Numerical prediction for Systolic Blood Pressure in Intradialytic Hypotension Using Time-relevant RNN Models

Nai Yun Tung, Hsiang Wei Hu, Hsin Yin Chi, Kuan Yu Chen, Junne Ming Sung, Kuan Hung Liu, Zachary Boyce, Chou Ching Lin, David Law, Chang Chia Yu, Chen Ying Chen, Hsuan Ming Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

During hemodialysis (HD), intradialytic hypotension (IDH) is a serious complication and a major risk factor of mortality. This study aimed to use machine learning to predict IDH occurrence to improve prevention. In the proposed model in this study, we conducted Gated Recurrent Units, Deep Neural Networks, and Long-Short-term Memory models to predict SBP values. For predicting IDH, a binary classification model was established. The results showed an accuracy of 90% with a difference between the predicted and actual value of 25mm-Hg for SBP value prediction. Also, the binary classification model had a threshold of 90mm-Hg with a accuracy of 93% and a specificity of 97%.

Original languageEnglish
Title of host publication3rd IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2021
EditorsTeen-Hang Meen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages57-59
Number of pages3
ISBN (Electronic)9781728193045
DOIs
Publication statusPublished - 2021
Event3rd IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2021 - Tainan, Taiwan
Duration: 2021 May 282021 May 30

Publication series

Name3rd IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2021

Conference

Conference3rd IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2021
Country/TerritoryTaiwan
CityTainan
Period21-05-2821-05-30

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Renewable Energy, Sustainability and the Environment
  • Biomedical Engineering
  • Safety, Risk, Reliability and Quality
  • Building and Construction
  • Health Policy
  • Health(social science)

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