Predicting atrial fibrillation in primary care using machine learning

Using a machine learning approach to predict outcome after surgery for degenerative cervical myelopathy

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Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease

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Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records

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Defining Disease Phenotypes Using National Linked Electronic Health Records: A Case Study of Atrial Fibrillation

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Simple Risk Model Predicts Incidence of Atrial Fibrillation in a Racially and Geographically Diverse Population: the CHARGE‐AF Consortium

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