Prediction of Patient Controlled Analgesic Consumption Using Patient Demand Behaviors

Yuh-Jyh Hu, Tien Hsiung Ku, Y.-H. Yang, J.-Y. Shen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Many factors affect individual variability in postoperative pain. Although several statistical studies have evaluated postoperative pain and analgesic consumption, previous research shows that the coefficient of determination of existing predictive models was small (e.g., R2 = 0.17–0.59 for postoperative pain, and 0.27–0.46 for postoperative analgesic consumption). This study presents the real- world application of computational models to anaesthesiology and considers a wider variety of predictive factors, including PCA demands over time. It extends previous works by proposing a 2-stage computational strategy that combines clustering and regression to predict analgesic consumption. The results of the cross validation, and the comparison with human experts have demonstrated the feasibility of the proposed computational methods.
Original languageAmerican English
Title of host publicationProceedings of the World Congress on New Technologies (NewTech 2015)
Subtitle of host publicationWorld Congress on New Technologies
Number of pages9
StatePublished - 2015


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