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Application of Machine Learning Techniques to Identify Data Reliability and Factors Affecting Outcome After Stroke Using Electronic Administrative Records
Conclusion: Electronic administrative records from this cohort produced reliable outcome prediction and identified clinically appropriate factors negatively impacting most outcome variables following hospital admission with stroke. This presents a means of future identification of modifiable factors associated with patient discharge destination. This may potentially aid in patient selection for certain interventions and aid in better patient and clinician education regarding expected discharge outcomes.
Source: Frontiers in Neurology - September 27, 2021 Category: Neurology 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

How a public health solution is reducing hypertension disparities
Addressing health care disparities can help practices improve the health of patients in vulnerable at-risk populations. Learn how eight family medicine practices boosted hypertension control rates for diverse patients by more than 3 percentage points in just three months. A targeted pilot As part of the Million Hearts initiative, the Summit County Public Health department (SCPH) and several partners in Ohio launched a pilot project with several family medicine practices to help reduce hypertension rates among black men. In Ohio, 38.5 percent of black patients have a diagnosis of hypertension, compared to 33.7 percent...
Source: AMA Wire - February 16, 2016 Category: Journals (General) Authors: Lyndra Vassar Source Type: news

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

Consider the Promises and Challenges of Medical Image Analyses Using Machine Learning
Medical imaging saves millions of lives each year, helping doctors detect and diagnose a wide range of diseases, from cancer and appendicitis to stroke and heart disease. Because non-invasive early disease detection saves so many lives, scientific investment continues to increase. Artifical intelligence (AI) has the potential to revolutionize the medical imaging industry by sifting through mountains of scans quickly and offering providers and patients with life-changing insights into a variety of diseases, injuries, and conditions that may be hard to detect without the supplemental technology. Images are the largest source...
Source: MDDI - June 2, 2020 Category: Medical Devices Authors: Partha S. Anbil and Michael T. Ricci Tags: Imaging Source Type: news

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

Systolic blood pressure, chronic obstructive pulmonary disease and cardiovascular risk
Conclusion Using deep learning for modelling EHR, we identified a monotonic association between SBP and risk of cardiovascular events in patients with COPD.
Source: Heart - July 27, 2023 Category: Cardiology Authors: Rao, S., Nazarzadeh, M., Li, Y., Canoy, D., Mamouei, M., Salimi-Khorshidi, G., Rahimi, K. Tags: Open access Cardiac risk factors and prevention Source Type: research