Abstract
Predicting the effects of the blood thinner warfarin is very difficult because of its long half-life, interaction with drugs and food, and because every patient has a unique response to a given dose. Previous attempts to use machine learning have shown that no individual learner can accurately predict the drug’s effect for all patients. In this paper we present our exploration of this problem using ensemble methods. The resulting system utilizes multiple ML algorithms and input parameters to make multiple predictions, which are then scrutinized by the doctor. Our results indicate that this approach may be a workable solution to the problem of automated warfarin prescription.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Gallus, A., Baker, R., Chong, B., Ockelford, P., Street, A.: Consensus guidelines for warfarin therapy. Medical Journal of Australia 172, 600–605 (2000)
Roland, M., Tozer, T.: Clinical Pharmacokinetics: Concepts and Applications. Williams and Wilkins, Philadelphia (1995)
Gage, B.F., Fihn, S.D., White, R.H.: Management and dosing of warfarin therapy. The American Journal of Medicine 5, 211–228 (2000)
Jaffer, A., Bragg, L.: Practical tips for warfarin dosing and monitoring. Cleveland Clinic Journal of Medicine 70, 361–371 (2003)
Dalere, G.: A graphical nomogram for warfarin dosage adjustment. Pharmacology 19, 461–467 (1999)
Schaufele, M.K., Marciello, M.A., Burke, D.T.: Dosing practices of physicians for anticoagulation with warfarin during inpatient rehabilitation. American Journal of Physical Medication and Rehabilitation 79, 69–74 (2000)
Floares, A.G., Floares, C., Cucu, M., Marian, M., Lazar, L.: Optimal drug dosage regimens in cancer chemotherapy with neural networks. Journal of Clinical Oncology 22, 2134 (2004)
Narayanan, M., Lucas, S.: A genetic algorithm to improve a neural network to predict a patient’s response to warfarin. Methods of Information in Medicine 32, 55–58 (1993)
Byrne, S., Cunningham, P., Barry, A., Graham, I., Delaney, T., Corrigan, O.I.: Using neural nets for decision support in prescription and outcome prediction in anticoagulation drug therapy. In: Lavrac, N., Miksch, S. (eds.) Fifth Workshop on Intelligent Data Analysis in Medicine and Pharmacology, Berlin (2000)
Wall, R., Cunningham, P., Walsh, P., Byrne, S.: Explaining the output of ensembles in mdeical decision support on a case by case basis. Artificial Intelligence in Medicine 28, 191–206 (2003)
Breiman, L.: Bagging predictors. Machine Learning 26, 123–140 (1996)
Wolpert, D.H.: Stacked generalisation. Neural Networks 5, 241–259 (1992)
Witten, I.H., Frank, E.: Data mining. Morgan Kaufman, San Francisco (2000)
Martin, B.: Instance-based learning: nearest neighbour with generalisation. In: Computer Science. University of Waikato, Hamilton (1995)
Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martin, B., Filipovic, M., Rennie, L., Shaw, D. (2010). Using Machine Learning to Prescribe Warfarin. In: Dicheva, D., Dochev, D. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2010. Lecture Notes in Computer Science(), vol 6304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15431-7_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-15431-7_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15430-0
Online ISBN: 978-3-642-15431-7
eBook Packages: Computer ScienceComputer Science (R0)