{'fraction_non_alphanumeric': 0.02108433734939759, 'fraction_numerical': 0.004518072289156626, 'mean_word_length': 5.268867924528302, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Risk prediction of new AF: is there a role for artificial intelligence?

Atrial fibrillation (AF) is the most common cardiac arrhythmia encountered in clinical practice. AF conveys significant health risks, most notably from a fivefold increased risk of stroke. Strokes attributable to AF are of a greater severity, cause greater disability and mortality, and result in greater healthcare costs than non-AF related strokes. Approximately 40% of people with AF are asymptomatic and are described as having ‘silent’ AF. It is therefore unsurprising that over 12% of people are found to have AF at the first presentation of a stroke. Early identification and treatment of AF before the occurrence of stroke is, therefore, of paramount importance to improve population health. AF screening has been recommended but is yet to be implemented. Recommendations currently suggest screening in patients aged 65 years or older but the yield of interventions for detecting new AF varies considerably according to the age of populations screened and the interventions tested. Identifying those at high risk of developing new AF, including those with ‘silent AF’, would enable better targeting of screening interventions and consequently has the potential to improve the effectiveness and cost-effectiveness of future AF screening programmes.