Clinical decision making: A framework for predicting Rx response

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

doi:10.1109/ICDMW.2014.154

Clinical decision making: A framework for predicting Rx response

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

Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	Proceedings - 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014
Editors	Zhi-Hua Zhou, Wei Wang, Ravi Kumar, Hannu Toivonen, Jian Pei, Joshua Zhexue Huang, Xindong Wu
Publisher	IEEE Computer Society
Pages	1185-1188
Number of pages	4
Edition	January
ISBN (Electronic)	9781479942749
DOIs	https://doi.org/10.1109/ICDMW.2014.154
State	Published - Jan 26 2015
Event	14th IEEE International Conference on Data Mining Workshops, ICDMW 2014 - Shenzhen, China Duration: Dec 14 2014 → …

Publication series

Name	IEEE International Conference on Data Mining Workshops, ICDMW
Number	January
Volume	2015-January
ISSN (Print)	2375-9232
ISSN (Electronic)	2375-9259

Other

Other	14th IEEE International Conference on Data Mining Workshops, ICDMW 2014
Country/Territory	China
City	Shenzhen
Period	12/14/14 → …

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

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

Publisher link

10.1109/ICDMW.2014.154

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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). Article 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

Clinical decision making: A framework for predicting Rx response. / Sathyanarayana, Aarti; Pathak, Jyotishman; McCoy, Rozalina et al.
Proceedings - 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014. ed. / Zhi-Hua Zhou; Wei Wang; Ravi Kumar; Hannu Toivonen; Jian Pei; Joshua Zhexue Huang; Xindong Wu. January. ed. IEEE Computer Society, 2015. p. 1185-1188 7022730 (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2015-January, No. January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 edn, 7022730, IEEE International Conference on Data Mining Workshops, ICDMW, no. January, vol. 2015-January, IEEE Computer Society, pp. 1185-1188, 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014, Shenzhen, China, 12/14/14. https://doi.org/10.1109/ICDMW.2014.154

Sathyanarayana A, Pathak J, McCoy R, Romero-Brufau S, Panaziahar M, Srivastava J. Clinical decision making: A framework for predicting Rx response. In Zhou ZH, Wang W, Kumar R, Toivonen H, Pei J, Zhexue Huang J, Wu X, editors, Proceedings - 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014. January ed. IEEE Computer Society. 2015. p. 1185-1188. 7022730. (IEEE International Conference on Data Mining Workshops, ICDMW; January). doi: 10.1109/ICDMW.2014.154

Sathyanarayana, Aarti ; Pathak, Jyotishman ; McCoy, Rozalina et al. / Clinical decision making : A framework for predicting Rx response. Proceedings - 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014. editor / Zhi-Hua Zhou ; Wei Wang ; Ravi Kumar ; Hannu Toivonen ; Jian Pei ; Joshua Zhexue Huang ; Xindong Wu. January. ed. IEEE Computer Society, 2015. pp. 1185-1188 (IEEE International Conference on Data Mining Workshops, ICDMW; January).

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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\%.",

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AU - Panaziahar, Maryam

AU - Srivastava, Jaideep

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N2 - 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%.

AB - 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%.

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KW - electronic medical records

KW - fuzzy logic

KW - predictive models

KW - supervised learning

KW - support vector machines

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ER -

Clinical decision making: A framework for predicting Rx response

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