轻度认知障碍:数据驱动的预测,风险因素和工作表
Sajjad Fouladvand1, Morteza Noshad1, Mary Kane Goldstein2
1Stanford Center for Biomedical Informatics Research.
概括
机器学习模型可以使用电子健康记录提前一年预测轻度认知障碍 (MCI) 的发作. 这有助于早期识别和个性化评估痴呆风险.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 神经学 神经学
背景情况:
- 预计到2030年,痴呆症将影响超过7800万人.
- 早期发现轻度认知障碍 (MCI) 对于及时干预至关重要.
- 需要个性化临床评估来诊断潜在的可逆性认知衰退原因.
研究的目的:
- 开发机器学习模型,可以提前一年预测MCI的发病情况.
- 从电子健康记录中确定可预测MCI的关键临床因素.
- 创建一个基于数据的临床程序列表,用于MCI工作.
主要方法:
- 利用来自OMOP数据模型的现实世界电子健康记录.
- 开发和评估了后勤回归,随机森林和xgboost模型.
- 在超过531,000次患者访问中培训和测试模型.
- 采用关联挖掘技术来识别预测性临床因素和程序.
主要成果:
- 随机森林模型在预测MCI发病时实现了68.2±0.7的ROC-AUC.
- 从临床医生笔记中确定了预测MCI的显著临床因素.
- 创建了一个基于数据的MCI评估常见临床程序列表.
结论:
- 机器学习模型,特别是随机森林,在预测MCI方面表现有前途.
- 识别预测性临床因素可以提高MCI早期检测.
- 数据驱动的方法可以为MCI工作提供临床指南和订单.
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