Utilizing graph neural networks for adverse health detection and personalized decision making in sensor-based remote monitoring for dementia care.
Journal:
Computers in biology and medicine
PMID:
39454523
Abstract
BACKGROUND: Sensor-based remote health monitoring is increasingly used to detect adverse health in people living with dementia (PLwD) at home, aiming to prevent hospitalizations and reduce caregiver burden. However, home sensor data is often noisy, overly granular, and suffers from unreliable labeling, data drift and high variability between households. Current anomaly detection methods lack generalizability and personalization, often requiring anomaly-free training data and frequent model updates.