Clinical decision making: A framework for predicting Rx response

Over seventy percent of Americans take at least one form of prescription medication, with twenty percent taking more than five. The numbers emphasize how important it is for clinicians to understand the effects of the medication and whether these medications are effective. In this paper we propose a data driven framework to predict the effectiveness of medication on a patient, specifically in the case of diabetes. Our dataset contains claims data from 1.5 million patients. A heuristic was established to evaluate the 'effectiveness' of Metformin using a set of three criteria. Decision trees and random forests were used to create prediction models on the training data and select features. The model was able to correctly predict whether a patient responded well to the medication with approximately 80% accuracy and an F1-measure of approximately 90%.