Clinician-in-the-Loop Smart Home System to Detect Urinary Tract Infection Flare-Ups via Uncertainty-Aware Decision Support

TL;DR

This paper introduces a clinician-in-the-loop smart home system that uses uncertainty-aware machine learning to detect urinary tract infection flare-ups early, improving decision support for healthcare providers managing older adults.

Contribution

The study presents a novel uncertainty quantification method, Conformal-Calibrated Interval, integrated into a smart home system for improved UTI flare-up detection and clinical decision support.

Findings

Outperforms baseline methods in recall and classification metrics.

Maintains low abstention proportion and narrow prediction intervals.

Survey confirms clinical utility and decision-making value.

Abstract

Urinary tract infection (UTI) flare-ups pose a significant health risk for older adults with chronic conditions. These infections often go unnoticed until they become severe, making early detection through innovative smart home technologies crucial. Traditional machine learning (ML) approaches relying on simple binary classification for UTI detection offer limited utility to nurses and practitioners as they lack insight into prediction uncertainty, hindering informed clinical decision-making. This paper presents a clinician-in-the-loop (CIL) smart home system that leverages ambient sensor data to extract meaningful behavioral markers, train robust predictive ML models, and calibrate them to enable uncertainty-aware decision support. The system incorporates a statistically valid uncertainty quantification method called Conformal-Calibrated Interval (CCI), which quantifies uncertainty and abstains from making predictions ("I don't know") when the ML model's confidence is low. Evaluated on real-world data from eight smart homes, our method outperforms baseline methods in recall and other classification metrics while maintaining the lowest abstention proportion and interval width. A survey of 42 nurses confirms that our system's outputs are valuable for guiding clinical decision-making, underscoring their practical utility in improving informed decisions and effectively managing UTIs and other condition flare-ups in older adults.