AIMC Topic: Cardiovascular Diseases

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Application of a machine learning-driven, multibiomarker panel for prediction of incident cardiovascular events in patients with suspected myocardial infarction.

Biomarkers in medicine May 28, 2020
In patients with suspected myocardial infarction (MI), we sought to validate a machine learning-driven, multibiomarker panel for prediction of incident major adverse cardiovascular events (MACE). A previously described prognostic panel for MACE con...
Cardiovascular Diseases Middle Aged Myocardial Infarction Prognosis Female Aged Male Humans Machine Learning Biomarkers
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Deep learning for predicting the occurrence of cardiopulmonary diseases in Nanjing, China.

Chemosphere May 27, 2020
The efficiency of disease prevention and medical care service necessitated the prediction of incidence. However, predictive accuracy and power were largely impeded in a complex system including multiple environmental stressors and health outcome of w...
Humans Air Pollutants Air Pollution Particulate Matter Seasons Cardiovascular Diseases Deep Learning China Incidence Nitrogen Dioxide
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Machine Learning to Support Hemodynamic Intervention in the Neonatal Intensive Care Unit.

Clinics in perinatology May 20, 2020
Hemodynamic support in neonatal intensive care is directed at maintaining cardiovascular wellbeing. At present, monitoring of vital signs plays an essential role in augmenting care in a reactive manner. By applying machine learning techniques, a mode...
Hemodynamic Monitoring Intensive Care Units, Neonatal Cardiovascular Physiological Phenomena Neonatal Sepsis Diagnostic Techniques, Cardiovascular Homeostasis Cerebrovascular Circulation Humans Machine Learning Infant, Newborn Cardiovascular Diseases Shock, Septic Infant, Premature
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Machine-learning based exploration of determinants of gray matter volume in the KORA-MRI study.

Scientific reports May 20, 2020
To identify the most important factors that impact brain volume, while accounting for potential collinearity, we used a data-driven machine-learning approach. Gray Matter Volume (GMV) was derived from magnetic resonance imaging (3T, FLAIR) and adjust...
Diabetes Mellitus, Type 2 Glomerular Filtration Rate Follow-Up Studies Magnetic Resonance Imaging Male Cardiovascular Diseases Middle Aged Case-Control Studies Gray Matter Prediabetic State Life Style Confounding Factors, Epidemiologic Models, Neurological Organ Size Linear Models Germany Machine Learning Humans Prospective Studies Female Cross-Sectional Studies
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Integrating the STOP-BANG Score and Clinical Data to Predict Cardiovascular Events After Infarction: A Machine Learning Study.

Chest Apr 25, 2020
BACKGROUND: OSA conveys worse clinical outcomes in patients with coronary artery disease. The STOP-BANG score is a simple tool that evaluates the risk of OSA and can be added to the large number of clinical variables and scores that are obtained duri...
Aged Male Cardiovascular Diseases Middle Aged Risk Assessment Humans Myocardial Infarction Machine Learning Sleep Apnea, Obstructive Prospective Studies Female
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Disease prediction via Bayesian hyperparameter optimization and ensemble learning.

BMC research notes Apr 10, 2020
OBJECTIVE: Early disease screening and diagnosis are important for improving patient survival. Thus, identifying early predictive features of disease is necessary. This paper presents a comprehensive comparative analysis of different Machine Learning...
Models, Biological Humans Biopsy, Fine-Needle Sensitivity and Specificity Machine Learning Diagnostic Techniques and Procedures Breast Neoplasms Female Cardiovascular Diseases Bayes Theorem
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LEAP: Using machine learning to support variant classification in a clinical setting.

Human mutation Apr 1, 2020
Advances in genome sequencing have led to a tremendous increase in the discovery of novel missense variants, but evidence for determining clinical significance can be limited or conflicting. Here, we present Learning from Evidence to Assess Pathogeni...
Area Under Curve Mutation, Missense Humans Genomics Logistic Models Models, Genetic Machine Learning Models, Statistical Cardiovascular Diseases Neoplasms ROC Curve
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Association of Cardiovascular Mortality and Deep Learning-Funduscopic Atherosclerosis Score derived from Retinal Fundus Images.

American journal of ophthalmology Mar 25, 2020
PURPOSE: The prediction of atherosclerosis using retinal fundus images and deep learning has not been shown possible. The purpose of this study was to develop a deep learning model which predicted atherosclerosis by using retinal fundus images and to...
Carotid Arteries Prognosis Ophthalmoscopy Retrospective Studies Republic of Korea Female Humans Predictive Value of Tests Survival Rate Risk Factors Male Aged Risk Assessment Atherosclerosis Cardiovascular Diseases Aged, 80 and over Deep Learning Fundus Oculi Middle Aged Adult Ultrasonography Retina Follow-Up Studies
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Applications of artificial intelligence in multimodality cardiovascular imaging: A state-of-the-art review.

Progress in cardiovascular diseases Mar 19, 2020
There has been a tidal wave of recent interest in artificial intelligence (AI), machine learning and deep learning approaches in cardiovascular (CV) medicine. In the era of modern medicine, AI and electronic health records hold the promise to improve...
Humans Machine Learning Reproducibility of Results Cardiovascular Diseases Diagnosis, Computer-Assisted Deep Learning Support Vector Machine Multimodal Imaging Predictive Value of Tests Image Interpretation, Computer-Assisted
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A health informatics transformation model based on intelligent cloud computing - exemplified by type 2 diabetes mellitus with related cardiovascular diseases.

Computer methods and programs in biomedicine Feb 25, 2020
BACKGROUND AND OBJECTIVE: Many studies regarding health analysis request structured datasets but the legacy resources provide scattered data. This study aims to establish a health informatics transformation model (HITM) based upon intelligent cloud c...
Cloud Computing Humans Algorithms Artificial Intelligence Cardiovascular Diseases Machine Learning Diabetes Mellitus, Type 2 Medical Informatics
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