AI-based detection of worsening heart failure from low-resolution telemonitoring data

2026-09-24

AI Summary

This study addresses the challenge of early deterioration detection in heart failure remote monitoring, where data are characterized by low resolution, irregular sampling, and sparse hospitalization events. To this end, we propose TRACER, a Transformer-based model that innovatively incorporates time-aware embeddings and a contrastive pretraining strategy. The method reformulates hospitalization prediction as an event detection task and employs independent binary classifiers to enhance the capture of scarce anomalous signals. Evaluated on a real-world imbalanced dataset, TRACER accurately predicted 66.7% of pre-hospitalization timelines, significantly outperforming existing baseline models. These results demonstrate its potential to provide effective clinical early warning support for heart failure management.

Abstract

Objective: Heart failure (HF) presents a healthcare challenge due to its high comorbidity burden, aging patient population and frequent hospitalizations. Remote monitoring offers a promising approach to managing HF patients by early detection of health deterioration. Developing autonomous systems to detect signs of worsening in telemonitoring data is of interest to reduce the workload of healthcare personnel. Methods: We propose the TRACER model, a Transformer with Contrastive Event Representation, designed to predict timelines leading to rare hospitalization events in low-resolution and irregularly sampled telemonitoring data. TRACER incorporates time-aware embeddings for each biomarker, contrastive pre-training to enhance anomaly detection via representation learning, and independent binary classifiers for detection. We used measurement data containing remotely recorded biomarker sequences from 276 HF patients segmented into overlapping windows based on temporal rules, and labeled the windows based on the occurrence of HF relevant hospitalizations at the latter edge of the window. Results: TRACER was able to correctly predict 66.7% timelines leading up to HF hospitalizations in the highly imbalanced real-world dataset with an overestimation of 7.9%. Reformulating the training of TRACER as an event detection problem improved the predictive performance compared with training directly on forecasting windows, enabling more effective use of the limited hospitalization events. Conclusion: TRACER demonstrated superior performance in detecting signs of worsening status in real-world telemonitoring data compared to the other tested models. Significance: TRACER shows promise in identifying signs of clinical deterioration that allow for alerts to be generated to provide counteractive treatment in patients with HF.

Problem

• Heart Failure
• Telemonitoring
• Worsening Detection
• Hospitalization Prediction
• Low-resolution Data

Innovation

• Transformer
• Contrastive Pre-training
• Time-aware Embeddings
• Event Detection
• Telemonitoring