@inproceedings{add56cbd6bc9427fb4601951b53a21b2,
title = "Numerical prediction for Systolic Blood Pressure in Intradialytic Hypotension Using Time-relevant RNN Models",
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%.",
author = "Tung, {Nai Yun} and Hu, {Hsiang Wei} and Chi, {Hsin Yin} and Chen, {Kuan Yu} and Sung, {Junne Ming} and Liu, {Kuan Hung} and Zachary Boyce and Lin, {Chou Ching} and David Law and Yu, {Chang Chia} and Chen, {Chen Ying} and Lin, {Hsuan Ming}",
note = "Publisher Copyright: {\textcopyright} 2021 ECBIOS 2021. All rights reserved.; 3rd IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2021 ; Conference date: 28-05-2021 Through 30-05-2021",
year = "2021",
doi = "10.1109/ECBIOS51820.2021.9510228",
language = "English",
series = "3rd IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "57--59",
editor = "Teen-Hang Meen",
booktitle = "3rd IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2021",
address = "United States",
}