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Acute Coronary Syndrome

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Artificial Intelligence for Diagnosis of Acute Coronary Syndromes: A Meta-analysis of Machine Learning Approaches.

The Canadian journal of cardiology
BACKGROUND: Machine learning (ML) encompasses a wide variety of methods by which artificial intelligence learns to perform tasks when exposed to data. Although detection of myocardial infarction has been facilitated with introduction of troponins, th...

Choosing Clinical Variables for Risk Stratification Post-Acute Coronary Syndrome.

Scientific reports
Most risk stratification methods use expert opinion to identify a fixed number of clinical variables that have prognostic significance. In this study our goal was to develop improved metrics that utilize a variable number of input parameters. We firs...

Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram.

Nature communications
Prompt identification of acute coronary syndrome is a challenge in clinical practice. The 12-lead electrocardiogram (ECG) is readily available during initial patient evaluation, but current rule-based interpretation approaches lack sufficient accurac...

Machine Learning Improves the Identification of Individuals With Higher Morbidity and Avoidable Health Costs After Acute Coronary Syndromes.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
OBJECTIVES: Traditional risk scores improved the definition of the initial therapeutic strategy in acute coronary syndrome (ACS), but they were not designed for predicting long-term individual risks and costs. In parallel, attempts to directly predic...

Concerns for management of STEMI patients in the COVID-19 era: a paradox phenomenon.

Journal of thrombosis and thrombolysis
The pandemic of coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern. During this time, the management of people with acute coronary syndromes (ACS) and COVID-19 has become a global issue, especially since...

Treatment effect prediction with adversarial deep learning using electronic health records.

BMC medical informatics and decision making
BACKGROUND: Treatment effect prediction (TEP) plays an important role in disease management by ensuring that the expected clinical outcomes are obtained after performing specialized and sophisticated treatments on patients given their personalized cl...

In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department.

Journal of the American Heart Association
Background Classical ST-T waveform changes on standard 12-lead ECG have limited sensitivity in detecting acute coronary syndrome (ACS) in the emergency department. Numerous novel ECG features have been previously proposed to augment clinicians' decis...

Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets.

Lancet (London, England)
BACKGROUND: The accuracy of current prediction tools for ischaemic and bleeding events after an acute coronary syndrome (ACS) remains insufficient for individualised patient management strategies. We developed a machine learning-based risk stratifica...