公共卫生 公共卫生
Xueting Ding1, Jiahui Dai2, Liner Xiang3
1Joe C Wen School of Population & Public Health, Henry and Susan Samueli College of of Health Sciences, University of California, Irvine, Irvine, CA, USA.
机器学习模型使用电子健康记录准确地预测一年的中风后痴呆风险. 这有助于识别高风险患者进行早期干预和监测.
科学领域:
- 神经学 神经学
- 数据科学数据科学数据科学
- 老年学是一门学科.
背景情况:
- 脑卒中是阿尔茨海默病和相关痴呆症 (ADRD) 的重要危险因素,在脑卒中后的第一年风险增加了三倍.
- 关于使用机器学习与真实世界的临床数据来预测中风后痴呆症的研究有限.
- 电子健康记录 (EHR) 为开发预测模型提供了宝贵的资源.
研究的目的:
- 评估各种预测建模方法,以在中风后一年内确定ADRD风险.
- 利用电子健康记录 (EHR) 进行中风后痴呆症预测.
- 将机器学习算法的性能与传统的统计方法进行比较.
主要方法:
- 从TrinetX网络提取了2018年首次中风的成年患者的EHR数据.
- 开发并比较了六种预测模型:后勤回归 (LR-Back,LR-LASSO,LR-Ridge),随机森林 (RF),LightGBM和XGBoost.
- 纳入人口统计数据,生命体征,实验室结果,并发症和药物史;使用准确度和AUC-ROC评估绩效.
主要成果:
- 在55888名中风患者中,8%的患者在一年内发展出ADRD.
- XGBoost和LightGBM模型显示出最高的预测性能,准确度超过92%和AUC约为0.83.
- 人口因素,并发症和药物使用是ADRD病例的重要预测因素.
结论:
- 机器学习和传统的统计模型可以有效地利用EHR数据预测一年的中风后痴呆风险.
- 这些预测模型可以帮助识别高风险个体,以进行有针对性的监测和早期干预.
- 进一步的研究可以完善这些模型,以便在中风幸存者护理中临床应用.
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