Emergency Medicine

Understanding Uncertainty in Large Language Model Predictions of Early Death in Critically Ill Patients: A Conformal Prediction Approach

Early prediction of in-hospital death remains a significant challenge due to the limited availability of structured data during initial admission. Unstructured clinical notes, which often contain important observations and impressions, are an underutilized resource for real-time risk stratification. While leveraging recent advances in large language models (LLM) is a promising approach to use this...

Implementation of an Opioid Use Disorder (OUD) Machine-Learning Phenotype in Real-Time for the ADAPT Project

Develop and deploy a real-time, EHR-integrated machine learning phenotype to identify emergency department (ED) patients with opioid use disorder (OUD) for prospective clinical trial screening and buprenorphine initiation. We conducted a multi-phase study across three EDs in a single United States health system from 2014 to 2025. Using visit-level data available at or before triage, we trained a r...

Machine Learning for Urinary Tract Infection Prediction in Emergency Departments: An Explainable Approach

Urinary tract infections (UTIs) represent a substantial burden in emergency department (ED) settings, where diagnostic delays and the limitations of t...

Construction of a predictive model for rebleeding risk in upper gastrointestinal bleeding patients based on clinical indicators such as infection.

BACKGROUND: The annual incidence of upper gastrointestinal hemorrhage (UGIB) is about 60 cases/100,000 people, and about 40% of UGIB patients have hem...

Deployable machine learning-based decision support system for tracheostomy in acute burn patients.

BACKGROUND: Airway obstruction is a common emergency in acute burns with high mortality. Tracheostomy is the most effective method to keep patency of ...

Intimate partner violence and stress-related disorders: from epigenomics to resilience.

Intimate Partner Violence (IPV) is a major public health problem to be addressed with innovative and interconnecting strategies for ensuring the psych...

SCIseg: Automatic Segmentation of Intramedullary Lesions in Spinal Cord Injury on T2-weighted MRI Scans.

Purpose To develop a deep learning tool for the automatic segmentation of the spinal cord and intramedullary lesions in spinal cord injury (SCI) on T2...

RSNA 2023 Abdominal Trauma AI Challenge: Review and Outcomes.

Purpose To evaluate the performance of the winning machine learning models from the 2023 RSNA Abdominal Trauma Detection AI Challenge. Materials and M...

Is Generative AI Increasing the Risk for Technology-Mediated Trauma Among Vulnerable Populations?

The proliferation of Generative Artificial Intelligence (Generative AI) has led to an increased reliance on AI-generated content for designing and dep...

PROGNOSTIC ACCURACY OF MACHINE LEARNING MODELS FOR IN-HOSPITAL MORTALITY AMONG CHILDREN WITH PHOENIX SEPSIS ADMITTED TO THE PEDIATRIC INTENSIVE CARE UNIT.

Objective: The Phoenix sepsis criteria define sepsis in children with suspected or confirmed infection who have ≥2 in the Phoenix Sepsis Score. The ad...

Challenges and opportunities for validation of AI-based new approach methods.

The integration of artificial intelligence (AI) into new approach methods (NAMs) for toxicology rep-resents a paradigm shift in chemical safety assess...

A Novel Ensemble Approach for Rib Fracture Detection and Visualization using CNNs and Grad-CAM.

AIM: This study aimed to develop a reliable and efficient system for predicting and locating rib fractures in medical images using an ensemble of conv...

Clinical Application of a Big Data Machine Learning Analysis Model for Osteoporotic Fracture Risk Assessment Built on Multicenter Clinical Data in Qingdao City.

BACKGROUND: Osteoporotic fractures (OPF) pose a public health issue, imposing significant burdens on families and societies worldwide. Currently, ther...

Deep-Learning Generated Synthetic Material Decomposition Images Based on Single-Energy CT to Differentiate Intracranial Hemorrhage and Contrast Staining Within 24 Hours After Endovascular Thrombectomy.

AIMS: To develop a transformer-based generative adversarial network (trans-GAN) that can generate synthetic material decomposition images from single-...

A Machine Learning Algorithm to Predict Medical Device Recall by the Food and Drug Administration.

INTRODUCTION: Medical device recalls are important to the practice of emergency medicine, as unsafe devices include many ubiquitous items in emergency...

Reported liver toxicity of food chemicals in rats extrapolated to humans using virtual human-to-rat hepatic concentration ratios generated by pharmacokinetic modeling with machine learning-derived parameters.

Pharmacokinetic data are not generally available for evaluating the toxicological potential of food chemicals. A simplified physiologically based phar...

Performance analysis of an emergency triage system in ophthalmology using a customized CHATBOT.

PURPOSE: To evaluate the performance of a custom ChatGPT-based chatbot in triaging ophthalmic emergencies compared to trained ophthalmologists.

Lightweight G-YOLOv11: Advancing Efficient Fracture Detection in Pediatric Wrist X-rays

Computer-aided diagnosis (CAD) systems have greatly improved the interpretation of medical images by radiologists and surgeons. However, current CAD...

[Image reconstruction for cerebral hemorrhage based on improved densely-connected fully convolutional neural network].

Cerebral hemorrhage is a serious cerebrovascular disease with high morbidity and high mortality, for which timely diagnosis and treatment are crucial....

Unsupervised Domain Adaptive Person Search via Dual Self-Calibration

Unsupervised Domain Adaptive (UDA) person search focuses on employing the model trained on a labeled source domain dataset to a target domain datase...