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  4. Internal and External Validation of a Deep Learning-Based Early Warning System of Cardiac Arrest with Variable-Length and Irregularly Measured Time Series Data
 
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Internal and External Validation of a Deep Learning-Based Early Warning System of Cardiac Arrest with Variable-Length and Irregularly Measured Time Series Data

Journal
Journal of Healthcare Informatics Research
Date Issued
2025-01-01
Author(s)
Wang, Jyun-Yi
Hsu, Su-Yin
Sun, Jen-Tang
Ko, Chia-Hsin
CHIEN-HUA HUANG  
CHU-LIN TSAI  
LI-CHEN FU  
DOI
2-s2.0-85217254324
DOI
10.1007/s41666-025-00188-7
URI
https://scholars.lib.ntu.edu.tw/handle/123456789/725780
Abstract
The early detection of cardiac arrest (CA) in emergency departments (EDs) is crucial for patient safety. However, existing deep-learning research often neglects irregular time intervals between measurements and the challenge of performance degradation in short sequences. The limited accessibility of medical data further complicates the external validation of models. To address these issues, we developed a deep learning-based early warning system accommodating variable-length and irregularly measured time series data. Our system includes three models: A Time Mask Temporal Convolutional Network (TM-TCN) incorporates a missing value mask to address the problem of missing values in multivariate time series, and univariate time series with time intervals are used to ensure that the model can detect the rapid deterioration of patients. Finally, we use a designed fusion method to enable the system to make better predictions for short sequence samples. Our system achieved an area under the receiver operating characteristic curve (AUROC) of 0.9831 and an area under the precision-recall curve (AUPRC) of 0.2150 in the experiment of 8 h before CA on the National Taiwan University Hospital dataset. In the external validation, the proposed system achieved an AUROC of 0.9734 and an AUPRC of 0.1336 8 h before CA on the Far Eastern Memorial Hospital dataset and obtained an AUROC of 0.8428 and an AUPRC of 0.0533 0 to 8 h before CA on the MIMIC-IV-ED dataset. These results demonstrate the system’s reliability and adaptability across datasets, highlighting its potential to advance healthcare informatics research by addressing critical challenges in time series data modeling.
Publisher
Springer Science and Business Media Deutschland GmbH
Type
journal article

臺大位居世界頂尖大學之列,為永久珍藏及向國際展現本校豐碩的研究成果及學術能量,圖書館整合機構典藏(NTUR)與學術庫(AH)不同功能平台,成為臺大學術典藏NTU scholars。期能整合研究能量、促進交流合作、保存學術產出、推廣研究成果。

To permanently archive and promote researcher profiles and scholarly works, Library integrates the services of “NTU Repository” with “Academic Hub” to form NTU Scholars.

總館學科館員 (Main Library)
醫學圖書館學科館員 (Medical Library)
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開放取用是從使用者角度提升資訊取用性的社會運動,應用在學術研究上是透過將研究著作公開供使用者自由取閱,以促進學術傳播及因應期刊訂購費用逐年攀升。同時可加速研究發展、提升研究影響力,NTU Scholars即為本校的開放取用典藏(OA Archive)平台。(點選深入了解OA)

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