使用可解释的深度学习模型,实时双重预测日内分析性低血压和高血压
Donghwan Yun1,2, Hyun-Lim Yang3,4, Seong Geun Kim5
1Department of Internal Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
Scientific reports
|October 23, 2023
概括
一个新的深度学习模型准确地预测血液透析患者的内透析性低血压和高血压. 这种可解释的模型识别了诸如年龄和血压等关键因素,用于实时事件预测.
科学领域:
- 科和人工智能的人工智能
- 临床信息学和预测建模
背景情况:
- 透析内低血压 (IDH) 和高血压 (IDHTN) 是血液透析患者的严重并发症,与不良结果有关.
- 现有的方法缺乏对IDH和IDHTN同时进行实时预测的能力.
研究的目的:
- 开发和验证一个可解释的深度学习模型,用于IDH和IDHTN的同时实时预测.
- 将开发模型的预测性能与其他机器学习方法进行比较.
主要方法:
- 基于时间融合变压器 (TFT) 的深度学习模型是使用来自11,110名患者的302,774次血液透析会议来开发的.
- 该模型经过电子健康记录数据的训练和验证,预测了IDH (定义为低至最低的缩血压<90 mmHg或显著的血压下降) 和IDHTN (缩血压增加≥10 mmHg).
- 模型性能使用接收器运行特征曲线 (AUROC) 下的区域和精度召回曲线 (AUPRC) 下的区域进行了评估,并与RNN,GBM,随机森林和后勤回归进行了比较.
主要成果:
- 基于TFT的模型表现出卓越的性能,实现了高AUROC:IDH-1的0.953,IDH-2的0.892和IDHTN的0.889.
- 该模型的AUPRC也超过了预测这些事件的其他机器学习模型.
- 发现的关键预测因素包括患者年龄,之前的治疗数据,静脉压和透析期间的时间.
结论:
- 开发的可解释的基于TFT的模型有效地预测了实时的内透析性低血压和高血压.
- 该模型提供了对促成因素的宝贵见解,增强了血液透析患者的临床决策.
- 该研究强调了先进的人工智能在改善患者安全和慢性病管理结果方面的潜力.
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