AI Medical Compendium Topic

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Emergency Service, Hospital

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Evaluating the Reliability of a Remote Acuity Prediction Tool in a Canadian Academic Emergency Department.

Annals of emergency medicine
STUDY OBJECTIVE: There is increasing interest in harnessing artificial intelligence to virtually triage patients seeking care. The objective was to examine the reliability of a virtual machine learning algorithm to remotely predict acuity scores for ...

Application of Machine Learning Techniques to Development of Emergency Medical Rapid Triage Prediction Models in Acute Care.

Computers, informatics, nursing : CIN
Given the critical and complex features of medical emergencies, it is essential to develop models that enable prompt and suitable clinical decision-making based on considerable information. Emergency nurses are responsible for categorizing and priori...

Applications of natural language processing at emergency department triage: A narrative review.

PloS one
INTRODUCTION: Natural language processing (NLP) uses various computational methods to analyse and understand human language, and has been applied to data acquired at Emergency Department (ED) triage to predict various outcomes. The objective of this ...

Use of deep learning model for paediatric elbow radiograph binomial classification: initial experience, performance and lessons learnt.

Singapore medical journal
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
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
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
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
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
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
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....