SRDA: Mobile Sensing based Fluid Overload Detection for End Stage Kidney Disease Patients using Sensor Relation Dual Autoencoder

Mingyu Tang, Jiechao Gao, Guimin Dong, Carl Yang, Bradford Campbell, Brendan Bowman, Jamie Marie Zoellner, Emaad Abdel-Rahman, Mehdi Boukhechba
Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:133-146, 2023.

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 this Paper

BibTeX
@InProceedings{pmlr-v209-tang23b, 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}, pages = {133--146}, year = {2023}, editor = {Mortazavi, Bobak J. and Sarker, Tasmie and Beam, Andrew and Ho, Joyce C.}, volume = {209}, series = {Proceedings of Machine Learning Research}, month = {22 Jun--24 Jun}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v209/tang23b/tang23b.pdf}, url = {https://proceedings.mlr.press/v209/tang23b.html}, 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.} }
Endnote
%0 Conference Paper %T SRDA: Mobile Sensing based Fluid Overload Detection for End Stage Kidney Disease Patients using Sensor Relation Dual Autoencoder %A Mingyu Tang %A Jiechao Gao %A Guimin Dong %A Carl Yang %A Bradford Campbell %A Brendan Bowman %A Jamie Marie Zoellner %A Emaad Abdel-Rahman %A Mehdi Boukhechba %B Proceedings of the Conference on Health, Inference, and Learning %C Proceedings of Machine Learning Research %D 2023 %E Bobak J. Mortazavi %E Tasmie Sarker %E Andrew Beam %E Joyce C. Ho %F pmlr-v209-tang23b %I PMLR %P 133--146 %U https://proceedings.mlr.press/v209/tang23b.html %V 209 %X 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.
APA
Tang, M., Gao, J., Dong, G., Yang, C., Campbell, B., Bowman, B., Zoellner, J.M., Abdel-Rahman, E. & Boukhechba, M.. (2023). 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, in Proceedings of Machine Learning Research 209:133-146 Available from https://proceedings.mlr.press/v209/tang23b.html.

Related Material