AIMC Topic: Emergency Service, Hospital
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Identifying low acuity Emergency Department visits with a machine learning approach: The low acuity visit algorithms (LAVA).
Health services research
Mar 30, 2024
OBJECTIVE: To improve the performance of International Classification of Disease (ICD) code rule-based algorithms for identifying low acuity Emergency Department (ED) visits by using machine learning methods and additional covariates.
Artificial intelligence to advance acute and intensive care medicine.
Current opinion in critical care
Mar 22, 2024
PURPOSE OF REVIEW: This review explores recent key advancements in artificial intelligence for acute and intensive care medicine. As artificial intelligence rapidly evolves, this review aims to elucidate its current applications, future possibilities...
Early identification of suspected serious infection among patients afebrile at initial presentation using neural network models and natural language processing: A development and external validation study in the emergency department.
The American journal of emergency medicine
Mar 16, 2024
OBJECTIVE: To develop and externally validate models based on neural networks and natural language processing (NLP) to identify suspected serious infections in emergency department (ED) patients afebrile at initial presentation.
A survey of patient acceptability of the use of artificial intelligence in the diagnosis of paediatric fractures: an observational study.
Annals of the Royal College of Surgeons of England
Mar 13, 2024
INTRODUCTION: This study aimed to assess carer attitudes towards the use of artificial intelligence (AI) in management of fractures in paediatric patients. As fracture clinic services come under increasing pressure, innovative solutions are needed to...
Harnessing the Power of Generative AI for Clinical Summaries: Perspectives From Emergency Physicians.
Annals of emergency medicine
Mar 12, 2024
STUDY OBJECTIVE: The workload of clinical documentation contributes to health care costs and professional burnout. The advent of generative artificial intelligence language models presents a promising solution. The perspective of clinicians may contr...
Trustworthy deep learning framework for the detection of abnormalities in X-ray shoulder images.
PloS one
Mar 11, 2024
Musculoskeletal conditions affect an estimated 1.7 billion people worldwide, causing intense pain and disability. These conditions lead to 30 million emergency room visits yearly, and the numbers are only increasing. However, diagnosing musculoskelet...
Development and Validation of a Natural Language Processing Model to Identify Low-Risk Pulmonary Embolism in Real Time to Facilitate Safe Outpatient Management.
Annals of emergency medicine
Mar 2, 2024
STUDY OBJECTIVE: This study aimed to (1) develop and validate a natural language processing model to identify the presence of pulmonary embolism (PE) based on real-time radiology reports and (2) identify low-risk PE patients based on previously valid...
A machine learning algorithm-based predictive model for pressure injury risk in emergency patients: A prospective cohort study.
International emergency nursing
Mar 2, 2024
OBJECTIVES: To construct pressure injury risk prediction models for emergency patients based on different machine learning algorithms, to optimize the best model, and to provide a suitable assessment tool for preventing the occurrence of pressure inj...
Real-time machine learning-assisted sepsis alert enhances the timeliness of antibiotic administration and diagnostic accuracy in emergency department patients with sepsis: a cluster-randomized trial.
Internal and emergency medicine
Feb 21, 2024
Machine learning (ML) has been applied in sepsis recognition across different healthcare settings with outstanding diagnostic accuracy. However, the advantage of ML-assisted sepsis alert in expediting clinical decisions leading to enhanced quality fo...
AI in the ED: Assessing the efficacy of GPT models vs. physicians in medical score calculation.
The American journal of emergency medicine
Feb 20, 2024
BACKGROUND AND AIMS: Artificial Intelligence (AI) models like GPT-3.5 and GPT-4 have shown promise across various domains but remain underexplored in healthcare. Emergency Departments (ED) rely on established scoring systems, such as NIHSS and HEART ...
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