机器学习模型用于预测术后疼痛在不可逆转的脉炎的情况下
Pedro Felipe de Jesus Freitas1, Ingrid Luiza Mendonça Cunha2, Thalita de Paris Matos1
1School of Dentistry, Tuiuti University of Paraná, Curitiba, Brazil.
International endodontic journal
|February 2, 2026
概括
机器学习模型可以有效地预测内牙治疗后的术后疼痛. 后勤回归和支持矢量机器算法显示出个性化疼痛管理策略的前景.
科学领域:
- 牙科 牙科是指牙科的专业.
- 机器学习 机器学习
- 疼痛管理 疼痛管理
背景情况:
- 手术后的疼痛是内牙治疗后的一个常见并发症.
- 预测和管理这种疼痛对于患者的福祉和治疗成功至关重要.
研究的目的:
- 开发和验证监督机器学习模型,用于在不可逆转的脉病例中预测内牙治疗后的术后疼痛.
- 确定影响疼痛发生的关键临床预测因素.
主要方法:
- 分析了接受内牙治疗的354名患者的前性队列.
- 八个受监督的机器学习算法被训练和验证,使用70/30训练测试分割.
- 使用曲线下的面积 (AUC),准确性,精度,回忆和F1得分来评估性能,并通过合成少数群体过量采样技术解决类不平衡.
主要成果:
- 监督的机器学习模型显示了手术后疼痛的良好预测性能.
- 后勤回归在24小时 (AUC 0.74) 实现了最佳性能,而支持向量机在72小时 (AUC 0.81) 取得了最佳表现.
- 患者的年龄和性别被确定为最重要的疼痛预测因素.
结论:
- 监督机器学习为预测术后内牙疼痛提供了一个有希望的方法.
- 后勤回归和支持矢量机器模型显示了个性化疼痛管理中的临床应用潜力.
- 这些模型可以帮助临床医生更有效地预测和管理患者的疼痛.
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