@inproceedings{4aaad724711a41ccab1c1f1ea9c78867,
title = "Differencing Time Series as an Important Feature Extraction for Intradialytic Hypotension Prediction using Machine Learning",
abstract = "Intradialytic hypotension (IDH) needs a real-time early warning system. Thus, the goal of the research is to design time-series differences of the features of IDH to increase the performance of the warning system. We created two new features called the time-relevant difference. These features were calculated by the current value minus the previous three values. The result showed a sensitivity of 88.9% and a specificity of 85.1%. Using the LightGBM, the sensitivity was 73.8%, and the specificity was 67.9%. Time series differences generated new eigenvalues for the model system for training of non-RNN-type algorithms to obtain acceptable values.",
author = "Yang, {Jiun Yi} and Hu, {Hsiang Wei} and Liu, {Chih Hao} and Chen, {Kuan Yu} and Un, {Chi Hin} and Huang, {Chih Chiang} and Chen, {Chou Cheng} and Lin, {Chou Ching K.} and Hsuan Chang 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/ECBIOS51820.2021.9510749",
language = "English",
series = "3rd IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "19--20",
editor = "Teen-Hang Meen",
booktitle = "3rd IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2021",
address = "United States",
}