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

Mingyue Tang (University of Virginia), Jiechao Gao* (University of Virginia), Guimin Dong (Amazon), Carl Yang (Emory University), Brad Campbell (University of Virginia), Brendan Bowman (University of Virginia), Jamie Marie Zoellner (University of Virginia), Emaad Abdel-Rahman (University of Virginia), Mehdi Boukhechba (The Janssen Pharmaceutical Companies of Johnson & Johnson)

Abstract: Chronic kidney disease (CKD) is a life-threatening and prevalent disease. CKD patients, especially end-stage kidney disease (ESKD) patients on hemodialysis, suffer from kidney failures and are unable to remove excessive fluid, causing fluid overload and multiple morbidities including death. Current solutions for fluid overtake monitoring such as ultrasonography and biomarkers assessment are cumbersome, discontinuous, and can only be performed in the clinic. In this paper, we propose SRDA, a latent graph learning powered fluid overload detection system 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 outperforms the state-of-the-art baselines in both F1 score and recall, and demonstrate the potential of ubiquitous sensing for ESKD fluid intake management.