Prediction of Patient-Controlled Analgesic Consumption: A Multimodel Regression Tree Approach

Yuh-Jyh Hu, Tien Hsiung Ku, Yu Hung Yang, Jia Ying Shen

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Several factors contribute to individual variability in postoperative pain, therefore, individuals consume postoperative analgesics at different rates. Although many statistical studies have analyzed postoperative pain and analgesic consumption, most have identified only the correlation and have not subjected the statistical model to further tests in order to evaluate its predictive accuracy. In this study involving 3052 patients, a multistrategy computational approach was developed for analgesic consumption prediction. This approach uses data on patient-controlled analgesia demand behavior over time and combines clustering, classification, and regression to mitigate the limitations of current statistical models. Cross-validation results indicated that the proposed approach significantly outperforms various existing regression methods. Moreover, a comparison between the predictions by anesthesiologists and medical specialists and those of the computational approach for an independent test data set of 60 patients further evidenced the superiority of the computational approach in predicting analgesic consumption because it produced markedly lower root mean squared errors.

Original languageEnglish
Article number7852464
Pages (from-to)265-275
Number of pages11
JournalIEEE Journal of Biomedical and Health Informatics
Issue number1
StatePublished - 1 Jan 2018


  • Classification
  • clustering
  • patient-controlled analgesia
  • prediction
  • regression

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