Emergency Medicine

Comment on "Integrating machine learning into fracture liaison services: toward precision risk stratification in osteoporosis care: a prospective cohort study".

In silico augmentation strategies for enhanced machine learning performance in fracture recognition.

This study presents a machine learning framework for fracture risk prediction and in silico validation of synthetic biomedical data. A retrospective dataset comprising 169 patient records with clinically relevant variables, including age, sex, weight, height, medication status, and bone mineral density (BMD), was analyzed. Multiple classification models, including Logistic Regression, Random Fores...

Innovations in pediatric imaging: a scoping review of the past decade with case illustrations.

BACKGROUND: Imaging plays a fundamental and increasing role in the diagnostic work-up of pediatric patients. Non-invasive imaging methods include ultr...

Integrating machine learning and structural analysis to decipher benzo[a]pyrene-induced bladder cancer networks.

Benzo[a]pyrene (BaP), a polycyclic aromatic hydrocarbon from tobacco smoke, exhaust, and pollutants, is linked to bladder cancer (BLCA). We systematic...

Enhancing aviation safety: An 80-year data-driven model for classification of aviation incident and accident.

The aviation system is safety-critical by nature, and any occurrence of an incident or accident can lead to the loss of human life and significant ope...

Current Documentation in the Delivery Room and Strategies for Improvement.

BACKGROUND: Current neonatal resuscitation program standards recommend the use of real-time documentation completed by a designated scribe. Accurate d...

Diagnostic performance of artificial intelligence in fracture detection with ultrasound: A systematic review and meta-analysis.

BACKGROUND: Bone fractures are common in acute care, and point-of-care ultrasound (POCUS) is an emerging diagnostic tool that can be complementary to ...

Mechanistic Insights into BDE-153-Induced Idiopathic Pulmonary Fibrosis through Network Toxicology, Machine Learning, and Cellular Validation.

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease with limited therapeutic options. Polybrominated diphenyl ethers (PBDEs...

Mitigating Algorithmic Bias in AI-Powered Toxicology: Frameworks for Explainable and Equitable Predictions in Human Health and Environmental Safety.

The rapid integration of artificial intelligence (AI) and machine learning into predictive toxicology has transformed chemical hazard identification, ...

Multi-dimensional analysis revealing the mechanism of T-2 toxin driving liver fibrosis: integration of network toxicology, machine learning, experimental verification, and molecular docking.

BACKGROUND: T-2 toxin is a highly toxic mycotoxin commonly present in food and the environment, with accumulating evidence supporting its hepatotoxic ...

QSAR in the AI Era: Reflections for Advancing Chemical Safety Assessment.

Quantitative Structure-Activity Relationship (QSAR) models are increasingly discussed in the broader context of artificial intelligence (AI). Indeed, ...

ToxiGuard: an AOP-guided mechanistically interpretable framework for multi-organ toxicity prediction.

Accurate prediction of organ-specific toxicity with mechanistic interpretability remains a central challenge in chemical safety assessment and transla...

Implementing Artificial Intelligence for Intracranial Hemorrhage Detection.

Postangiography Prediction of Renal Replacement Therapy in Acute Myocardial Infarction-Related Cardiogenic Shock: Least Absolute Shrinkage and Selection Operator Nomogram Development and Validation.

BACKGROUND: Acute kidney injury critically impacts outcomes in cardiogenic shock secondary to acute myocardial infarction (CS-AMI). Acute kidney injur...

Development, Feasibility, Acceptability, and Usability of an Artificial Intelligence-Powered Chatbot (Suzy) to Support Patients in Substance Use Disorder Recovery: Multiphase Study.

BACKGROUND: Substance use disorder (SUD) remains a major public health crisis in the United States, with significant challenges in treatment access, r...

Machine Learning-Based Prediction of NOECs for over 10,000 PFAS across Multiple Fish Species Enables Large-Scale Toxicological Analysis.

Per- and polyfluoroalkyl substances (PFASs), comprising over 10,000 persistent chemicals, are prevalent in aquatic ecosystems and threaten ecological ...

Systematic review and meta-analysis of machine learning models predicting massive hemorrhage protocol in trauma.

Timely activation of massive hemorrhage protocols (MHP) is critical to prevent exsanguination and improve survival in trauma patients. Current clinica...

National trends and comparative outcomes of insulin versus non-insulin therapy in acute ischemic stroke patients treated with mechanical thrombectomy: a retrospective cohort study using the national inpatient sample.

Background diabetes mellitus is prevalent among patients with acute ischemic stroke (AIS). The prognostic significance of long-term insulin treatment ...

Polystyrene Microplastics Drive Chondrotoxicity in Osteoarthritis through Inducing ER Stress and Apoptosis via NFKB1 Activation.

Polystyrene microplastics (PS-MPs), recognized as widespread environmental contaminants, have been identified within human articular tissues. However,...

Human-Artificial Intelligence Collaborative Decision-Making in Emergencies: Relative Advantage Theory.

ObjectiveThis study proposes Relative Advantage Theory to explain decision-making mechanisms in emergencies where humans collaborate with AI systems, ...