与早产严重视网膜病变相关的氧化波动:来自多模式深度学习方法的见解
Wei-Chun Lin1, Brian K Jordan2, Brian Scottoline2
1Department of Ophthalmology, Oregon Health & Science University, Portland, Oregon.
氧气暴露的波动,而不仅仅是平均水平,显著影响早产性视网膜病变 (ROP) 的严重程度. 先进的AI模型使用时间序列氧气数据准确地预测中度至严重的ROP.
科学领域:
- 新生儿眼科新生儿眼科
- 儿科重症监护 儿科重症监护
- 医疗人工智能的人工智能
背景情况:
- 早产视网膜病变 (ROP) 是儿童失明的主要原因.
- 氧气治疗对早产婴儿至关重要,但其在ROP发育中的确切作用是复杂的.
- 了解氧气暴露和ROP之间的动态关系对于预防和治疗至关重要.
研究的目的:
- 调查时间依赖的氧气变量与ROP的发生率和严重程度之间的相关性.
- 评估氧气暴露变异对ROP发展的影响.
- 通过使用临床和与氧气相关的数据,开发适度至严重ROP的预测模型.
主要方法:
- 对2011年1月至2022年10月间出生的230名婴儿进行了回顾性队列研究.
- 从电子健康记录中提取时间依赖的氧气数据 (吸入氧气的分数 - FiO2).
- 利用混合效应线性模型,支持矢量机 (SVM) 和长短记忆 (LSTM) 神经网络进行分析和预测.
主要成果:
- FiO2 的每日平均值和变化系数 (COV) 都与 ROP 严重程度的增加显著相关 (P < 0.001).
- 与静态SVM模型相比,多式LSTM模型实现了更高的预测性能 (平均AUROC = 0.89 ± 0.04).
- LSTM模型的表现优于在妊娠年龄和平均FiO2.2等静态特征上训练的SVM模型.
结论:
- 除了平均水平之外,氧气暴露的变化在严重的ROP发展中起着关键作用.
- 多模式神经网络显示出分析时间序列EHR数据以预测疾病结果的前景.
- 该方法可用于使用复杂的患者数据调查其他疾病.
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