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.
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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
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DOI: https://doi.org/10.1007/978-3-642-15431-7_16
Publisher Name: Springer, Berlin, Heidelberg
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