This study presents a machine learning-driven model predicting all-cause mortality two years in advance using administrative health data focused on diabetic patients. Integrating hospitalization records, emergency department data, demographics, and c...
BACKGROUND: Offline reinforcement learning (RL) has been increasingly applied to clinical decision-making problems. However, due to the lack of a standardized pipeline, prior work often relied on strategies that may lead to overfitted policies and in...
This study aimed to develop machine learning-based algorithms to assist physicians in ultrasound-guided localization of the cricoid cartilage (CC), thyroid cartilage (TC), and cricothyroid membrane (CTM) for cricothyroidotomy. Adult female participan...
While predictors of asthma exacerbation risk are generally well established, predictors of exacerbation severity remain largely undefined. Identifying robust clinical predictors of exacerbation severity is essential to support tailored management str...
BACKGROUND: Patients with diabetes admitted to emergency care face a higher risk of complications, including prolonged hospital stays, admissions to the intensive care unit and mortality.
Scandinavian journal of trauma, resuscitation and emergency medicine
Sep 24, 2025
BACKGROUND: Accurately predicting mortality in patients with skin and soft-tissue infections (SSTIs) remains challenging. Machine learning models offer rapid processing, algorithmic impartiality, and strong predictive accuracy, which may improve earl...
BACKGROUND: Emergency department (ED) overcrowding remains a critical challenge, leading to delays in patient care and increased operational strain. Current hospital management strategies often rely on reactive decision-making, addressing congestion ...
INTRODUCTION: Bronchiolitis, a viral lower respiratory tract infection, is the leading cause of hospitalisation for infants, with healthcare utilisation highest among young infants (aged ≤90 days). Clinical models to predict respiratory deterioration...
Urinary tract infections (UTIs) are among the most common bacterial infections, yet they are both frequently misdiagnosed and inappropriately treated. We aimed to determine whether a machine learning model could accurately predict bacteriuria by usin...
BACKGROUND: Large language models (LLMs) such as ChatGPT are increasingly explored for clinical decision support. However, their performance in high-stakes emergency scenarios remains underexamined. This study aimed to evaluate ChatGPT's diagnostic a...
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