一个基于随机森林算法的预测模型,用于在全身麻醉下进行骨科手术后适度至严重的急性术后疼痛
Gaoxiang Shi1,2,3,4, Geliang Liu2,3,5, Qichao Gao1,2,3
1School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, China.
BMC anesthesiology
|November 6, 2023
概括
一个随机森林模型有效地预测骨科手术后中度至严重的术后疼痛. 确定的主要风险因素包括固定和手术持续时间,有助于早期干预.
科学领域:
- 整形外科手术 整形外科手术
- 疼痛管理 疼痛管理
- 临床预测建模临床预测建模
背景情况:
- 手术后的疼痛是经常出现的手术并发症.
- 对于中度至重度疼痛的早期发现和干预至关重要.
- 确定风险因素和开发预测模型是必要的.
研究的目的:
- 确定中度至重度急性术后疼痛的重大风险因素.
- 建立一个高性能临床预测模型.
- 专注于在全身麻醉下进行骨科手术.
主要方法:
- 患者被分为中度至重度疼痛 (P组) 和没有中度至重度疼痛 (N组) 组.
- 拉索回归选择的特征,由随机森林和多变量物流回归模型处理.
- 用曲线下的面积 (AUC),准确度和错误率来评估模型性能.
主要成果:
- 随机森林模型在测试组中实现了25.2%的错误率和0.810 AUC.
- 多变量物流回归模型的错误率为31.3%,AUC为0.764.
- 固定和手术持续时间是被确定的前两个风险因素.
结论:
- 随机森林模型在预测中度至严重的急性术后疼痛方面表现出有效性.
- 这种预测模型对骨科手术患者具有潜在的临床价值.
- 早期识别有风险的患者可以指导及时干预.
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