# Consider this a WARNing

## TL;DR

This paper introduces a model called WARN that predicts atrial fibrillation in the near-term, aiming to improve preventive treatments and reduce healthcare burdens.

## Contribution

The novel contribution is the development of a model for short-term atrial fibrillation prediction using continuous Holter data.

## Key findings

- WARN is trained to predict atrial fibrillation in the timescale of minutes.
- The model could enable preventive therapies with rapid mechanisms of action.
- Algorithmic monitoring of AF risk may reduce the burden on healthcare workers.

## Abstract

Atrial fibrillation (AF) prediction can be valuable at many timescales and in many populations. In this issue of Patterns, Gavidia et al. train a model called WARN for short-term prediction of AF in the timescale of minutes in patients wearing 24-h continuous Holter electrocardiograms. The ability to predict near-term (e.g., 30 min) AF has the potential to enable preventive therapies with rapid mechanisms of action (e.g., oral anticoagulation, anti-arrhythmic drugs). In this way, efficient, continuous, and algorithmic monitoring of AF risk could reduce burden on healthcare workers and represents a valuable clinical pursuit.

Atrial fibrillation (AF) prediction can be valuable at many timescales and in many populations. In this issue of Patterns, Gavidia et al. train a model called WARN for short-term prediction of AF in the timescale of minutes in patients wearing 24-h continuous Holter electrocardiograms. The ability to predict near-term (e.g., 30 min) AF has the potential to enable preventive therapies with rapid mechanisms of action (e.g., oral anticoagulation, anti-arrhythmic drugs). In this way, efficient, continuous, and algorithmic monitoring of AF risk could reduce burden on healthcare workers and represents a valuable clinical pursuit.