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Predicting patient-level new-onset atrial fibrillation from population-based nationwide electronic health records: protocol of FIND-AF for developing a precision medicine prediction model using artificial intelligence
Introduction Atrial fibrillation (AF) is a major cardiovascular health problem: it is common, chronic and incurs substantial healthcare expenditure because of stroke. Oral anticoagulation reduces the risk of thromboembolic stroke in those at higher risk; but for a number of patients, stroke is the first manifestation of undetected AF. There is a rationale for the early diagnosis of AF, before the first complication occurs, but population-based screening is not recommended. Previous prediction models have been limited by their data sources and methodologies. An accurate model that uses existing routinely collected data is n...
Source: BMJ Open - November 2, 2021 Category: General Medicine Authors: Nadarajah, R., Wu, J., Frangi, A. F., Hogg, D., Cowan, C., Gale, C. Tags: Open access, Cardiovascular medicine Source Type: research

Using electronic health records to develop and validate a machine-learning tool to predict type 2 diabetes outcomes: a study protocol
Introduction Type 2 diabetes mellitus (T2DM) is a major cause of blindness, kidney failure, myocardial infarction, stroke and lower limb amputation. We are still unable, however, to accurately predict or identify which patients are at a higher risk of deterioration. Most risk stratification tools do not account for novel factors such as sociodemographic determinants, self-management ability or access to healthcare. Additionally, most tools are based in clinical trials, with limited external generalisability. Objective The aim of this work is to design and validate a machine learning-based tool to identify patients with T2...
Source: BMJ Open - July 30, 2021 Category: General Medicine Authors: Neves, A. L., Pereira Rodrigues, P., Mulla, A., Glampson, B., Willis, T., Darzi, A., Mayer, E. Tags: Open access, Health informatics Source Type: research