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Patients with congestive heart failure (HF) are prone to clinical deterioration leading to hospital admissions, burdening both patients and the healthcare system. Predicting hospital admission in this patient group could enable timely intervention, with subsequent reduction of these admissions. To date, hospital admission prediction remains challenging. Increasing amounts of acquired data and development of artificial intelligence (AI) technology allow for the creation of reliable hospital prediction algorithms for HF patients. This scoping review describes the current literature on strategies and performance of AI-based algorithms for prediction of hospital admission in patients with HF.

Current state of artificial intelligence-based algorithms for hospital admission prediction in patients with heart failure: a scoping review