arXiv:2606.10725v1 Announce Type: cross Abstract: Background. Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and a major determinant of prognosis. Established AF risk scores rely on factors (older age, hypertension) nearly ubiquitous among patients with cardiovascular disease (CVD), offering limited stratification in this high-risk group. Most target long-term (5-10 year) rather than medium-term prediction. We developed interpretable ML models predicting AF risk over a 24-month and entire follow-up horizon in CVD patients using routinely collected hospital data. Methods. Sin
The proliferation of routinely collected hospital data and advancements in interpretable machine learning are enabling more granular and actionable predictive analytics in healthcare.
This development represents a practical application of AI in healthcare, moving beyond broad risk factors to provide more precise and timely stratification for critical conditions like atrial fibrillation.
Healthcare providers will have more sophisticated tools to identify individuals at medium-term risk for AF, potentially leading to earlier interventions and improved patient outcomes.
- · Cardiology departments
- · Patients with cardiovascular disease
- · Healthcare AI developers
- · Traditional, less precise risk scoring methods
Improved AF detection and prevention strategies via early risk stratification.
Reduced healthcare costs associated with advanced AF complications due to proactive management.
Increased adoption of interpretable AI models in clinical decision support across various medical specialties.
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