Emergency Medicine

Large-scale proteomic profiling identifies distinct inflammatory phenotypes in acute respiratory distress syndrome: a multicentre, prospective cohort study.

BACKGROUND: Host responses during acute respiratory distress syndrome are highly heterogeneous, contributing to inconsistent therapeutic outcomes. Proteome-based phenotyping may identify biologically and clinically distinct phenotypes to guide precision therapy. METHODS: In this multicentre cohort study, we used latent class analysis of targeted serum proteomics to identify acute respiratory distr...

Factors and determinants of primary care to tertiary care referrals in Singapore: A multi-centre analysis using artificial intelligence-powered large language models.

BACKGROUND: As Singapore adopts a population health approach under Healthier Singapore (Healthier SG), optimizing healthcare resources is crucial. We examined referral reasons (using large language models [LLM]), wait times, and analyse factors affecting referrals from primary to tertiary care. METHODS: In 2023, 1,063,646 patient visits from seven primary care clinics in Singapore were analysed. P...

The application of artificial intelligence in cryopreservation: Technological advances and future challenges.

This review systematically sorts out the latest research progress and application status of artificial intelligence (AI) technology in the field of cr...

Temporal Evolution in Toxicity Drivers of Shale Gas Flowback and Produced Water: Bridging Compositional Dynamics to Risk Prediction.

Flowback and produced water (FPW) from shale gas extraction poses significant environmental risks due to its complex chemical composition. To address ...

Video-based diagnostics supported by artificial intelligence as an opportunity to address the epilepsy diagnostic gap: A narrative review.

Despite advancements in epilepsy care, a substantial diagnostic gap persists, particularly in resource-limited settings. This narrative review explore...

Forensic Transcriptomics: Research Progress of the Past Two Decades.

Over the last two decades, advancements in sequencing technology and data science have significantly deepened the study of transcriptomics, especially...

Evaluation of an artificial intelligence model for the identification of obstructive hydrocephalus on computed tomography of the head.

OBJECTIVE: Obstructive hydrocephalus is a critical radiographic finding requiring emergent treatment. Its identification on head CT by an AI model cou...

Clinical Profile of Neurosurgical Consultations from the Emergency Department : A Retrospective Analysis.

OBJECTIVE: This study aimed to retrospectively analyze consultations requested from the emergency departments (EDs) to the neurosurgery (NS) departmen...

Using deep learning to assess the toxicological effects of sublethal exposure of a novel green pesticide in a stored-product beetle.

BACKGROUND: Managing stored-grain pests requires new strategies to limit economic and health risks. This study analyses the sublethal effects of the n...

Fracture risk scores using output from an opportunistic screen of low bone density from conventional X-ray.

Fracture risk is commonly assessed by FRAX, a tool that estimates 10-yr risk for major osteoporotic fracture (MOF) and hip fracture. FRAX scores are o...

Spine age derived from DXA vertebral fracture assessment images predicts incident fractures and mortality: the Manitoba Bone Mineral Density Registry.

Biological age may better predict health outcomes than chronological age by capturing individual heterogeneity in aging. We investigated whether accel...

K-MIMIC: a nationwide Korean multi-institutional Multimodal intensive care dataset.

BACKGROUND: Recent advancements in critical care have highlighted the need for comprehensive, multimodal datasets to support clinical decision-making ...

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, ar...

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...