AIMC Topic: Myocardial Infarction
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Development and validation of an artificial neural network algorithm to predict mortality and admission to hospital for heart failure after myocardial infarction: a nationwide population-based study.
The Lancet. Digital health
Jan 1, 2022
BACKGROUND: Patients have an estimated mortality of 15-20% within the first year following myocardial infarction and one in four patients who survive myocardial infarction will develop heart failure, severely reducing quality of life and increasing t...
A deep learning algorithm for detecting acute myocardial infarction.
EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology
Oct 20, 2021
BACKGROUND: Delayed diagnosis or misdiagnosis of acute myocardial infarction (AMI) is not unusual in daily practice. Since a 12-lead electrocardiogram (ECG) is crucial for the detection of AMI, a systematic algorithm to strengthen ECG interpretation ...
SGANRDA: semi-supervised generative adversarial networks for predicting circRNA-disease associations.
Briefings in bioinformatics
Sep 2, 2021
Emerging research shows that circular RNA (circRNA) plays a crucial role in the diagnosis, occurrence and prognosis of complex human diseases. Compared with traditional biological experiments, the computational method of fusing multi-source biologica...
Classifying Microscopic Acute and Old Myocardial Infarction Using Convolutional Neural Networks.
The American journal of forensic medicine and pathology
Sep 1, 2021
Convolutional neural network (CNN) has advanced in recent years and translated from research into medical practice, most notably in clinical radiology and histopathology. Research on CNNs in forensic/postmortem pathology is almost exclusive to postmo...
More than meets the eye: Using AI to identify reduced heart function by electrocardiograms.
Med (New York, N.Y.)
Jul 9, 2021
Electrocardiographic (ECG) assessment of patients with suspected heart disease is a bedrock of cardiology for diagnosing conduction system disease, arrhythmias, and heart attack. Now, using AI-assisted interpretation of ECGs, the signals within these...
Ethical issues in two parallel trials of personalised criteria for implantation of implantable cardioverter defibrillators for primary prevention: the PROFID project-a position paper.
Open heart
Jul 1, 2021
AIM: To discuss ethical issues related to a complex study (PROFID) involving the development of a new, partly artificial intelligence-based, prediction model to enable personalised decision-making about the implantation of an implantable cardioverter...
Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction.
JAMA cardiology
Jun 1, 2021
IMPORTANCE: Accurate prediction of adverse outcomes after acute myocardial infarction (AMI) can guide the triage of care services and shared decision-making, and novel methods hold promise for using existing data to generate additional insights.
Predictors of bleeding event among elderly patients with mechanical valve replacement using random forest model: A retrospective study.
Medicine
May 14, 2021
Available classification tools and risk factors predicting bleeding events in elderly patients after mechanical valve replacement may not be suitable in Asian populations. Thus, we aimed to identify an accurate model for predicting bleeding in elderl...
Machine Learning Based Risk Prediction for Major Adverse Cardiovascular Events.
Studies in health technology and informatics
May 7, 2021
BACKGROUND: Patients with major adverse cardiovascular events (MACE) such as myocardial infarction or stroke suffer from frequent hospitalizations and have high mortality rates. By identifying patients at risk at an early stage, MACE can be prevented...
Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: a prospective study.
Cardiovascular research
Dec 1, 2020
AIMS: Our aim was to evaluate the performance of machine learning (ML), integrating clinical parameters with coronary artery calcium (CAC), and automated epicardial adipose tissue (EAT) quantification, for the prediction of long-term risk of myocardi...
Time Factors
Adipose Tissue
Predictive Value of Tests
Machine Learning
Humans
Decision Support Techniques
Pericardium
Sex Factors
Coronary Angiography
Prospective Studies
Male
Prognosis
Multidetector Computed Tomography
Cause of Death
Middle Aged
Female
Vascular Calcification
Radiographic Image Interpretation, Computer-Assisted
Risk Assessment
Risk Factors
Computed Tomography Angiography
Coronary Artery Disease
Aged
Myocardial Infarction
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