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Emergency Service, Hospital

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Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services.

Scandinavian journal of trauma, resuscitation and emergency medicine
BACKGROUND: In emergency medical services (EMSs), accurately predicting the severity of a patient's medical condition is important for the early identification of those who are vulnerable and at high-risk. In this study, we developed and validated an...

HarborBot: A Chatbot for Social Needs Screening.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Accessing patients' social needs is a critical challenge at emergency departments (EDs). However, most EDs do not have extra staff to administer screeners, and without personnel administration, response rates are low especially for low health literac...

Using machine learning to classify suicide attempt history among youth in medical care settings.

Journal of affective disorders
BACKGROUND: The current study aimed to classify recent and lifetime suicide attempt history among youth presenting to medical settings using machine learning (ML) as applied to a behavioral health screen self-report survey.

Comparing an Artificial Neural Network to Logistic Regression for Predicting ED Visit Risk Among Patients With Cancer: A Population-Based Cohort Study.

Journal of pain and symptom management
CONTEXT: Prior work using symptom burden to predict emergency department (ED) visits among patients with cancer has used traditional statistical methods such as logistic regression (LR). Machine learning approaches for prediction, such as artificial ...

Mixed-integer optimization approach to learning association rules for unplanned ICU transfer.

Artificial intelligence in medicine
After admission to emergency department (ED), patients with critical illnesses are transferred to intensive care unit (ICU) due to unexpected clinical deterioration occurrence. Identifying such unplanned ICU transfers is urgently needed for medical p...

Use of natural language processing to improve predictive models for imaging utilization in children presenting to the emergency department.

BMC medical informatics and decision making
OBJECTIVE: To examine the association between the medical imaging utilization and information related to patients' socioeconomic, demographic and clinical factors during the patients' ED visits; and to develop predictive models using these associated...

A validation of machine learning-based risk scores in the prehospital setting.

PloS one
BACKGROUND: The triage of patients in prehospital care is a difficult task, and improved risk assessment tools are needed both at the dispatch center and on the ambulance to differentiate between low- and high-risk patients. This study validates a ma...

Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review.

Artificial intelligence in medicine
MOTIVATION: Emergency Departments' (ED) modern triage systems implemented worldwide are solely based upon medical knowledge and experience. This is a limitation of these systems, since there might be hidden patterns that can be explored in big volume...

Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach.

Computational and mathematical methods in medicine
Emergency departments (EDs) play a vital role in the whole healthcare system as they are the first point of care in hospitals for urgent and critically ill patients. Therefore, effective management of hospital's ED is crucial in improving the quality...