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 language||American English|
|Title of host publication||Proceedings of the World Congress on New Technologies (NewTech 2015)|
|Subtitle of host publication||World Congress on New Technologies|
|Number of pages||9|
|State||Published - 2015|