AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Coronary Care Units

Showing 1 to 10 of 13 articles

Clear Filters

Comparing ensemble learning algorithms and severity of illness scoring systems in cardiac intensive care units: a retrospective study.

Einstein (Sao Paulo, Brazil)
BACKGROUND: Beatriz Nistal-Nuño designed a machine learning system type of ensemble learning for patients undergoing cardiac surgery and intensive care unit cardiology patients, based on sequences of cardiovascular physiological measurements and othe...

Machine learning methods for developing a predictive model of the incidence of delirium in cardiac intensive care units.

Revista espanola de cardiologia (English ed.)
INTRODUCTION AND OBJECTIVES: Delirium, recognized as a crucial prognostic factor in the cardiac intensive care unit (CICU), has evolved in response to the changing demographics among critically ill cardiac patients. This study aimed to create a predi...

The premise, promise, and perils of artificial intelligence in critical care cardiology.

Progress in cardiovascular diseases
Artificial intelligence (AI) is an emerging technology with numerous healthcare applications. AI could prove particularly useful in the cardiac intensive care unit (CICU) where its capacity to analyze large datasets in real-time would assist clinicia...

Development and external validation of a dynamic risk score for early prediction of cardiogenic shock in cardiac intensive care units using machine learning.

European heart journal. Acute cardiovascular care
AIMS: Myocardial infarction and heart failure are major cardiovascular diseases that affect millions of people in the USA with morbidity and mortality being highest among patients who develop cardiogenic shock. Early recognition of cardiogenic shock ...

Unsupervised machine learning to identify subphenotypes among cardiac intensive care unit patients with heart failure.

ESC heart failure
AIMS: Hospitalized patients with heart failure (HF) are a heterogeneous population, with multiple phenotypes proposed. Prior studies have not examined the biological phenotypes of critically ill patients with HF admitted to the contemporary cardiac i...

Utility of an Echocardiographic Machine Learning Model to Predict Outcomes in Intensive Cardiac Care Unit Patients.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
INTRODUCTION: The risk stratification at admission to the intensive cardiac care unit (ICCU) is crucial and remains challenging.

Machine learning to detect recent recreational drug use in intensive cardiac care units.

Archives of cardiovascular diseases
BACKGROUND: Although recreational drug use is a strong risk factor for acute cardiovascular events, systematic testing is currently not performed in patients admitted to intensive cardiac care units, with a risk of underdetection. To address this iss...

Key Concepts in Machine Learning and Clinical Applications in the Cardiac Intensive Care Unit.

Current cardiology reports
PURPOSE OF REVIEW: Artificial Intelligence (AI) technology will significantly alter critical care cardiology, from our understanding of diseases to the way in which we communicate with patients and colleagues. We summarize the potential applications ...

Artificial Intelligence ECG Diastolic Dysfunction and Survival in Cardiac Intensive Care Unit Patients.

Journal of the American Heart Association
BACKGROUND: Left ventricular diastolic dysfunction (LVDD) predicts mortality in patients in cardiac intensive care units. An artificial intelligence enhanced ECG (AIECG) algorithm can predict LVDD and mortality in general populations but has not been...