SOURCE-LINKED INTELLIGENCE
AI-based detection of worsening heart failure from low-resolution telemonitoring data
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 irreg
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-24T12:57:12.000Z
First collected: 2026-09-25T06:12:46.948Z. This is not the publication date.