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

研究成果: Conference contribution

2 引文 斯高帕斯(Scopus)

摘要

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%.

原文English
主出版物標題3rd IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2021
編輯Teen-Hang Meen
發行者Institute of Electrical and Electronics Engineers Inc.
頁面57-59
頁數3
ISBN(電子)9781728193045
DOIs
出版狀態Published - 2021
事件3rd IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2021 - Tainan, Taiwan
持續時間: 2021 5月 282021 5月 30

出版系列

名字3rd IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2021

Conference

Conference3rd IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2021
國家/地區Taiwan
城市Tainan
期間21-05-2821-05-30

All Science Journal Classification (ASJC) codes

  • 策略與管理
  • 可再生能源、永續發展與環境
  • 生物醫學工程
  • 安全、風險、可靠性和品質
  • 建築與營造
  • 健康政策
  • 健康(社會科學)

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