Emergency Medicine

A machine learning multimodal profiling of Per- and Polyfluoroalkyls (PFAS) distribution across animal species organs via clustering and dimensionality reduction techniques.

Per- and polyfluoroalkyl substances (PFAS) contamination in aquatic and terrestrial organisms poses significant environmental and health risks. This study quantified 15 PFAS compounds across various tissues (liver, kidney, gill, muscle, skin, lung, blood, breast, feather) from fish (Clarias gariepinus, Oreochromis niloticus, Lates niloticus, Tilapia zilli), livestock (camel, cow, sheep, ram, goat)...

Predicting strength of femora with metastatic lesions from single 2D radiographic projections using convolutional neural networks.

BACKGROUND AND OBJECTIVE: Patients with metastatic bone disease are at risk of pathological femoral fractures and may require prophylactic surgical fixation. Current clinical decision support tools often overestimate fracture risk, leading to overtreatment. While novel scores integrating femoral strength assessment via finite element (FE) models show promise, they require 3D imaging, extensive com...

Pediatric Electrocardiogram-Based Deep Learning to Predict Secundum Atrial Septal Defects.

Secundum atrial septal defect (ASD2) detection is often delayed, with the potential for late diagnosis complications. Recent work demonstrated artific...

Evolving Therapeutic Landscape of Intracerebral Hemorrhage: Emerging Cutting-Edge Advancements in Surgical Robots, Regenerative Medicine, and Neurorehabilitation Techniques.

Intracerebral hemorrhage (ICH) is the most serious form of stroke and has limited available therapeutic options. As knowledge on ICH rapidly develops,...

Chat-GPT in triage: Still far from surpassing human expertise - An observational study.

BACKGROUND: Triage is essential in emergency departments (EDs) to prioritize patient care based on clinical urgency. Recent investigations have explor...

Exploring the toxicological impact of bisphenol a exposure on psoriasis through network toxicology, machine learning, and multi-dimensional bioinformatics analysis.

Psoriasis is a common immune - mediated skin disease, the pathogenesis of which is not completely elucidated. Environmental factors are key to its ons...

CDR-Agent: Intelligent Selection and Execution of Clinical Decision Rules Using Large Language Model Agents

Clinical decision-making is inherently complex and fast-paced, particularly in emergency departments (EDs) where critical, rapid and high-stakes dec...

Application of the Bidirectional Encoder Representations from Transformers Model for Predicting the Abbreviated Injury Scale in Patients with Trauma: Algorithm Development and Validation Study.

BACKGROUND: Deaths related to physical trauma impose a heavy burden on society, and the Abbreviated Injury Scale (AIS) is an important tool for injury...

Research on multi-algorithm and explainable AI techniques for predictive modeling of acute spinal cord injury using multimodal data.

Machine learning technology has been extensively applied in the medical field, particularly in the context of disease prediction and patient rehabilit...

ER-REASON: A Benchmark Dataset for LLM-Based Clinical Reasoning in the Emergency Room

Large language models (LLMs) have been extensively evaluated on medical question answering tasks based on licensing exams. However, real-world evalu...

A machine learning and centrifugal microfluidics platform for bedside prediction of sepsis.

Sepsis is a life-threatening organ dysfunction due to a dysfunctional response to infection. Delays in diagnosis have substantial impact on survival. ...

VLMLight: Traffic Signal Control via Vision-Language Meta-Control and Dual-Branch Reasoning

Traffic signal control (TSC) is a core challenge in urban mobility, where real-time decisions must balance efficiency and safety. Existing methods -...

Interpretable machine learning for predicting optimal surgical timing in polytrauma patients with TBI and fractures to reduce postoperative infection risk.

This retrospective study leverages machine learning to determine the optimal timing for fracture reconstruction surgery in polytrauma patients, focusi...

Clinical, radiological, and radiomics feature-based explainable machine learning models for prediction of neurological deterioration and 90-day outcomes in mild intracerebral hemorrhage.

BACKGROUND: The risks and prognosis of mild intracerebral hemorrhage (ICH) patients were easily overlooked by clinicians. Our goal was to use machine ...

Exploring treatment effects and fluid resuscitation strategies in septic shock: a deep learning-based causal inference approach.

Septic shock exhibits diverse etiologies and patient characteristics, necessitating tailored fluid management. We aimed to compare resuscitation strat...

Deep learning-based identification of vertebral fracture and osteoporosis in lateral spine radiographs and DXA vertebral fracture assessment to predict incident fracture.

Deep learning (DL) identification of vertebral fractures and osteoporosis in lateral spine radiographs and DXA vertebral fracture assessment (VFA) ima...

Dual Attention Residual U-Net for Accurate Brain Ultrasound Segmentation in IVH Detection

Intraventricular hemorrhage (IVH) is a severe neurological complication among premature infants, necessitating early and accurate detection from bra...

Anatomy-Guided Multitask Learning for MRI-Based Classification of Placenta Accreta Spectrum and its Subtypes

Placenta Accreta Spectrum Disorders (PAS) pose significant risks during pregnancy, frequently leading to postpartum hemorrhage during cesarean deliv...

Early Predictive Accuracy of Machine Learning for Hemorrhagic Transformation in Acute Ischemic Stroke: Systematic Review and Meta-Analysis.

BACKGROUND: Hemorrhagic transformation (HT) is commonly detected in acute ischemic stroke (AIS) and often leads to poor outcomes. Currently, there is ...

Better Safe Than Sorry? Overreaction Problem of Vision Language Models in Visual Emergency Recognition

Vision-Language Models (VLMs) have demonstrated impressive capabilities in understanding visual content, but their reliability in safety-critical co...