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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

Developing a New Score: How Machine Learning Improves Risk Prediction
Composite risk scores have been used for decades to identify disease risk and health status in the general population. However, current approaches often fail to identify people who would benefit from intervention or recommend unnecessary intervention. Machine learning promises to improve accuracy, ensuring targeted treatment for patients that need it and reducing unnecessary intervention. Framingham Risk Score, the gold standard for predicting the likelihood of heart disease, predicts hospitalizations with about 56% accuracy. It uses factors such as age, gender, smoking, cholesterol levels, and systolic blood pressure to...
Source: MDDI - November 17, 2017 Category: Medical Devices Authors: Heather R. Johnson Tags: R & D Source Type: news

Deep learning approach for diabetes prediction using PIMA Indian dataset
ConclusionThe outcome of the study confirms that DL provides the best results with the most promising extracted features. DL achieves the accuracy of 98.07% which can be used for further development of the automatic prognosis tool. The accuracy of the DL approach can further be enhanced by including the omics data for prediction of the onset of the disease.
Source: Journal of Diabetes and Metabolic Disorders - April 13, 2020 Category: Endocrinology Source Type: research

Risk of cardiovascular events in patients having had acute calcium pyrophosphate crystal arthritis
Conclusions Acute CPP crystal arthritis was significantly associated with elevated short and long-term risk for non-fatal CV event.
Source: Annals of the Rheumatic Diseases - August 11, 2022 Category: Rheumatology Authors: Tedeschi, S. K., Huang, W., Yoshida, K., Solomon, D. H. Tags: ARD, Crystal arthropathies Source Type: research