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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

How to prevent diabetes from sneaking up on your patients
An AMA Viewpoints post by AMA Board Chair Stephen R. Permut, MD A major health threat has been silently taking hold of 86 million Americans, with 90 percent of them unaware of it. A new public health campaign is about to change that—and you’re the key to helping these patients take their health back. A campaign to prevent type 2 diabetes If you’re not already talking to your patients about prediabetes and the risks associated with it, it’s time to start. People with prediabetes—more than 1 in 3 adults—are at higher risk of developing serious health problems such as type 2 diabetes, heart disease and s...
Source: AMA Wire - January 21, 2016 Category: Journals (General) Authors: Amy Farouk Source Type: news

Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning
Conclusions: With statewide EHR datasets, our study prospectively validated an accurate 1-year risk prediction model for incident essential hypertension. Our real-time predictive analytic model has been deployed in the state of Maine, providing implications in interventions for hypertension and related diseases and hopefully enhancing hypertension care.
Source: Journal of Medical Internet Research - January 30, 2018 Category: General Medicine Authors: Chengyin Ye Tianyun Fu Shiying Hao Yan Zhang Oliver Wang Bo Jin Minjie Xia Modi Liu Xin Zhou Qian Wu Yanting Guo Chunqing Zhu Yu-Ming Li Devore S Culver Shaun T Alfreds Frank Stearns Karl G Sylvester Eric Widen Doff McElhinney Xuefeng Ling Source Type: research

Electronic Medical Record Risk Modeling of Cardiovascular Outcomes Among Patients with Type 2 Diabetes
ConclusionsThe Ochsner model overestimated 5-year CHD risk, but had relatively higher calibration than the other models in CHD. Risk equations fitted for local populations improved cardiovascular risk stratification for patients with T2DM. Application of machine learning simplified the models compared to “generalized” risk equations.
Source: Diabetes Therapy - June 18, 2021 Category: Endocrinology Source Type: research