AIMC Topic: Emergency Service, Hospital
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Machine learning for selecting patients with Crohn's disease for abdominopelvic computed tomography in the emergency department.
Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
Jul 9, 2021
BACKGROUND: Patients with Crohn's disease (CD) frequently undergo abdominopelvic computed tomography (APCT) in the emergency department (ED). It's essential to diagnose clinically actionable findings (CAF) as they may need immediate intervention, fre...
Remote Care Assistance in Emergency Department Based on Smart Medical.
Journal of healthcare engineering
Jun 19, 2021
Smart medical care is user-centric, medical information is the main line, and big data, Internet of Things, cloud computing, artificial intelligence, and other technologies are used to establish scientific and accurate information, as well as an effi...
Deep learning-based automated detection of pulmonary embolism on CT pulmonary angiograms: No significant effects on report communication times and patient turnaround in the emergency department nine months after technical implementation.
European journal of radiology
Jun 11, 2021
OBJECTIVES: Rapid communication of CT exams positive for pulmonary embolism (PE) is crucial for timely initiation of anticoagulation and patient outcome. It is unknown if deep learning automated detection of PE on CT Pulmonary Angiograms (CTPA) in co...
Musculoskeletal trauma and artificial intelligence: current trends and projections.
Skeletal radiology
Jun 5, 2021
Musculoskeletal trauma accounts for a significant fraction of emergency department visits and patients seeking urgent care, with a high financial cost to society. Diagnostic imaging is indispensable in the workup and management of trauma patients. Ho...
Performance evaluation of Emergency Department patient arrivals forecasting models by including meteorological and calendar information: A comparative study.
Computers in biology and medicine
Jun 3, 2021
The volume of daily patient arrivals at Emergency Departments (EDs) is unpredictable and is a significant reason of ED crowding in hospitals worldwide. Timely forecast of patients arriving at ED can help the hospital management in early planning and ...
Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study.
Journal of medical Internet research
May 27, 2021
BACKGROUND: Early detection and intervention are the key factors for improving outcomes in patients with COVID-19.
Impact of ECG Characteristics on the Performance of an Artificial Intelligence Enabled ECG for Predicting Left Ventricular Dysfunction.
Circulation. Arrhythmia and electrophysiology
May 17, 2021
Development and Validation of Machine Learning Models to Predict Admission From Emergency Department to Inpatient and Intensive Care Units.
Annals of emergency medicine
May 8, 2021
STUDY OBJECTIVE: This study aimed to develop and validate 2 machine learning models that use historical and current-visit patient data from electronic health records to predict the probability of patient admission to either an inpatient unit or ICU a...
Predicting Ambulance Patient Wait Times: A Multicenter Derivation and Validation Study.
Annals of emergency medicine
May 8, 2021
STUDY OBJECTIVE: To derive and internally and externally validate machine-learning models to predict emergency ambulance patient door-to-off-stretcher wait times that are applicable to a wide variety of emergency departments.
Assessment of an AI Aid in Detection of Adult Appendicular Skeletal Fractures by Emergency Physicians and Radiologists: A Multicenter Cross-sectional Diagnostic Study.
Radiology
May 4, 2021
Background The interpretation of radiographs suffers from an ever-increasing workload in emergency and radiology departments, while missed fractures represent up to 80% of diagnostic errors in the emergency department. Purpose To assess the performan...
Emergency Service, Hospital
Radiologists
Radiographic Image Interpretation, Computer-Assisted
Middle Aged
Adolescent
Fractures, Bone
Aged
Aged, 80 and over
Young Adult
Cross-Sectional Studies
Physicians
Male
Retrospective Studies
Adult
Female
Sensitivity and Specificity
Reproducibility of Results
Humans
Artificial Intelligence
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