Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction
Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting
electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis …
electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis …
Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association
A major focus of academia, industry, and global governmental agencies is to develop and
apply artificial intelligence and other advanced analytical tools to transform health care …
apply artificial intelligence and other advanced analytical tools to transform health care …
[HTML][HTML] Must-have qualities of clinical research on artificial intelligence and machine learning
In the field of computer science, known as artificial intelligence, algorithms imitate reasoning
tasks that are typically performed by humans. The techniques that allow machines to learn …
tasks that are typically performed by humans. The techniques that allow machines to learn …
Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model …
Purpose To recognize and address various sources of bias essential for algorithmic fairness
and trustworthiness and to contribute to a just and equitable deployment of AI in medical …
and trustworthiness and to contribute to a just and equitable deployment of AI in medical …
Emerging ECG methods for acute coronary syndrome detection: Recommendations & future opportunities
Despite being the mainstay for the initial noninvasive assessment of patients with
symptomatic coronary artery disease, the 12‑lead ECG remains a suboptimal diagnostic tool …
symptomatic coronary artery disease, the 12‑lead ECG remains a suboptimal diagnostic tool …
Continuous ECG monitoring should be the heart of bedside AI-based predictive analytics monitoring for early detection of clinical deterioration
OJ Monfredi, CC Moore, BA Sullivan… - Journal of …, 2023 - Elsevier
The idea that we can detect subacute potentially catastrophic illness earlier by using
statistical models trained on clinical data is now well-established. We review evidence that …
statistical models trained on clinical data is now well-established. We review evidence that …
Improving cardiovascular risk prediction through machine learning modelling of irregularly repeated electronic health records
C Li, X Liu, P Shen, Y Sun, T Zhou… - … Heart Journal-Digital …, 2024 - academic.oup.com
Aims Existing electronic health records (EHRs) often consist of abundant but irregular
longitudinal measurements of risk factors. In this study, we aim to leverage such data to …
longitudinal measurements of risk factors. In this study, we aim to leverage such data to …
[HTML][HTML] Big data in oncology nursing research: state of the science
Objective To review the state of oncology nursing science as it pertains to big data. The
authors aim to define and characterize big data, describe key considerations for accessing …
authors aim to define and characterize big data, describe key considerations for accessing …
[PDF][PDF] Machine learning in transfusion medicine: A scoping review
Blood transfusion is a routine medical procedure in hospitals with over 2 million blood
products transfused in the UK every year at a cost of over£ 300 million and a median …
products transfused in the UK every year at a cost of over£ 300 million and a median …
Integrating multimodal information in machine learning for classifying acute myocardial infarction
Objective. Prompt identification and recognization of myocardial ischemia/infarction (MI) is
the most important goal in the management of acute coronary syndrome. The 12-lead …
the most important goal in the management of acute coronary syndrome. The 12-lead …