Copyright
©The Author(s) 2024.
World J Clin Cases. Aug 26, 2024; 12(24): 5513-5522
Published online Aug 26, 2024. doi: 10.12998/wjcc.v12.i24.5513
Published online Aug 26, 2024. doi: 10.12998/wjcc.v12.i24.5513
Variable | Factor | Assignment |
Y | Hypothermia | 1 = hypothermia; 0 = non-hypothermia |
X1 | Age | Continuous variable, original value input |
X2 | Basal body temperature | Continuous variable, original value input |
X3 | Operating room temperature | Continuous variable, original value input |
X4 | Duration of anesthesia | Continuous variable, original value input |
X5 | Operation duration | Continuous variable, original value input |
X6 | Intraoperative fluid infusion | Continuous variable, original value input |
X7 | Infusion of crystal solution | Continuous variable, original value input |
X8 | Infusion of colloidal solution | Continuous variable, original value input |
X9 | Pneumoperitoneum flow | Continuous variable, original value input |
X10 | Sex | 0 = Male; 1 = Female |
- Citation: Zhu K, Zhang ZX, Zhang M. Application value of machine learning models in predicting intraoperative hypothermia in laparoscopic surgery for polytrauma patients. World J Clin Cases 2024; 12(24): 5513-5522
- URL: https://www.wjgnet.com/2307-8960/full/v12/i24/5513.htm
- DOI: https://dx.doi.org/10.12998/wjcc.v12.i24.5513