Emergency Medicine

Predicting Infant Sleep Patterns From Postpartum Maternal Mental Health Measures: Machine Learning Approach.

BACKGROUND: Postpartum maternal mental health (MMH) symptoms, including depression, anxiety, and childbirth-related post-traumatic stress disorder, are known to influence infant sleep trajectories. While previous research has examined their individual and combined associations, the predictive utility of these MMH symptoms for the early identification of infant sleep problems through machine learni...

Machine learning models powered by emergency medical services data enhance stroke triage in prehospital settings.

Machine Learning Models for Predicting Stroke-Associated Pneumonia: A Systematic Review and Meta-Analysis.

Stroke-associated pneumonia (SAP) is a frequent and severe complication following stroke. Recently, several machine learning (ML) models have been dev...

AI-Assisted Chromosome Aberration Analysis for Rapid Biological Dose Estimation of Ionizing Radiation.

Radiation dose assessment in exposed individuals relies on the dicentric assay, the gold-standard cytogenetic biodosimeter that quantifies radiation-i...

Predicting non-emergency healthcare use in Australia using machine learning on longitudinal household data.

The persistent increase in healthcare expenditure has become a major challenge for the sustainability of public financing worldwide. Therefore, identi...

ChatGPT-4o Mini Fabricates and Miscites Evidence for American Academy of Orthopaedic Surgeons Hip Fracture Clinical Practice Guidelines.

BACKGROUND: Generative artificial intelligence (AI) large language model (LLM) chatbots, such as ChatGPT, are increasingly used to answer medical ques...

Machine Learning-based Radiomic Model for Early Diagnosis of Male Urethral Injury in Pelvic Fracture Patients.

BACKGROUND AND OBJECTIVE: Pelvic fracture urethral injury (PFUI) is serious and requires prompt diagnosis. Traditional diagnostic methods, which rely ...

Hematologic markers and machine learning in predicting placenta accreta: A case-control study.

OBJECTIVE: This study aims to enhance antenatal detection of placenta accreta spectrum (PAS) and predict severe hemorrhage at delivery using machine l...

Transforming clinical toxicology in Singapore: four decades of progress and beyond.

By examining key milestones, challenges and future directions, this review chronicles the evolution of clinical toxicology in Singapore into a recogni...

ML-Assisted Olfactory Epidemiology Survey Urbanizing China: A Population-Based Study in Yancheng.

OBJECTIVES: Urbanization-related air pollution may be associated with olfactory dysfunction (OD) in China, yet population studies are lacking. METHODS...

Interpretable machine learning unveils non-linear inflammatory thresholds and synergistic interactions in post-burn hypertrophic scarring: development of an intelligent clinical decision support system.

Hypertrophic scarring (HS) following severe burns remains a persistent rehabilitative challenge, yet traditional linear prediction models fail to capt...

Advancements in psoriasis classification using custom transfer learning algorithms.

Psoriasis is a skin disorder which mainly occurs as a rash, scaly areas and an itchy skin. The symptoms usually occur on the chest, elbows, and the sc...

Performance Evaluation of a Commercial Deep Learning Software for Detecting Intracranial Hemorrhage in a Pediatric Population.

This study evaluates a commercially available AI tool (Aidoc) for intracranial hemorrhage (ICH) detection-originally trained on adults-in pediatric pa...

Rethinking Cardiopulmonary Bypass Management in The Digital Health Era.

Minimally invasive and robotic cardiac surgery have been developed to reduce surgical trauma, shorten recovery, and improve cosmetic and functional ou...

Integrating network toxicology, machine learning, and molecular dynamics simulations to reveal tanshinone iia's dual mechanisms in TNBC and doxorubicin-induced cardiotoxicity.

Doxorubicin (Dox)-induced cardiotoxicity remains a critical barrier to optimizing breast cancer (BC) treatment, highlighting the urgent need to dissec...

Predicting Traumatic Brain Injury Post-Trauma Using Temporal Attention on Sleep-Wake Data.

BACKGROUND: Traumatic Brain Injury (TBI) is a major public health concern, and accurate classification is essential for effective treatment and improv...

Graph Convolutional Networks Enable Fast Hemorrhagic Stroke Monitoring With Electrical Impedance Tomography.

OBJECTIVE: To develop a fast image reconstruction method for stroke monitoring with electrical impedance tomography with image quality comparable to c...

Patient attitudes toward ambient artificial intelligence scribes in clinical care: insights from a cross-sectional study.

OBJECTIVE: To assess patient attitudes towards ambient artificial intelligence (AI) scribes, including comfort, trust, perceived impact on provider in...

Monitoring the lateral ventricles in the presence of intracranial hemorrhage using automated dual segmentation.

BACKGROUND: Segmentation of intracranial hemorrhage (ICH) alongside the brain's ventricles can provide crucial information in the management stroke or...

Multisite derivation of a machine learning algorithm using high sensitivity troponin to predict major adverse cardiac events in the emergency department.

OBJECTIVE: To develop a machine learning (ML) algorithm to stratify risk for major adverse cardiac events (MACE) within 30 days in emergency departmen...