开发和验证可解释的机器学习模型,用于预测长期的功能结果,在老年患者瘤下arachnoid出血
Xianggan Wang1, Wei Tu2, Xiuli Li3
1Department of Neurosurgery, Yichun People's Hospital, Yichun, Jiangxi, China.
概括
机器学习模型准确地预测了12个月的功能结果,在老年患者的动脉瘤下关节出血 (aSAH). 这些可解释的工具有助于个性化临床决策和神经临床护理的资源配置.
科学领域:
- 神经科学是一个神经科学.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 由于高发病率和死亡率,患有动脉瘤下关节出血 (aSAH) 的老年患者的预后具有挑战性.
- 准确预测长期功能结果对于有效的患者管理至关重要.
研究的目的:
- 开发和外部验证可解释的机器学习 (ML) 模型,用于预测老年aSAH患者的12个月功能结果.
- 在这个患者群体中确定功能结果的关键预测因素.
主要方法:
- 使用426名老年aSAH患者的数据开发和验证8个ML模型.
- 利用后勤回归和Boruta算法来进行特征选择.
- 通过ROC/PR曲线和校准图表评估模型性能;通过SHAP分析解释性.
主要成果:
- 多层感知器 (MLP) 模型在有利的校准下实现了高性能 (ROC-AUC为0.913内部,0.912外部).
- 确定的主要预测因素包括亨特-赫斯尺度,年龄,出血量,延迟脑缺血症 (DCI) 和修改后的费舍尔尺度.
- SHAP分析使得个性化风险解释成为可能.
结论:
- 成功开发并验证了可解释的ML模型,用于预测老年aSAH患者的长期功能结果.
- 模型展示了个性化临床决策的概括性和潜力.
- 这些工具可以优化神经临床护理环境中的资源配置.
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