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使用循环神经网络和电子健康记录预测慢性病进展情况

Yitan Zhu¹, Dehua Bi², Milda Saunders³

¹Computing, Environment and Life Sciences, Argonne National Laboratory, 9700 S Cass Ave, Lemont, IL, 60439, USA. yitan.zhu@anl.gov.

Scientific reports

|December 12, 2023

概括

循环神经网络模型使用电子健康记录准确预测慢性病 (CKD) 的进展. 这种方法增强了早期检测,以更好地照顾患者,管理慢性脏病.

科学领域:

腎臟病學 (nephrology) 是一種醫學專業.
人工智能的人工智能
生物医学信息学生物医学信息学

背景情况:

慢性病 (CKD) 是一种影响功能的渐进性疾病.
早期识别患有CKD进展高风险的患者对于及时干预至关重要.
当前的预测模型可能无法充分利用纵向患者数据.

研究的目的:

开发和评估一个用于预测CKD进展的循环神经网络 (RNN) 模型.
通过使用纵向电子健康记录 (EHR) 评估模型的性能.
将RNN模型与已建立的预测方法进行比较.

主要方法:

创建了一个管道来处理纵向EHR数据.
经常性神经网络 (RNN) 模型被训练来预测CKD进展 (II/III到IV/V阶段).
模型性能使用接收器操作特征曲线 (AUROC) 下的面积和交叉验证进行了评估.

主要成果:

一个仅使用时间序列估计的膜过率 (eGFR) 的RNN模型实现了0.957.5的AUROC.
纳入额外的临床变量改善了AUROC,达到0.967.
与考克斯模型,随机森林和LightGBM相比,RNN模型表现出优异的性能,具有稳定的高精度 (AUROC std dev < 0.01).

结论:

开发的RNN模型有效地使用纵向EHR数据预测CKD进展.
时间序列的eGFR测量对CKD进展具有很高的预测性.
这种RNN方法为CKD患者的临床风险评估提供了一个强大的和可访问的工具.

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