AIMC Topic: Emergency Service, Hospital

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Early short-term prediction of emergency department length of stay using natural language processing for low-acuity outpatients.

The American journal of emergency medicine Mar 10, 2020
BACKGROUND: Low-acuity outpatients constitute the majority of emergency department (ED) patients, and these patients often experience an unpredictable length of stay (LOS). Effective LOS prediction might improve the quality of ED care and reduce ED c...
Adult Male Middle Aged Length of Stay Humans Retrospective Studies Female Aged Vital Signs Outpatients Crowding Clinical Decision Rules Taiwan Natural Language Processing Emergency Service, Hospital Patient Acuity
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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 Mar 4, 2020
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...
Female Male Middle Aged Deep Learning Artificial Intelligence Algorithms Retrospective Studies Republic of Korea Critical Care Cohort Studies Emergency Medical Services Triage Emergency Service, Hospital Humans
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HarborBot: A Chatbot for Social Needs Screening.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
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...
Social Determinants of Health Surveys and Questionnaires Humans Comprehension Mass Screening Artificial Intelligence Health Literacy Emergency Service, Hospital Software Attitude to Computers Patient Satisfaction
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Using machine learning to classify suicide attempt history among youth in medical care settings.

Journal of affective disorders Feb 28, 2020
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.
Male Suicide, Attempted Adolescent Suicidal Ideation Emergency Service, Hospital Self-Injurious Behavior Pennsylvania Algorithms Female Risk Factors Cross-Sectional Studies Humans Machine Learning
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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 Feb 21, 2020
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 ...
Humans Neural Networks, Computer Neoplasms Cohort Studies Ontario Logistic Models Emergency Service, Hospital
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Artificial Intelligence in Emergency Medicine: Surmountable Barriers With Revolutionary Potential.

Annals of emergency medicine Feb 21, 2020
Humans Neural Networks, Computer Artificial Intelligence Emergency Medicine Triage Emergency Service, Hospital Delivery of Health Care Patient-Centered Care United States Food and Drug Administration Deep Learning Canada United States
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Mixed-integer optimization approach to learning association rules for unplanned ICU transfer.

Artificial intelligence in medicine Jan 30, 2020
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...
Hospitals, Teaching Severity of Illness Index Time Factors Humans Logistic Models Age Factors Machine Learning Models, Theoretical Emergency Service, Hospital Critical Illness Quality Improvement Intensive Care Units Patient Transfer
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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 Dec 30, 2019
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...
Adolescent Child, Preschool Child Logistic Models United States Socioeconomic Factors Triage Natural Language Processing Tomography, X-Ray Computed Female Male Infant Humans Radiography Emergency Service, Hospital Health Care Surveys Patient Acceptance of Health Care
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A validation of machine learning-based risk scores in the prehospital setting.

PloS one Dec 13, 2019
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...
Middle Aged Risk Assessment ROC Curve Triage Humans Sweden Emergency Service, Hospital Hospitalization Machine Learning Aged Critical Care Prospective Studies Male Needs Assessment Female Ambulances
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Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review.

Artificial intelligence in medicine Nov 17, 2019
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...
Humans Emergency Medical Services Artificial Intelligence Triage Machine Learning Emergency Service, Hospital Decision Support Systems, Clinical
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