AIMC Topic: Risk Factors

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Prediction of perioperative transfusions using an artificial neural network.

PloS one
BACKGROUND: Accurate prediction of operative transfusions is essential for resource allocation and identifying patients at risk of postoperative adverse events. This research examines the efficacy of using artificial neural networks (ANNs) to predict...

Artificial intelligence in abdominal aortic aneurysm.

Journal of vascular surgery
OBJECTIVE: Abdominal aortic aneurysm (AAA) is a life-threatening disease, and the only curative treatment relies on open or endovascular repair. The decision to treat relies on the evaluation of the risk of AAA growth and rupture, which can be diffic...

Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records.

Translational psychiatry
Suicide is the tenth leading cause of death in the United States (US). An early-warning system (EWS) for suicide attempt could prove valuable for identifying those at risk of suicide attempts, and analyzing the contribution of repeated attempts to th...

Deep Learning-Based Quantification of Epicardial Adipose Tissue Volume and Attenuation Predicts Major Adverse Cardiovascular Events in Asymptomatic Subjects.

Circulation. Cardiovascular imaging
BACKGROUND: Epicardial adipose tissue (EAT) volume (cm) and attenuation (Hounsfield units) may predict major adverse cardiovascular events (MACE). We aimed to evaluate the prognostic value of fully automated deep learning-based EAT volume and attenua...

Vitamin D insufficiency is associated with subclinical atherosclerosis in HIV-1-infected patients on combination antiretroviral therapy.

HIV research & clinical practice
Vitamin D insufficiency has been associated with faster progression of atherosclerosis and increased cardiovascular disease risk, but limited data are available in HIV-infected people. So, we examined potential correlation between vitamin D status a...

The Prediction of Human Abdominal Adiposity Based on the Combination of a Particle Swarm Algorithm and Support Vector Machine.

International journal of environmental research and public health
: Abdominal adiposity is an important risk factor of chronic cardiovascular diseases, thus the prediction of abdominal adiposity and obesity can reduce the risks of contracting such diseases. However, the current prediction models display low accurac...

Machine Learning to Predict Stent Restenosis Based on Daily Demographic, Clinical, and Angiographic Characteristics.

The Canadian journal of cardiology
BACKGROUND: Machine learning (ML) has arrived in medicine to deliver individually adapted medical care. This study sought to use ML to discriminate stent restenosis (SR) compared with existing predictive scores of SR. To develop an easily applicable ...

Deep learning risk assessment models for predicting progression of radiographic medial joint space loss over a 48-MONTH follow-up period.

Osteoarthritis and cartilage
OBJECTIVE: To develop and evaluate deep learning (DL) risk assessment models for predicting the progression of radiographic medial joint space loss using baseline knee X-rays.

Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy.

Critical care (London, England)
BACKGROUND: Previous scoring models such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) and the Sequential Organ Failure Assessment (SOFA) scoring systems do not adequately predict mortality of patients undergoing co...

Virtual genetic diagnosis for familial hypercholesterolemia powered by machine learning.

European journal of preventive cardiology
AIMS: Familial hypercholesterolemia (FH) is the most common genetic disorder of lipid metabolism. The gold standard for FH diagnosis is genetic testing, available, however, only in selected university hospitals. Clinical scores - for example, the Dut...