AIMC Topic: Cardiology Service, Hospital

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Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea.

Circulation. Arrhythmia and electrophysiology Aug 4, 2020
BACKGROUND: Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing ...
Humans Artificial Intelligence Retrospective Studies Female Reproducibility of Results Aged Male Ventricular Function, Left Stroke Volume Middle Aged Diagnosis, Computer-Assisted Electrocardiography Predictive Value of Tests Emergency Medical Services Signal Processing, Computer-Assisted Dyspnea Ventricular Dysfunction, Left Heart Failure, Systolic Systole Cardiology Service, Hospital
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Neural networks as a tool to predict syncope risk in the Emergency Department.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology Nov 1, 2017
AIMS: There is no universally accepted tool for the risk stratification of syncope patients in the Emergency Department. The aim of this study was to investigate the short-term predictive accuracy of an artificial neural network (ANN) in stratifying ...
Humans Neural Networks, Computer Decision Support Techniques Databases, Factual Prognosis Clinical Decision-Making Female Reproducibility of Results Risk Factors Aged Aged, 80 and over Adult Male Middle Aged Risk Assessment Time Factors ROC Curve Electrocardiography Predictive Value of Tests Triage Area Under Curve Emergency Service, Hospital Italy Syncope Cardiology Service, Hospital
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