AI Medical Compendium Topic

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Emergency 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
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 ...

Assessing Risk for Future Firearms Violence in Young People Who Present to ED.

ED management : the monthly update on emergency department management
A new clinical index tool designed specifically for the emergency environment predicts the risk for future firearms violence in young people 14-24 years of age. The approach employs a brief, 10-point instrument that can be administered in one to two ...

Suicide Risk Assessment in Hospitals: An Expert System-Based Triage Tool.

The Journal of clinical psychiatry
BACKGROUND: The November 2010 Joint Commission Sentinel Event Alert on the prevention of suicides in medical/surgical units and the emergency department (ED) mandates screening every patient treated as an outpatient or admitted to the hospital for su...

Emergency Department Visit Forecasting and Dynamic Nursing Staff Allocation Using Machine Learning Techniques With Readily Available Open-Source Software.

Computers, informatics, nursing : CIN
Although emergency department visit forecasting can be of use for nurse staff planning, previous research has focused on models that lacked sufficient resolution and realistic error metrics for these predictions to be applied in practice. Using data ...

Evaluation of a hospital admission prediction model adding coded chief complaint data using neural network methodology.

European journal of emergency medicine : official journal of the European Society for Emergency Medicine
OBJECTIVE: Our objective was to apply neural network methodology to determine whether adding coded chief complaint (CCC) data to triage information would result in an improved hospital admission prediction model than one without CCC data.

Secondary triage classification using an ensemble random forest technique.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Triage of patients in the emergency department is a complex task based on several uncertainties and ambiguous information. Triage must be implemented within two to five minutes to avoid potential fatality and increased waiting time.