Clinical decision making: A framework for predicting Rx response

Aarti Sathyanarayana, Jyotishman Pathak, Rozalina McCoy, Santiago Romero-Brufau, Maryam Panaziahar, Jaideep Srivastava

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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%.

Original languageEnglish (US)
Title of host publicationProceedings - 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014
EditorsZhi-Hua Zhou, Wei Wang, Ravi Kumar, Hannu Toivonen, Jian Pei, Joshua Zhexue Huang, Xindong Wu
PublisherIEEE Computer Society
Pages1185-1188
Number of pages4
EditionJanuary
ISBN (Electronic)9781479942749
DOIs
StatePublished - Jan 26 2015
Event14th IEEE International Conference on Data Mining Workshops, ICDMW 2014 - Shenzhen, China
Duration: Dec 14 2014 → …

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
NumberJanuary
Volume2015-January
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Other

Other14th IEEE International Conference on Data Mining Workshops, ICDMW 2014
CountryChina
CityShenzhen
Period12/14/14 → …

Keywords

  • Decision support systems
  • Medical information systems
  • electronic medical records
  • fuzzy logic
  • predictive models
  • supervised learning
  • support vector machines

Cite this

Sathyanarayana, A., Pathak, J., McCoy, R., Romero-Brufau, S., Panaziahar, M., & Srivastava, J. (2015). Clinical decision making: A framework for predicting Rx response. In Z-H. Zhou, W. Wang, R. Kumar, H. Toivonen, J. Pei, J. Zhexue Huang, & X. Wu (Eds.), Proceedings - 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014 (January ed., pp. 1185-1188). [7022730] (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2015-January, No. January). IEEE Computer Society. https://doi.org/10.1109/ICDMW.2014.154