AIMC Topic: Emergency Service, Hospital

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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 ...
Cohort Studies Ontario Humans Logistic Models Neural Networks, Computer Emergency Service, Hospital Neoplasms
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Artificial Intelligence in Emergency Medicine: Surmountable Barriers With Revolutionary Potential.

Annals of emergency medicine Feb 21, 2020
Triage Emergency Service, Hospital Patient-Centered Care United States Food and Drug Administration Humans Delivery of Health Care Emergency Medicine Deep Learning Neural Networks, Computer Canada Artificial Intelligence 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...
Age Factors Emergency Service, Hospital Quality Improvement Patient Transfer Severity of Illness Index Humans Hospitals, Teaching Machine Learning Time Factors Critical Illness Logistic Models Intensive Care Units Models, Theoretical
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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 Tomography, X-Ray Computed Female Male Infant Radiography Emergency Service, Hospital Health Care Surveys Patient Acceptance of Health Care United States Socioeconomic Factors Triage Natural Language Processing Humans
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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...
Ambulances Male Middle Aged Risk Assessment ROC Curve Triage Emergency Service, Hospital Critical Care Female Needs Assessment Humans Sweden Machine Learning Hospitalization Prospective Studies Aged
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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...
Emergency Service, Hospital Humans Artificial Intelligence Machine Learning Decision Support Systems, Clinical Emergency Medical Services Triage
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Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach.

Computational and mathematical methods in medicine Nov 15, 2019
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...
Algorithms Computer Simulation Reproducibility of Results Length of Stay Humans Hospital Administration Time Factors Neural Networks, Computer Emergency Treatment Emergency Service, Hospital Machine Learning Bed Occupancy Turkey Climate
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A Gradient Boosting Machine Learning Model for Predicting Early Mortality in the Emergency Department Triage: Devising a Nine-Point Triage Score.

Journal of general internal medicine Nov 1, 2019
BACKGROUND: Emergency departments (ED) are becoming increasingly overwhelmed, increasing poor outcomes. Triage scores aim to optimize the waiting time and prioritize the resource usage. Artificial intelligence (AI) algorithms offer advantages for cre...
Young Adult Adult Middle Aged Adolescent Hospital Mortality Triage Emergency Service, Hospital Machine Learning Retrospective Studies Aged Aged, 80 and over Humans Artificial Intelligence
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Machine learning for prediction of septic shock at initial triage in emergency department.

Journal of critical care Oct 22, 2019
BACKGROUND: We hypothesized utilizing machine learning (ML) algorithms for screening septic shock in ED would provide better accuracy than qSOFA or MEWS.
Aged Young Adult Adult Male Middle Aged Sepsis Hospital Mortality Humans Shock, Septic Multivariate Analysis Prognosis Machine Learning Vital Signs Electronic Health Records Support Vector Machine Software Republic of Korea Algorithms Triage Female Area Under Curve Emergency Service, Hospital
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Deep Learning for Chest Radiography in the Emergency Department.

Radiology Oct 22, 2019
Emergency Service, Hospital Thorax Deep Learning Radiography
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