证明模型复杂性:评估转移学习与在麻醉下进行手术内感觉监测的经典模型相比
Chanseo Lee1,2, Jaihyoung Lee1,2, Kimon-Aristotelis Vogt2,3
1Yale School of Medicine, New Haven, Connecticut, United States of America.
PloS one
|February 17, 2026
概括
经典的机器学习模型用于手术期间的感觉检测,表现优于深度学习. 简单的,可解释的模型在外科手术环境中提供了更好的准确性,效率和操作简单性.
科学领域:
- 麻醉学和外科手术期间的医学.
- 医疗保健中的机器学习
- 计算生理学计算生理学
背景情况:
- 准确的手术内感觉检测对于优化止痛和改善患者的治疗结果至关重要.
- 深度学习模型为复杂的生理动力学提供了潜力,但由于外科手术环境的复杂性,可能不会在临床上有益.
研究的目的:
- 将经典的监督模型与深度学习时间卷积网络 (TCN) 进行比较,用于手术内感知感觉检测.
- 为了评估模型性能,概率校准,集合策略和计算成本在一个现实的周围操作数据集.
主要方法:
- 对L1-规范化后勤回归和随机森林与TCN转移学习框架进行对比评估.
- 利用了101个成年人手术病例,其中30个生理和18个药物剂量特征在5秒间隔取样.
- 评估模型使用假期-一次手术-交叉验证,AUROC,AUPRC,概率校准和计算成本分析.
主要成果:
- 与TCN (AUROC 0.649,AUPRC 0.311) 相比,药物意识的随机森林获得了更好的歧视 (AUROC 0.716,AUPRC 0.399).
- TCN个性化产生了不一致的收益;同位素校准改善了概率校准而不影响歧视.
- 随机森林的计算成本显著降低 (操作较少,训练更快),尽管TCN的内存足迹较小.
结论:
- 可解释的经典模型在没有个性化的情况下匹配或超过了深度学习性能,提供了计算优势.
- 这些发现突显了在外科手术期间机器学习中证明模型复杂性的必要性.
- 经典方法为手术内感知监测提供了准确性,可解释性和操作效率的良好平衡.
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