Predicting atrial fibrillation in primary care using machine learning - Hill, Nathan R.; Ayoubkhani, Daniel; McEwan, Phil; Sugrue, Daniel M.; Farooqui, Usman; Lister, Steven; Lumley, Matthew; Bakhai, Ameet; Cohen, Alexander T.; O’Neill, Mark; Clifton, David; Gordon, Jason (PloS one, 2019)
Keywords: Adult; Age Factors; Aged; Analysis; Antihypertensive Agents - therapeutic use; Antihypertensives; Arrhythmia; Artificial intelligence; Artificial neural networks; Atrial fibrillation; Atrial Fibrillation - diagnosis; Atrial Fibrillation - etiology; Audio frequencies; Biology and Life Sciences; Blood Pressure; Blood pressure measurement; Body mass; Body Mass Index; Body size; Cardiac arrhythmia; Cardiovascular agents; Cardiovascular disease; Cardiovascular diseases; Cardiovascular Diseases - complications; Clinical medicine; Complications; Computer and Information Sciences; Diagnosis; Electronic health records; Evaluation; Female; Fibrillation; Forests; Health care; Health economics; Health risks; Heart; Heart failure; Humans; Learning algorithms; Machine Learning; Male; Mathematical models; Medical research; Medicine; Medicine and Health Sciences; Middle Aged; Mortality; Neural networks; Neural Networks, Computer; Novels; Patients; Performance prediction; Pharmaceuticals; Prediction models; Pressure measurement; Primary care; Primary Health Care - methods; Regression analysis; Retrospective Studies; Risk analysis; Risk assessment; Risk Assessment - methods; Risk Factors; Statistical analysis; Stroke; Studies; Support vector machines; United Kingdom; United Kingdom--UK