Emergency Medicine

Convolutional Neural Networks for Burn Segmentation and Classification Tasks Using RGB Photographs: A Five-Year Systematic Review

Burn wound assessment remains complex, with visual accuracy often below 50% among non-specialists. Convolutional neural networks (CNNs) offer promising solutions, achieving 68.9%–95.4% accuracy in depth classification and 76.0%–99.4% in area segmentation. This review systematically evaluates CNN-based burn area segmentation (BAS), burn depth classification (BDC), and burn depth segmentation (BDS) ...

External Validation of a Machine Learning Model to Predict Postpartum Hemorrhage in a US Northeastern Healthcare System

Postpartum hemorrhage (PPH) is a major cause of maternal morbidity and mortality. Timely prediction may prevent adverse maternal outcomes, and efforts are needed to develop accurate predictive tools. A high-performing machine learning model to predict PPH using data from the US Consortium for Safe Labor (CSL) remains to be widely validated in contemporary clinical settings using electronic health ...

Risk Prediction Modelling of 30-day all-cause mortality following percutaneous coronary intervention in an Australian population: Leveraging Machine Learning

Pre-procedural risk prediction of 30-day all-cause mortality after percutaneous coronary intervention (PCI) aids in clinical decision-making and bench...

From Black Box to Discovery Engine: A Geometric and Topological Framework for Interpreting Graph Neural Networks in Critical Care

Effective clinical decision-making in critical care depends on interpreting complex, high-dimensional patient data. However, many advanced AI models f...

AI-Powered Triage of Suicidal Ideation in Adolescents: A Comparative Evaluation of Large Language Models Using Synthetic Clinical Vignettes

To evaluate the performance of leading Large Language Models (LLMs) in classifying suicide risk and generating clinically appropriate action plans for...

A Better Way: Initial Acceptability Testing of Using Artificial Intelligence Tools to Accelerate Development of Trauma Clinical Guidance

Representatives of the trauma community have voiced a need for a new approach to developing clinical guidance. In this study, we test the initial acce...

Diagnostic Codes in AI prediction models and Label Leakage of Same-admission Clinical Outcomes

Artificial intelligence (AI) and statistical models designed to predict same-admission outcomes for hospitalized patients, such inpatient mortality, o...

Machine learning algorithm to predict fragility fractures and identification of important features – an explainable approach

In this study, we developed ML algorithms to predict fragility fractures, considering the occurrence of fractures at different skeletal sites. We inve...

Key features associated with opioid misuse in chronic pain: A machine learning cross-sectional study

Opioid misuse remains a critical public health concern, associated with increased risk of overdose, psychiatric comorbidity, and societal costs. While...

Integrating BERT and Graph Convolutional Networks for Medical Literature Mining: A Knowledge Graph Ap-proach to Pelvic Fracture Research Analysis

Pelvic fractures have consistently been a focal point in orthopedic research. This study aims to provide a comprehensive analysis of the literature on...

Temperature dominates dengue transmission in Thailand: Machine learning reveals critical thresholds and COVID-19 disruption

Dengue fever remains a critical public health challenge in Thailand, with transmission dynamics driven by complex interactions between environmental a...

Machine Learning Assisted Differentiation of Low Acuity Patients at Dispatch (MADLAD): A Randomized Controlled Trial

Resource Constrained Situations (RCS) at Emergency Medical Dispatch centers where there are more patients requiring an ambulance than there are availa...

The Effect of Image Resolution on the Performance of Deep Learning Algorithms in Detecting Calcaneus Fractures on X-Ray

To evaluate convolutional neural network (CNN) model training strategies that optimize the performance of calcaneus fracture detection on radiographs ...

Evaluating Accuracy and Reasoning Capabilities of Large Language Models for Acute Ischemic Stroke Management

Acute ischemic stroke (AIS) management has evolved substantially over the past two decades, with mechanical thrombectomy adding complexity that requir...

Predictive modeling of hematoma expansion from non-contrast computed tomography in spontaneous intracerebral hemorrhage patients

Hematoma expansion is a consistent predictor of poor neurological outcome and mortality after spontaneous intracerebral hemorrhage (ICH). An incomplet...

Predicting Hospital Admissions Using Pretrained EHR Embeddings: External Evaluation and Insights on Local Vocabulary Adaptation

Unplanned hospital admissions impose substantial strain on healthcare systems, yet predictive models for these events remain underexplored in practice...

Predicting ICU Transfer and Short-term Mortality in Emergency Department Atrial Fibrillation Patients: An Enhanced Machine Learning Model Using MIMIC Data

Atrial fibrillation (AF) is a prevalent condition in emergency department (ED) patients and is associated with an elevated risk of intensive care unit...

Bayesian machine learning enables discovery of risk factors for hepatosplenic multimorbidity related to schistosomiasis

One in 25 deaths worldwide is related to liver disease, and often with multiple hepatosplenic conditions. Yet, little is understood of the risk factor...

Machine learning models for early prognosis prediction in cardiogenic shock

Cardiogenic shock (CS) is a severe and frequent complication of acute myocardial infarction (AMI), necessitating rapid and accurate prognosis as-sessm...

Development, System Design, Safety, and Performance Metrics of a Conversational Agent for Reducing Depressive and Anxious Symptoms Based on a Large Language Model: The MHAI Study

Conversational agents based on large language models (LLMs) have shown moderate efficacy in reducing depressive and anxiety symptoms. However, most ex...