Hydration Monitoring Using Urinary Biomarkers: A Hybrid Classical Quantum Predictive Modeling Framework

TL;DR

This paper explores hybrid classical-quantum machine learning models for predicting hydration status from urinary biomarkers, demonstrating potential and limitations of quantum approaches in digital health monitoring.

Contribution

It introduces a modular Quantum Sequential Model (QSM) for hydration prediction, combining classical and quantum machine learning techniques.

Findings

Quantum models show promise but are limited by current hardware capabilities.

QSM architecture offers flexible hybrid quantum-classical predictive pipelines.

Classical models perform comparably to quantum approaches in this context.

Abstract

Hydration status is a key physiological indicator associated with cellular homeostasis, renal function, and overall health. Recent advances in smart sensing environments enable passive monitoring of urinary biomarkers that can provide continuous insight into hydration dynamics. In this work, we investigate predictive modeling approaches for hydration monitoring using biomarker data collected through the Predict Health Toilet (PHT) system. The problem is formulated as a regression task using urinary indicators such as urine specific gravity, conductivity, and volume. We evaluate classical machine learning models and quantum machine learning architectures based on variational quantum circuits. In particular, we introduce a modular Quantum Sequential Model (QSM) designed to construct flexible hybrid quantum classical predictive pipelines. Experimental results compare classical regression models, symmetry-constrained quantum regressors, and QSM architectures. The results provide insights into the potential role of quantum machine learning in digital health monitoring systems and highlight the opportunities and current limitations of near-term quantum computing for physiological data analysis.