標題: Prediction of Patient-Controlled Analgesic Consumption: A Multimodel Regression Tree Approach
作者: Hu, Yuh-Jyh
Ku, Tien-Hsiung
Yang, Yu-Hung
Shen, Jia-Ying
分子醫學與生物工程研究所
生醫工程研究所
Institute of Molecular Medicine and Bioengineering
Institute of Biomedical Engineering
關鍵字: Classification;clustering;patient-controlled analgesia;prediction;regression
公開日期: 1-Jan-2018
摘要: 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.
URI: http://dx.doi.org/10.1109/JBHI.2017.2668393
http://hdl.handle.net/11536/144340
ISSN: 2168-2194
DOI: 10.1109/JBHI.2017.2668393
期刊: IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Volume: 22
起始頁: 265
結束頁: 275
Appears in Collections:Articles