临床医生和计算方法在预测间歇性失调中不良结果的比较性能
Bharadhwaj Ravindhran1, Arthur Lim1, Sean Pymer1
1Academic Vascular Surgical Unit, Hull York Medical School, Hull, UK.
Annals of vascular surgery
|May 16, 2025
概括
机器学习 (ML) 模型在预测间歇性关节病患者心血管和四肢事件方面明显优于逻辑回归和临床医生. 机器学习模型表现出卓越的准确性和预测性能,捕捉复杂的变量关联.
科学领域:
- 血管医学 血管医学
- 生物统计学 生物统计学
- 医疗保健中的人工智能
背景情况:
- 机器学习 (ML) 在预测间歇性关节病患者的不良心血管和四肢事件方面表现有前途.
- 这项研究是第一个直接比较ML预测性能与传统的逻辑回归 (LR) 和临床医生的判断.
研究的目的:
- 将ML模型的预测性能与后勤回归 (LR) 和临床医生进行比较.
- 评估不同模型预测慢性四肢威胁性缺血症 (CLTI) 和主要心血管或四肢不良事件的能力.
主要方法:
- 使用了99名患者的匿名数据集,具有27个基线特征.
- 使用接收器操作特征曲线 (AUC) 下面的面积,F1得分和Brier得分来评估预测性能.
- 基于最小绝对收缩和选择运算符 (LASSO) 的ML模型与LR和8名临床医生的预测进行了比较.
主要成果:
- 在所有预测结果 (CLTI,主要不良心血管事件,主要不良肢体事件) 中,ML显著优于LR和临床医生.
- 与LR (0.74-0.808) 和临床医生 (0.611-0.677) 相比,ML获得了更高的AUC值 (0.885-0.963).
- 与LR (Brier: 0.10-0.22, F1: 0.61-0.72) 和临床医生 (Brier: >0.31, F1: 0.50-0.59) 相比,ML在较低的Brier分数 (0.03-0.07) 和更高的F1分数 (0.80-0.86) 中表现出更高的准确性.
结论:
- 基于ML的预测模型在管理间歇性音方面显著优于传统的回归和临床医生的判断.
- ML的优势来自于它能够捕捉患者变量之间的复杂,非线性关联.
- 这些发现突显了ML在改善血管疾病风险分层和患者结局方面的潜力.
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