Early Detection of Patient Deterioration from Real-Time Wearable Monitoring System

TL;DR

This paper introduces TARL, a novel method leveraging shapelet-transition knowledge graphs and transition-aware embeddings to improve early detection of patient deterioration from wearable heart rate data, addressing missing data and interpretability.

Contribution

TARL is a new approach that models shapelet dynamics and missing data impact to enhance early illness detection from wearable heart rate time series.

Findings

TARL achieves high reliability in ICU patient deterioration detection.

The method demonstrates early detection capabilities compared to existing approaches.

Case studies show TARL's explanations aid clinical decision-making.

Abstract

Early detection of patient deterioration is crucial for reducing mortality rates. Heart rate data has shown promise in assessing patient health, and wearable devices offer a cost-effective solution for real-time monitoring. However, extracting meaningful insights from diverse heart rate data and handling missing values in wearable device data remain key challenges. To address these challenges, we propose TARL, an innovative approach that models the structural relationships of representative subsequences, known as shapelets, in heart rate time series. TARL creates a shapelet-transition knowledge graph to model shapelet dynamics in heart rate time series, indicating illness progression and potential future changes. We further introduce a transition-aware knowledge embedding to reinforce relationships among shapelets and quantify the impact of missing values, enabling the formulation of comprehensive heart rate representations. These representations capture explanatory structures and predict future heart rate trends, aiding early illness detection. We collaborate with physicians and nurses to gather ICU patient heart rate data from wearables and diagnostic metrics assessing illness severity for evaluating deterioration. Experiments on real-world ICU data demonstrate that TARL achieves both high reliability and early detection. A case study further showcases TARL's explainable detection process, highlighting its potential as an AI-driven tool to assist clinicians in recognizing early signs of patient deterioration.