用于实时预测透析中低血压的深度学习模型。
Deep Learning Model for Real-Time Prediction of Intradialytic Hypotension.
机构信息
Department of Intelligence and Information, Seoul National University, Seoul, Korea.
Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea.
出版信息
Clin J Am Soc Nephrol. 2021 Mar 8;16(3):396-406. doi: 10.2215/CJN.09280620. Epub 2021 Feb 11.
BACKGROUND AND OBJECTIVES
Intradialytic hypotension has high clinical significance. However, predicting it using conventional statistical models may be difficult because several factors have interactive and complex effects on the risk. Herein, we applied a deep learning model (recurrent neural network) to predict the risk of intradialytic hypotension using a timestamp-bearing dataset.
DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We obtained 261,647 hemodialysis sessions with 1,600,531 independent timestamps (., time-varying vital signs) and randomly divided them into training (70%), validation (5%), calibration (5%), and testing (20%) sets. Intradialytic hypotension was defined when nadir systolic BP was <90 mm Hg (termed intradialytic hypotension 1) or when a decrease in systolic BP ≥20 mm Hg and/or a decrease in mean arterial pressure ≥10 mm Hg on the basis of the initial BPs (termed intradialytic hypotension 2) or prediction time BPs (termed intradialytic hypotension 3) occurred within 1 hour. The area under the receiver operating characteristic curves, the area under the precision-recall curves, and F1 scores obtained using the recurrent neural network model were compared with those obtained using multilayer perceptron, Light Gradient Boosting Machine, and logistic regression models.
RESULTS
The recurrent neural network model for predicting intradialytic hypotension 1 achieved an area under the receiver operating characteristic curve of 0.94 (95% confidence intervals, 0.94 to 0.94), which was higher than those obtained using the other models (<0.001). The recurrent neural network model for predicting intradialytic hypotension 2 and intradialytic hypotension 3 achieved area under the receiver operating characteristic curves of 0.87 (interquartile range, 0.87-0.87) and 0.79 (interquartile range, 0.79-0.79), respectively, which were also higher than those obtained using the other models (≤0.001). The area under the precision-recall curve and F1 score were higher using the recurrent neural network model than they were using the other models. The recurrent neural network models for intradialytic hypotension were highly calibrated.
CONCLUSIONS
Our deep learning model can be used to predict the real-time risk of intradialytic hypotension.
背景与目的
透析中低血压具有重要的临床意义。然而，使用传统的统计模型预测它可能具有挑战性，因为有几个因素对风险具有相互作用和复杂的影响。在此，我们应用深度学习模型（递归神经网络）使用带有时间戳的数据来预测透析中低血压的风险。
设计、设置、参与者和测量：我们获得了 261647 次血液透析治疗，其中有 1600531 个独立的时间戳（即随时间变化的生命体征），并将其随机分为训练集（70%）、验证集（5%）、校准集（5%）和测试集（20%）。当最低收缩压<90mmHg 时定义为透析中低血压 1（称为透析中低血压 1），或当收缩压下降≥20mmHg 和/或平均动脉压下降≥10mmHg 时基于初始 BP（称为透析中低血压 2）或预测时间 BP（称为透析中低血压 3）在 1 小时内发生。递归神经网络模型的接收者操作特征曲线下面积、精确召回曲线下面积和 F1 评分与多层感知器、Light Gradient Boosting Machine 和逻辑回归模型的相应评分进行了比较。
结果
用于预测透析中低血压 1 的递归神经网络模型的接收者操作特征曲线下面积为 0.94（95%置信区间，0.94-0.94），高于其他模型（<0.001）。用于预测透析中低血压 2 和透析中低血压 3 的递归神经网络模型的接收者操作特征曲线下面积分别为 0.87（四分位间距，0.87-0.87）和 0.79（四分位间距，0.79-0.79），也高于其他模型（≤0.001）。使用递归神经网络模型的精确召回曲线下面积和 F1 评分高于其他模型。透析中低血压的递归神经网络模型具有高度校准性。
结论
我们的深度学习模型可用于预测透析中低血压的实时风险。
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