识别帕金森病初期痴呆症的关键多模式预测因素:机器学习分析和树SHAP解释
G Peggy McFall1,2, Linzy Bohn1,2, Myrlene Gee3
1Department of Psychology, University of Alberta, Edmonton, AB, Canada.
Frontiers in aging neuroscience
|July 17, 2023
概括
机器学习在帕金森病 (PD) 患者中确定了关键的痴呆风险因素. 在PD中早期检测认知衰退是可能的,使用来自运动,认知和分子领域的多模式预测器.
科学领域:
- 神经科学是一个神经科学.
- 计算生物学 计算生物学
- 老年学是指老年学的学科.
背景情况:
- 帕金森病 (PD) 患者表现出对认知障碍和痴呆症的不同进展.
- 在PD中识别早期痴呆风险因素对于及时干预至关重要.
- 纵向研究对于了解PD疾病进展至关重要.
研究的目的:
- 在最初没有痴呆的帕金森病患者中识别和解释痴呆的主要预测因素.
- 应用机器学习和可解释的人工智能用于早期痴呆风险检测.
- 区分患有痴呆症的PD患者和没有痴呆症的PD患者.
主要方法:
- 使用了48名帕金森病患者的3年纵向样本.
- 采用随机森林 (RF) 分类器模型来区分PD无痴呆症 (PDND) 和PD初发性痴呆症 (PDID) 组.
- 应用Tree SHapley添加式扩展 (Tree SHAP) 值用于解释预测者的重要性和方向.
主要成果:
- 一个射频模型在PDID和PDND之间取得了很好的区分 (AUC = 0.84).
- 十个主要预测因素占该模型预测能力的62.5%.
- 关键预测因素包括运动 (步态),认知 (Trail A),分子 (代谢物),人口 (年龄),成像 (心室体积) 和生活方式 (ADLs) 的因素.
结论:
- 集成RF和Tree SHAP的数据驱动协议有效地识别和解释PD中的早期痴呆风险因素.
- 在PD中,主要的痴呆症预测因子来自多个互补的风险领域.
- 这种方法有助于理解和潜在地减轻帕金森病的认知衰退.
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