AIMC Topic: Triage

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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 Prognosis Clinical Decision-Making Female Decision Support Techniques Databases, Factual Predictive Value of Tests Triage ROC Curve Electrocardiography Italy Syncope Cardiology Service, Hospital Area Under Curve Emergency Service, Hospital Adult Male Risk Assessment Time Factors Middle Aged Aged Aged, 80 and over Reproducibility of Results Risk Factors
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Suicide Risk Assessment in Hospitals: An Expert System-Based Triage Tool.

The Journal of clinical psychiatry Jul 1, 2016
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...
Expert Systems Models, Psychological Surveys and Questionnaires Suicide Prevention Triage Emergency Service, Hospital Risk Assessment Hospitals Male Middle Aged Humans Female Adult Cross-Sectional Studies Young Adult
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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 Apr 1, 2015
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.
Triage Algorithms Area Under Curve Retrospective Studies Emergency Medicine Humans Emergency Service, Hospital Patient Admission Neural Networks, Computer Emergency Treatment Pennsylvania Confidence Intervals Hospitalization Cohort Studies Female Aged Male Reproducibility of Results Predictive Value of Tests Aged, 80 and over Risk Assessment
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Secondary triage classification using an ensemble random forest technique.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2015
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.
Malaysia Support Vector Machine Emergency Service, Hospital Triage Age Factors Vital Signs Humans Reproducibility of Results
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