Deep Learning Model for Real-Time Prediction of Intradialytic Hypotension.

ABSTRACT:

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 (i.e., 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 dec

SUBMITTER: Lee H 

PROVIDER: S-EPMC8011016 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature