整合心率变量 改善基于机器学习的恐慌症预测 症状严重性
Jin Goo Lee1,2, Jae-Jin Kim2,3, Jeong-Ho Seok2,3
1Eulji University College of Medicine, Daejeon, Korea.
概括
将心率变化 (HRV) 与心理测量尺度相结合,显著改善了机器学习对恐慌障碍 (PD) 严重性的预测. 低频HRV功率成为一个关键的预测因素.
科学领域:
- 心脏病学 心脏病学
- 精神病学是一个精神病学.
- 机器学习 机器学习
- 自主神经系统研究 自主神经系统研究
背景情况:
- 恐慌症 (PD) 通常与自主神经系统失衡有关,心率变化 (HRV) 是一个关键指标.
- 以前的研究已经探索了PD和HRV之间的关联,但预测PD严重程度仍然具有挑战性.
研究的目的:
- 评估HRV的预测能力,结合心理测量尺度,以通过机器学习来确定恐慌失调的严重程度.
- 为了比较不同特征集 (单独HRV,单独尺度和组合) 在预测PD严重性的有效性.
主要方法:
- 招募了507名被诊断患有PD的患者,提供了心理测量尺度和HRV组件的数据.
- 使用不同的输入特征组合进行了三项机器学习实验:尺度和HRV (ExSH),仅尺度 (ExS) 和仅HRV.
- 在每个实验中应用了9个机器学习模型,分析了性能指标,并使用了SHapley添加式扩展 (SHAP) 来进行特征重要性评估.
主要成果:
- 使用心理测量尺度和HRV (ExSH) 的随机森林模型实现了最高的性能,f1得分为76.50%,灵敏度为75.35%.
- 与仅使用尺度的方法 (ExS) 相比,ExSH表现出明显更高的灵敏度和f1得分.
- 对于ExSH中的随机森林模型,汉密尔顿焦虑评分表,汉密尔顿抑郁评分表和HRV的低频 (LF) 功率被确定为最重要的预测特征.
结论:
- 将HRV测量与传统的心理测量尺度相结合,可以提高机器学习模型在预测恐慌症严重程度方面的准确性.
- HRV的低频 (LF) 功率被确定为预测PD严重性的重要和有前途的生物标志物.
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