AIMC Topic: Emergency Service, Hospital
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Use of deep learning model for paediatric elbow radiograph binomial classification: initial experience, performance and lessons learnt.
Singapore medical journal
Nov 29, 2023
INTRODUCTION: In this study, we aimed to compare the performance of a convolutional neural network (CNN)-based deep learning model that was trained on a dataset of normal and abnormal paediatric elbow radiographs with that of paediatric emergency dep...
Application of a Deep Learning System to Detect Papilledema on Nonmydriatic Ocular Fundus Photographs in an Emergency Department.
American journal of ophthalmology
Nov 4, 2023
PURPOSE: The Fundus photography vs Ophthalmoscopy Trial Outcomes in the Emergency Department (FOTO-ED) studies showed that ED providers poorly recognized funduscopic findings in patients in the ED. We tested a modified version of the Brain and Optic ...
Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text data.
Health care management science
Nov 3, 2023
Overcrowding of emergency departments is a global concern, leading to numerous negative consequences. This study aimed to develop a useful and inexpensive tool derived from electronic medical records that supports clinical decision-making and can be ...
Detecting changes in the performance of a clinical machine learning tool over time.
EBioMedicine
Oct 2, 2023
BACKGROUND: Excessive use of blood cultures (BCs) in Emergency Departments (EDs) results in low yields and high contamination rates, associated with increased antibiotic use and unnecessary diagnostics. Our team previously developed and validated a m...
Generative Artificial Intelligence for Chest Radiograph Interpretation in the Emergency Department.
JAMA network open
Oct 2, 2023
IMPORTANCE: Multimodal generative artificial intelligence (AI) methodologies have the potential to optimize emergency department care by producing draft radiology reports from input images.
Beyond SEP-1 Compliance: Assessing the Impact of Antibiotic Overtreatment and Fluid Overload in Suspected Septic Patients.
The Journal of emergency medicine
Sep 15, 2023
BACKGROUND: The Centers for Medicare and Medicaid Services (CMS) developed the Severe Sepsis and Septic Shock Performance Measure bundle (SEP-1) metric to improve sepsis care, but evidence supporting this bundle is limited and harms secondary to comp...
Improved patient mortality predictions in emergency departments with deep learning data-synthesis and ensemble models.
Scientific reports
Sep 12, 2023
The triage process in emergency departments (EDs) relies on the subjective assessment of medical practitioners, making it unreliable in certain aspects. There is a need for a more accurate and objective algorithm to determine the urgency of patients....
ChatGPT and Generating a Differential Diagnosis Early in an Emergency Department Presentation.
Annals of emergency medicine
Sep 9, 2023
An Artificial Intelligence Model for Predicting Trauma Mortality Among Emergency Department Patients in South Korea: Retrospective Cohort Study.
Journal of medical Internet research
Aug 29, 2023
BACKGROUND: Within the trauma system, the emergency department (ED) is the hospital's first contact and is vital for allocating medical resources. However, there is generally limited information about patients that die in the ED.
Criticality and clinical department prediction of ED patients using machine learning based on heterogeneous medical data.
Computers in biology and medicine
Aug 28, 2023
PROBLEM: Emergency triage faces multiple challenges, including limited medical resources and inadequate manual triage nurses, which cause incorrect triage, overcrowding in the emergency department (ED), and long patient waiting time.
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