AIMC Topic: Coronary Disease

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Deep Learning-Based Assessment of Built Environment From Satellite Images and Cardiometabolic Disease Prevalence.

JAMA cardiology
IMPORTANCE: Built environment plays an important role in development of cardiovascular disease. Large scale, pragmatic evaluation of built environment has been limited owing to scarce data and inconsistent data quality.

Coronary heart disease classification using deep learning approach with feature selection for improved accuracy.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Coronary heart disease (CHD) is one of the deadliest diseases and a risk prediction model for cardiovascular conditions is needed. Due to the huge number of features that lead to heart problems, it is often difficult for an expert to eval...

Coronary heart disease prediction based on hybrid deep learning.

The Review of scientific instruments
Machine learning provides increasingly reliable assistance for medical experts in diagnosing coronary heart disease. This study proposes a deep learning hybrid model based coronary heart disease (CAD) prediction method, which can significantly improv...

[A deep-learning model for the assessment of coronary heart disease and related risk factors via the evaluation of retinal fundus photographs].

Zhonghua xin xue guan bing za zhi
To develop and validate a deep learning model based on fundus photos for the identification of coronary heart disease (CHD) and associated risk factors. Subjects aged>18 years with complete clinical examination data from 149 hospitals and medical e...

On-chip photonic diffractive optical neural network based on a spatial domain electromagnetic propagation model.

Optics express
An integrated physical diffractive optical neural network (DONN) is proposed based on a standard silicon-on-insulator (SOI) substrate. This DONN has compact structure and can realize the function of machine learning with whole-passive fully-optical m...

Web-Based Decision Support System for Coronary Heart Disease Diagnosis.

Advances in experimental medicine and biology
Coronary heart disease is a serious and common disease that affects a large part of the population. There is a tendency to use machine learning techniques for the punctual and valid diagnosis, which can determine the effectiveness of treatment and th...

Supporting Real World Decision Making in Coronary Diseases Using Machine Learning.

Inquiry : a journal of medical care organization, provision and financing
Cardiovascular diseases are one of the leading global causes of death. Following the positive experiences with machine learning in medicine we performed a study in which we assessed how machine learning can support decision making regarding coronary ...

Coronary heart disease diagnosis by artificial neural networks including aortic pulse wave velocity index and clinical parameters.

Journal of hypertension
BACKGROUND: Cardiovascular disease, such as coronary heart disease (CHD), are the main cause of mortality and morbidity worldwide. CHD is not entirely predicted by classic risk factors; however, they are preventable. Facing this major problem, the de...

Serum Aldosterone as Predictor of Progression of Coronary Heart Disease in Patients Without Signs of Heart Failure After Acute Myocardial Infarction.

Medical archives (Sarajevo, Bosnia and Herzegovina)
INTRODUCTION: In patients with acute myocardial infarction (AMI) early risk assessment of development of complications is of great importance. It is proven that aldosterone level has a major role in progression of cardiovascular pathology.

[Clinical medication characteristics of Shuxuening injection in treatment of cerebral infarction research based on registration].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica
To understand the medication characteristics of Xingxue@Shuxuening solutions in the real world, multi-center, large-sample-size registration design method is adopted in this study. Between October 2012 and October 2015, hospitalized patients in 27 me...