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Myocardial Infarction

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Acute myocardial infarction prognosis prediction with reliable and interpretable artificial intelligence system.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Predicting mortality after acute myocardial infarction (AMI) is crucial for timely prescription and treatment of AMI patients, but there are no appropriate AI systems for clinicians. Our primary goal is to develop a reliable and interpreta...

Machine learning-driven predictions and interventions for cardiovascular occlusions.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Cardiovascular diseases remain a leading cause of global morbidity and mortality, with heart attacks and strokes representing significant health challenges. The accurate, early diagnosis and management of these conditions are paramount in...

[Research progress of in-hospital mortality risk model in patients with acute myocardial infarction].

Zhonghua wei zhong bing ji jiu yi xue
The incidence of in-hospital death in acute myocardial infarction (AMI) is high, which seriously threatens the life and health of patients. At present, many countries and regions have established a variety of objective assessment models for predictin...

Deep learning-based prediction of heart failure rehospitalization during 6, 12, 24-month follow-ups in patients with acute myocardial infarction.

Health informatics journal
Heart failure is a clinical syndrome that occurs when the heart is too weak or stiff and cannot pump enough blood that our body needs. It is one of the most expensive diseases due to frequent hospitalizations and emergency room visits. Reducing unnec...

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
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
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
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
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...