA.
authors.forename: Adam
authors.initials: A
authors.lastname: Bress
authors.affiliation: Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Room 4209, SEL West Building, 950 South Halsted Street, Chicago, IL 60607, USA.
authors.forename: Elnaz
authors.initials: E
authors.lastname: Douzali
authors.affiliation: Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Room 2055, ERF Building, 842 W Taylor Street, Chicago, IL 60607, USA.
authors.forename: Houshang
authors.initials: H
authors.lastname: Darabi
mesh_terms: D000465:Algorithms; D000925:Anticoagulants / Q000008:administration & dosage*; D016208:Databases, Factual; D004305:Dose-Response Relationship, Drug; D004334:Drug Administration Schedule; D005189:False Positive Reactions; D005260:Female; D005838:Genotype; D006801:Humans; D000069550:Machine Learning; D008297:Male; D010597:Pharmacogenetics; D012044:Regression Analysis; D015203:Reproducibility of Results; D014859:Warfarin / Q000008:administration & dosage*
chemical_list: D000925:Anticoagulants; D014859:Warfarin