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Triage

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Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department.

Medicine
Early identification of high-risk septic patients in the emergency department (ED) may guide appropriate management and disposition, thereby improving outcomes. We compared the performance of machine learning models against conventional risk stratifi...

Machine learning for psychiatric patient triaging: an investigation of cascading classifiers.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Develop an approach, One-class-at-a-time, for triaging psychiatric patients using machine learning on textual patient records. Our approach aims to automate the triaging process and reduce expert effort while providing high classification ...

Quro: Facilitating User Symptom Check Using a Personalised Chatbot-Oriented Dialogue System.

Studies in health technology and informatics
Automated conversational agents built with medical applications in mind, have the potential to reduce healthcare readmissions and improve accessibility to medical knowledge. In this work, we demonstrate the development and evaluation of an automated ...

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 ...

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...

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.