Emergency Medicine

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 overall outcomes but do not estimate how an individual's outcome might change with the decision to operate. Moreover, physiologic data such as vital signs are often collected but rarely used for such decision support. We aimed to develop and externa...

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. Despite the advances in trauma systems, surgical techniques, and critical care, the optimal management strategies for acute SCI continue to improve, and clinical practice remains heterogeneous across institutions. The pathophysiology of acute SCI involves ...

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...

Non-invasive classification of coronary perfusion pressure during CPR using smartphone-based skin video and deep learning.

BACKGROUND AND OBJECTIVE: Coronary perfusion pressure (CPP) is an important determinant of myocardial blood flow and an indicator during cardiopulmona...

A pipeline for developing AI-driven models to predict molecular initiating events: a case study on neural tube defects.

Adverse Outcome Pathways (AOPs) describe the sequence of molecular and cellular events that lead to toxicity. Each pathway begins with a Molecular Ini...

Exploratory prediction model for deep vein thrombosis in intensive care patients after anticoagulation therapy: An observational study.

BACKGROUND: Despite low-molecular-weight heparin (LMWH) prophylaxis, the incidence of deep vein thrombosis (DVT) remains high in intensive care unit (...

Comments on: "the use of machine learning in predicting clinical outcomes in emergency pre-examination triage: A systematic review of the literature".

Acceptable accuracy for medical AI: a survey of physicians and the general population in Sweden.

OBJECTIVES: To identify the lowest sensitivity and specificity that physicians and the general population consider acceptable for medical artificial i...

Clinical applications of EEG connectivity in acute brain injuries: A systematic review.

OBJECTIVE: While connectivity methods have been widely studied as predictors of recovery in chronic disorders of consciousness (DoC), evidence for EEG...

Current applications and challenges of artificial intelligence applied to diagnostics in pediatric musculoskeletal imaging.

The use of artificial intelligence (AI) in pediatric musculoskeletal imaging has undergone significant expansion over the past few years. Until recent...

An inventory of emergency department information systems (EDIS) in Canada: Benchmarking system functionality and technology readiness.

OBJECTIVES: Emergency Department Information Systems (EDIS) are essential digital technology used in Emergency Departments (ED). Modern EDIS provide e...

Sonogenetics for Precision Medicine: A Focus on Immunoengineering and Genome Engineering.

Ultrasound can penetrate centimetres of soft tissue, focus energy with millimetre precision, and operate safely under real-time image guidance. Levera...