SRDA: Mobile Sensing Based Fluid Overload Detection for End Stage Kidney Disease Patients Using Sensor Relation Dual Autoencoder
Abstract
Chronic kidney disease (CKD) is a life-threatening and prevalent disease. CKD patients, especially end- stage kidney disease (ESKD) patients on hemodial- ysis, suffer from kidney failures and are unable to remove excessive fluid, causing fluid overload and multiple morbidities including death. Current so- lutions for fluid overtake monitoring such as ultra- sonography and biomarkers assessment are cumber- some, discontinuous, and can only be performed in the clinic. In this paper, we propose SRDA, a latent graph learning powered fluid overload detection sys- tem based on Sensor Relation Dual Autoencoder to detect excessive fluid consumption of EKSD patients based on passively collected bio-behavioral data from smartwatch sensors. Experiments using real- world mobile sensing data indicate that SRDA outper- forms the state-of-the-art baselines in both F1 score and recall, and demonstrate the potential of ubiqui- tous sensing for ESKD fluid intake management.
Cite
Text
Tang et al. "SRDA: Mobile Sensing Based Fluid Overload Detection for End Stage Kidney Disease Patients Using Sensor Relation Dual Autoencoder." Proceedings of the Conference on Health, Inference, and Learning, 2023.Markdown
[Tang et al. "SRDA: Mobile Sensing Based Fluid Overload Detection for End Stage Kidney Disease Patients Using Sensor Relation Dual Autoencoder." Proceedings of the Conference on Health, Inference, and Learning, 2023.](https://mlanthology.org/chil/2023/tang2023chil-srda/)BibTeX
@inproceedings{tang2023chil-srda,
title = {{SRDA: Mobile Sensing Based Fluid Overload Detection for End Stage Kidney Disease Patients Using Sensor Relation Dual Autoencoder}},
author = {Tang, Mingyu and Gao, Jiechao and Dong, Guimin and Yang, Carl and Campbell, Bradford and Bowman, Brendan and Zoellner, Jamie Marie and Abdel-Rahman, Emaad and Boukhechba, Mehdi},
booktitle = {Proceedings of the Conference on Health, Inference, and Learning},
year = {2023},
pages = {133-146},
volume = {209},
url = {https://mlanthology.org/chil/2023/tang2023chil-srda/}
}