Emergency Medicine

Predicting public Intensive care unit mortality and hospitalization using Data: An evaluation of Brazil's Largest COVID-19 epidemiological dataset.

OBJECTIVES: To assess the SRAG dataset's potential for modeling COVID-19 mortality and LOS-ICU, identify key data gaps, and support the development of predictive tools for ICU planning in future outbreaks. METHODS: The SRAG dataset was split into training and test sets, followed by a hybrid feature variable selection strategy, which applied the XGBoost classifier and subsequent inclusion of comorb...

Three decades of hemodynamic monitoring (1995-2025): from invasive pressure based hemodynamic profiling to functional physiology.

PURPOSE OF REVIEW: Hemodynamic monitoring has undergone a profound transformation over the last 30 years. The field has transitioned from the "standard of care" invasive pulmonary artery catheterization (PAC) toward minimally invasive and noninvasive technologies. This evolution is characterized by a shift in clinical philosophy. RECENT FINDINGS: A very strong shift away from the measurement of st...

AI Models for Surgical Decision Support in Spontaneous Intracerebral Hemorrhage: A Systematic Review in Relation to Trials and Guidelines.

Artificial intelligence (AI) applications for spontaneous intracerebral hemorrhage (ICH) are rapidly expanding, particularly in perioperative imaging ...

Development and validation of a machine learning model for predicting 6-month mortality in patients with infective endocarditis.

OBJECTIVE: This study aimed to develop and validate a machine learning (ML)-based model for predicting 6-month all-cause mortality in patients diagnos...

An effluent risk informed closed-loop framework for early warning of influent anomalies using COD soft sensing.

Sudden shock loads in wastewater influent can severely disrupt biological treatment processes and cause effluent quality exceedances in wastewater tre...

Association Between Environmental Chemicals Phthalates and Stroke Among the US Adults: A Cross-Sectional Study.

Stroke has a multifactorial etiology, and phthalates, as widely used environmental chemicals, may play an underexplored role in cerebrovascular health...

Developing an artificial intelligence tool for detecting fractures of child abuse: preliminary findings.

OBJECTIVES: Approximately 6.9% of children in the United Kingdom have suffered physical abuse. Fractures are a common sign and must not be overlooked ...

A hierarchical prompt and prototype learning framework for brain disorder classification.

Accurate diagnosis of brain disorders (BDs) is challenging in clinical practice. Most existing deep learning-based methods perform diagnosis only in a...

From predictive accuracy to clinical utility: Model interpretability and the missing bone mineral density data.

This letter to the editor commends the study by Liu et al. on their machine learning model for predicting rib fractures but highlights two crucial cha...

Physiology-informed machine learning for patient-level surgical decision-making in severe traumatic brain injury: Multicenter model development with external validation.

BACKGROUND: Surgical decisions for severe traumatic brain injury (TBI) are often made under prognostic uncertainty. Existing prognostic models predict...

Acute spinal cord injury: contemporary management and novel therapies.

Acute spinal cord injury (SCI) remains a life-threatening condition associated with substantial morbidity, mortality, and long-term disability. Despit...

Interpretable Machine Learning to Understand Wildfire Toxicity: Bridging Chemicals, Omics, and Toxicological Outcomes via Symbolic Regression with Novel Feature Scoring.

Wildfire smoke exposures are increasingly common, consisting of complex mixtures of gases and particulates known to cause diverse pulmonary health eff...

Integrating the interpretable machine learning Score For Emergency Risk Prediction (SERP) with emergency department triage to better predict 30-Day mortality.

OBJECTIVE: This study integrates a machine learning (ML) based Score for Emergency Risk Prediction (SERP), developed using objective mortality endpoin...

CEREBLEED: Automated Quantification and Severity Scoring of Intracranial Hemorrhage on Noncontrast CT.

BACKGROUND AND OBJECTIVES: Standardized interpretation of intracranial hemorrhage (ICH) severity on noncontrast computed tomography (NCCT) is limited ...

Computational models for predicting bone fracture healing: a review of modeling approaches, predictions, and emerging strategies.

BACKGROUND AND OBJECTIVE: The development of computational models for predicting bone fracture healing process holds strong potential to optimize ther...

Predicting Ketamine Exposure in Pediatric Status Epilepticus: A Pharmacokinetic-Machine Learning Approach.

BACKGROUND AND OBJECTIVE: Status epilepticus is a life-threatening neurological emergency. Ketamine combined with levetiracetam is a promising therapy...

Machine learning prediction of hospitalization outcomes in critically Ill emergency department patients transported by ambulance: A Retrospective Single Center Cohort Study.

Artificial intelligence in orthopedics: current applications, challenges, and future directions.

BACKGROUND: Artificial intelligence research in orthopedics has grown rapidly, yet a substantial gap remains between technical development and clinica...

Beyond block time: a head-to-head comparison of reinforcement learning, genetic algorithms, and predict-then-optimize scheduling for operating room workflow using discrete-event simulation.

BACKGROUND: Operating room (OR) inefficiency persists despite decades of process improvement, largely due to stochastic case durations, emergency disr...

An algorithm-enhanced stool DNA system improves the differential diagnosis of colorectal cancer versus Crohn's disease in high-risk symptomatic patients.

BACKGROUND AND OBJECTIVE: Crohn's disease (CD) and colorectal cancer (CRC) share many clinical symptoms, making non-invasive differential diagnosis di...