Detecting factors associated with polypharmacy in general practitioners' prescriptions: A data mining approach

Document Type : Article

Authors

1 - Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran - Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran

2 Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran

3 Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran

Abstract

Prescribing and consuming drugs more than necessary is considered as polypharmacy, which is both wasteful and harmful. The purpose of this paper is to establish an innovative data mining framework for analyzing physicians’ prescriptions regarding polypharmacy. The approach consists of three main steps: pre-modeling, modeling, and post-modeling. In the first step, after collecting and cleaning the raw data, several novel physicians’ features are extracted. In the modeling step, two popular decision trees, i.e., C4.5 and Classification and Regression Tree (CART), are applied to generate a set of If-Then rules in a tree-shaped structure to detect and describe physicians’ features associated with polypharmacy. In a novel approach, the response surface method (RSM) as a tool for hyper-parameter tuning is simultaneously applied along with correlation-based feature selection (CFS) to enhance the performance of the algorithms. In the post-modeling step, the discovered knowledge is visualized to make the results more perceptible, then is presented to domain experts to evaluate whether they make sense or not. The framework has been applied to a real-world dataset of prescriptions. The results have been confirmed by the experts, which demonstrates the capabilities of the data mining framework in the detection and analysis of polypharmacy.

Keywords

References

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Scientia Iranica
Volume 29, Issue 6 - Serial Number 6
Transactions on Industrial Engineering (E)
November and December 2022
Pages 3489-3504

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History

  • Receive Date: 03 March 2020
  • Revise Date: 18 October 2020
  • Accept Date: 07 December 2020

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