AIMC Topic: Emergency Service, Hospital
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Prediction of contrast-associated acute kidney injury with machine-learning in patients undergoing contrast-enhanced computed tomography in emergency department.
Scientific reports
Feb 27, 2025
Radiocontrast media is a major cause of nephrotoxic acute kidney injury(AKI). Contrast-enhanced CT(CE-CT) is commonly performed in emergency departments(ED). Predicting individualized risks of contrast-associated AKI(CA-AKI) in ED patients is challen...
Comparison of a Novel Machine Learning-Based Clinical Query Platform With Traditional Guideline Searches for Hospital Emergencies: Prospective Pilot Study of User Experience and Time Efficiency.
JMIR human factors
Feb 25, 2025
BACKGROUND: Emergency and acute medicine doctors require easily accessible evidence-based information to safely manage a wide range of clinical presentations. The inability to find evidence-based local guidelines on the trust's intranet leads to info...
Performance of machine learning models in predicting difficult laryngoscopy in the emergency department: a single-centre retrospective study comparing with conventional regression method.
BMC emergency medicine
Feb 21, 2025
BACKGROUND: Emergency endotracheal intubation is a critical skill for managing airway emergencies in the emergency department (ED). Accurate prediction of difficult laryngoscopy is essential for improving first-attempt success, minimizing complicatio...
Automated identification of incidental hepatic steatosis on Emergency Department imaging using large language models.
Hepatology communications
Feb 19, 2025
BACKGROUND: Hepatic steatosis is a precursor to more severe liver disease, increasing morbidity and mortality risks. In the Emergency Department, routine abdominal imaging often reveals incidental hepatic steatosis that goes undiagnosed due to the ac...
An effective multi-step feature selection framework for clinical outcome prediction using electronic medical records.
BMC medical informatics and decision making
Feb 17, 2025
BACKGROUND: Identifying key variables is essential for developing clinical outcome prediction models based on high-dimensional electronic medical records (EMR). However, despite the abundance of feature selection (FS) methods available, challenges re...
AI-driven triage in emergency departments: A review of benefits, challenges, and future directions.
International journal of medical informatics
Feb 15, 2025
BACKGROUND: Emergency Departments (EDs) are critical in providing immediate care, often under pressure from overcrowding, resource constraints, and variability in patient prioritization. Traditional triage systems, while structured, rely on subjectiv...
The diagnostic performance of automatic B-lines detection for evaluating pulmonary edema in the emergency department among novice point-of-care ultrasound practitioners.
Emergency radiology
Feb 14, 2025
PURPOSE: B-lines in lung ultrasound have been a critical clue for detecting pulmonary edema. However, distinguishing B-lines from other artifacts is a challenge, especially for novice point of care ultrasound (POCUS) practitioners. This study aimed t...
Establishing methodological standards for the development of artificial intelligence-based Clinical Decision Support in emergency medicine.
CJEM
Feb 7, 2025
OBJECTIVE: Artificial intelligence (AI) offers opportunities for managing the complexities of clinical care in the emergency department (ED), and Clinical Decision Support has been identified as a priority application. However, there is a lack of pub...
Artificial intelligence-based clinical decision support in the emergency department: A scoping review.
Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
Feb 4, 2025
OBJECTIVE: Artificial intelligence (AI)-based clinical decision support (CDS) has the potential to augment high-stakes clinical decisions in the emergency department (ED). However, its current usage and translation to implementation remains poorly un...
Prediction of the Risk of Adverse Clinical Outcomes with Machine Learning Techniques in Patients with Noncommunicable Diseases.
Journal of medical systems
Feb 3, 2025
Decision-making in chronic diseases guided by clinical decision support systems that use models including multiple variables based on artificial intelligence requires scientific validation in different populations to optimize the use of limited human...
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