øurs: Mobile Sensing based Fluid Overload Detection for End Stage Kidney Disease Patients using _Sensor _Relation _Dual _Autoencoder

Mingyue Tang, Jiechao Gao, Guimin Dong, Carl Yang, Bradford Campbell, Brendan Bowman, Jamie Marie Zoellner, Emaad Abdel-Rahman, Mehdi Boukhechba

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.