Machine Learning Analysis of Time-Dependent Features for Predicting Adverse Events During Hemodialysis Therapy: Model Development and Validation Study

Machine Learning Analysis of Time-Dependent Features for Predicting Adverse Events During Hemodialysis Therapy: Model Development and Validation Study

BackgroundHemodialysis (HD) therapy is an indispensable tool used in critical care management. Patients undergoing HD are at risk for intradialytic adverse events, ranging from muscle cramps to cardiac arrest. So far, there is no effective HD device–integrated algorithm to as...

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Bibliographic Details
Main Authors: Yi-Shiuan Liu, Chih-Yu Yang, Ping-Fang Chiu, Hui-Chu Lin, Chung-Chuan Lo, Alan Szu-Han Lai, Chia-Chu Chang, Oscar Kuang-Sheng Lee
Format: Article
Language:English
Published: JMIR Publications 2021-09-01
Series:Journal of Medical Internet Research
Online Access:https://www.jmir.org/2021/9/e27098

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https://www.jmir.org/2021/9/e27098

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