European respiratory review : an official journal of the European Respiratory Society
39537241
BACKGROUND: Asthma exacerbations in children pose a significant burden on healthcare systems and families. While traditional risk assessment tools exist, artificial intelligence (AI) offers the potential for enhanced prediction models.
Studies in health technology and informatics
39575796
Proximal humeral fractures are among the most common fractures seen in emergency departments. Accurately diagnosing and selecting the most appropriate treatment for these fractures can be challenging, and consultation with a senior orthopedic surgeon...
The American journal of emergency medicine
39566376
BACKGROUND: Approximately 20 % of emergency department (ED) visits involve cardiovascular symptoms. While ECGs are crucial for diagnosing serious conditions, interpretation accuracy varies among emergency physicians. Artificial intelligence (AI), suc...
BACKGROUND: To retrospectively assess the added value of an artificial intelligence (AI) algorithm for detecting pulmonary nodules on ultra-low-dose computed tomography (ULDCT) performed at the emergency department (ED).
STUDY OBJECTIVE: This study investigates the potential to improve emergency department (ED) triage using machine learning models by comparing their predictive performance with the Canadian Triage Acuity Scale (CTAS) in identifying the need for critic...
BACKGROUND: In Emergency Departments (EDs), triage is crucial for determining patient severity and prioritizing care, typically using the Manchester Triage Scale (MTS). Traditional triage systems, reliant on human judgment, are prone to under-triage ...
OBJECTIVES: We assessed whether proactive care management for artificial intelligence (AI)-identified at-risk patients reduced preventable emergency department (ED) visits and hospital admissions (HAs).
OBJECTIVES: Increasing operational pressures on emergency departments (ED) make it imperative to quickly and accurately identify patients requiring urgent clinical intervention. The widespread adoption of electronic health records (EHR) makes rich fe...
The array of complex and evolving patient data has limited clinical decision making in the emergency department (ED). This study introduces an advanced deep learning algorithm designed to enhance real-time prediction accuracy for integration into a n...
The journal of applied laboratory medicine
39499535
BACKGROUND: Interest in prediction models, including machine learning (ML) models, based on laboratory data has increased tremendously. Uncertainty in laboratory measurements and predictions based on such data are inherently intertwined. This study d...