Emergency Medicine

Machine learning models to improve targeting of blood culture testing

Background Bloodstream infections are a major cause of mortality, yet the primary testing method, blood cultures, have low positivity (<10%) and turnaround times of 24 - 48 hours. Many are taken from patients at low risk of infection, while some bloodstream infections are diagnosed late or missed entirely. We aimed to develop and externally validate machine learning models to improve targeting of ...

Validating Artificial Intelligence Guidance for Ultrasound Acquisition and Remote Interpretation

Background: Venous thromboembolism (VTE), including deep vein thrombosis (DVT), remains a major global health burden. Diagnostic pathways rely on ultrasound but are limited by availability and prolonged time-to-imaging. Novel artificial intelligence (AI) guidance systems have been designed to enable non-ultrasound-trained operators to acquire proximal lower extremity compression ultrasounds for re...

Learned ultrasound segmentation and deformable CT fusion for augmented reality endovascular surgery

Minimally invasive endovascular procedures offer reduced surgical trauma, shorter recovery times, and improved outcomes, but rely on 2D fluoroscopic X...

Reconsidering the case against risk prediction in self-harm: routinely collected health data distinguishes groups at higher and lower risk of adverse outcomes following paracetamol overdose

Background. UK clinical guidance recommends that structured risk prediction tools and risk stratification should not be used in self-harm, to predict ...

Calibrated Selective Prediction Using Deep Ensembles for ROI-Based Thyroid Nodule Ultrasound Classification Under Dataset Shift: A Retrospective Evaluation

Background: Deep learning models can classify thyroid nodules on ultrasound, but reliable clinical decision support also requires calibrated probabili...

A retrospective study of a Chinese vision-language large model for emergency 3D brain CT interpretation

Emergency brain computed tomography (CT) is the first line imaging modality for patients with acute neurological symptoms and trauma, where delayed or...

SPARC-Net: A Spectral, Causality-Aware, and Hard-Constrained Physics-Informed Architecture for Stiff and Shock-Dominated Partial Differential Equations

Physics-Informed Neural Networks (PINNs) provide a meshless approach for solving partial differential equations (PDEs), but suffer severe degradation ...

Integrating Physics-Informed Neural Networks and 3D Vascular Geometry Learning for Cerebral Aneurysm Detection and Multimodal Rupture-Risk Prediction

Cerebral aneurysms are localized dilations of intracranial arteries that may rupture and cause subarachnoid hemorrhage. Current assessment relies on h...

The Singularity Space: A Generative Diffusion Framework for Signal Representation

Generative models often represent signals as dense grids of amplitudes, blurring sharp transients that are crucial for the correctness of physical sig...

Development and Evaluation of Artificial Intelligence-Assisted Decision Support System for Public Health Emergency Classification and Escalation in Kenya

Background Timely assessment, classification, and escalation of public health events are essential for effective outbreak response, yet decision-makin...

The Causal Artificial Intelligence Clinician for early haemodynamic management of septic shock in ICU

Introduction: Standardizing fluid and vasopressor resuscitation in sep- tic shock is challenging due to patient heterogeneity. We trained a causal mod...

A multimodal foundation model for emergency head CT interpretation

Non-contrast head CT is the first-line imaging modality for acute neurological emergencies, with demand rising worldwide. However, existing foundation...

Uncertainty-aware extraction of clinical findings from Finnish EHRs using open large language models

Objective. To evaluate whether open-weight large language models (LLMs) can accurately extract clinical findings from Finnish-language pediatric recor...

Is simple better? Comparing Computational Cost and Carbon Impact of Machine Learning Models for Traumatic Brain Injury Prediction; A Case Study for Sustainable Digital Health Implementation

Background Machine learning (ML) models for traumatic brain injury (TBI) prediction increasingly demand extensive data, computational resources, and e...

Emergency Department Presenting Concerns Among Admissions With Hypercapnia: A Retrospective NLP Study of MIMIC-IV

Background Hypercapnia may indicate a primary ventilatory syndrome, a complication of another illness, or an epiphenomenon of severe disease. The pres...

CerebAI: Explainable Three-Class Stroke CT Classification via ConvNeXt and Integrated Gradients

Stroke is a leading cause of death and long-term disability worldwide, affecting approximately 15 million individuals annually. Prompt and accurate su...

Comparative Performance of Clinical Scoring Systems for Early Mortality Prediction in Blunt Traumatic Brain Injury

Background: Early risk stratification in traumatic brain injury (TBI) is essential for timely triage, resource allocation, and clinical decision-makin...

HyTrax: Deep Sequential Modeling of Serial Musculoskeletal Measurements for Fracture Prediction in the Women's Health Initiative with External Evaluation in the Framingham Heart Study

The clinical utility of monitoring longitudinal changes in musculoskeletal trajectories, including bone mineral density (BMD), muscle strength, height...

Diagnostic accuracy of a DenseNet-121 deep learning algorithm for chest radiograph triage in health assessment applicants: a prospective shadow-mode validation study in Nepal

Objectives: To evaluate the diagnostic accuracy of a publicly available DenseNet-121 convolutional neural network (TorchXRayVision) for triaging chest...

A Local Outpatient Practice-Level Prediction Model for Short-Term Psychiatric Emergency Presentation

Importance: Psychiatric emergency department (ED) presentations are difficult to predict using general medical risk stratification tools. Health infor...