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Open Access Research article

Decision tree-based learning to predict patient controlled analgesia consumption and readjustment

Yuh-Jyh Hu12*, Tien-Hsiung Ku3, Rong-Hong Jan2, Kuochen Wang2, Yu-Chee Tseng2 and Shu-Fen Yang3

Author affiliations

1 Institute of Biomedical Engineering, National Chiao Tung University, Hsinchu, Taiwan

2 Department of Computer science, National Chiao Tung University, Hsinchu, Taiwan

3 Department of Anesthesiology, Changhwa Christian Hospital, Changhwa, Taiwan

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Citation and License

BMC Medical Informatics and Decision Making 2012, 12:131  doi:10.1186/1472-6947-12-131

Published: 14 November 2012

Abstract

Background

Appropriate postoperative pain management contributes to earlier mobilization, shorter hospitalization, and reduced cost. The under treatment of pain may impede short-term recovery and have a detrimental long-term effect on health. This study focuses on Patient Controlled Analgesia (PCA), which is a delivery system for pain medication. This study proposes and demonstrates how to use machine learning and data mining techniques to predict analgesic requirements and PCA readjustment.

Methods

The sample in this study included 1099 patients. Every patient was described by 280 attributes, including the class attribute. In addition to commonly studied demographic and physiological factors, this study emphasizes attributes related to PCA. We used decision tree-based learning algorithms to predict analgesic consumption and PCA control readjustment based on the first few hours of PCA medications. We also developed a nearest neighbor-based data cleaning method to alleviate the class-imbalance problem in PCA setting readjustment prediction.

Results

The prediction accuracies of total analgesic consumption (continuous dose and PCA dose) and PCA analgesic requirement (PCA dose only) by an ensemble of decision trees were 80.9% and 73.1%, respectively. Decision tree-based learning outperformed Artificial Neural Network, Support Vector Machine, Random Forest, Rotation Forest, and Naïve Bayesian classifiers in analgesic consumption prediction. The proposed data cleaning method improved the performance of every learning method in this study of PCA setting readjustment prediction. Comparative analysis identified the informative attributes from the data mining models and compared them with the correlates of analgesic requirement reported in previous works.

Conclusion

This study presents a real-world application of data mining to anesthesiology. Unlike previous research, this study considers a wider variety of predictive factors, including PCA demands over time. We analyzed PCA patient data and conducted several experiments to evaluate the potential of applying machine-learning algorithms to assist anesthesiologists in PCA administration. Results demonstrate the feasibility of the proposed ensemble approach to postoperative pain management.

Keywords:
Classification; Data cleaning; Decision tree-based learning; Pain management; Patient Controlled Analgesia (PCA)