Emergency Medicine

Machine learning-based prediction of short- and long-term mortality for shared decision-making in older hip fracture patients: the Dutch Hip Fracture Audit algorithms in 74,396 cases.

BACKGROUND AND PURPOSE:  Treatment-related shared decision-making (SDM) in older adults with hip fractures is complex due to the need to balance patient-specific factors such as life goals, frailty, and surgical risks. It includes considerations such as prognosis and decisions concerning whether to operate or not on frail, life-limited patients. We aimed to develop machine learning (ML)-driven pre...

Harnessing technology for hope: a systematic review of digital suicide prevention tools.

This systematic review scrutinizes digital interventions in suicide prevention, telehealth, mobile applications, artificial intelligence (AI), and digital psychotherapy. Apps with cognitive behavioural therapy (CBT) and crisis help worked well, but there were worries about keeping users engaged and data safe. AI tools were good at spotting suicide risk (72-93% accurate) by looking at social media ...

Discrimination of Dengue Diseases in Children Using Surface-Enhanced Raman Spectroscopy Coupled with Machine Learning Approaches.

This study introduces a novel approach to dengue diagnostics by leveraging surface-enhanced Raman spectroscopy (SERS) coupled to machine learning. Thi...

External validation of an artificial intelligence tool for fracture detection in children with osteogenesis imperfecta: a multireader study.

OBJECTIVE: To determine the performance of a commercially available AI tool for fracture detection when used in children with osteogenesis imperfecta ...

Improving prediction of fragility fractures in postmenopausal women using random forest.

Osteoporosis is a chronic disease characterized by a progressive decline in bone density and quality, leading to increased bone fragility and a higher...

Predictive modeling and machine learning show poor performance of clinical, morphological, and hemodynamic parameters for small intracranial aneurysm rupture.

Small intracranial aneurysms (SIAs) (< 5 mm) are increasingly detected due to advanced imaging, but predicting rupture risk remains challenging. Ruptu...

Development of a Risk Prediction Model for Linezolid-Induced Thrombocytopenia Based on the Machine Learning Algorithm.

The research aimed to develop a validated model for predicting the risk of linezolid-induced thrombocytopenia (LIT). An XGBoost model and SelectFromMo...

Identifying features of prior hemorrhage in cerebral cavernous malformations on quantitative susceptibility maps: a machine learning pilot study.

Features of new bleeding on conventional imaging in cerebral cavernous malformations (CCMs) often disappear after several weeks, yet the risk of reble...

Assessing risk of bias in toxicological studies in the era of artificial intelligence.

Risk of bias is a critical factor influencing the reliability and validity of toxicological studies, impacting evidence synthesis and decision-making ...

Predicting ESWL success for ureteral stones: a radiomics-based machine learning approach.

OBJECTIVES: This study aimed to develop and validate a machine learning (ML) model that integrates radiomics and conventional radiological features to...

Experimental and machine learning-driven assessment of IS 2062 steel under double-base propellant combustion conditions.

The degradation behavior of IS 2062 structural steel under extreme combustion conditions induced by double-base propellant exposure is crucial for aer...

How well does GPT-4 perform on an emergency medicine board exam? A comparative assessment.

BACKGROUND: Recent advancements in artificial intelligence have shown promise in enhancing diagnostic precision within healthcare sectors. In emergenc...

Cross-validation of an artificial intelligence tool for fracture classification and localization on conventional radiography in Dutch population.

OBJECTIVES: The aim of this study is to validate the effectiveness of an AI tool trained on Indian data in a Dutch medical center and to assess its ab...

Predicting Unplanned Intubations in Rib Fracture Patients: An Interpretable Machine Learning Approach.

BackgroundTraumatic rib fractures can lead to respiratory complications necessitating unplanned intubation, but predictors have been inadequately deli...

Clinical decision support using pseudo-notes from multiple streams of EHR data.

Electronic health records (EHR) contain data from disparate sources, spanning various biological and temporal scales. In this work, we introduce the M...

Spatial analysis of county-level determinants of overdose mortality in the United States using spatial machine learning.

In recent years, there has been a growing body of literature on identifying effective determinants for modeling the spatial variation of overdose rate...

Development and validation of a transformer model-based early warning score for real-time prediction of adverse outcomes in the emergency department.

This study aimed to develop and validate a transformer-based early warning score (TEWS) system for predicting adverse events (AEs) in the emergency de...

Parsimonious and explainable machine learning for predicting mortality in patients post hip fracture surgery.

Hip fractures among the elderly population continue to present significant risks and high mortality rates despite advancements in surgical procedures....

Clinical validation of AI assisted animal ultrasound models for diagnosis of early liver trauma.

The study aimed to develop an AI-assisted ultrasound model for early liver trauma identification, using data from Bama miniature pigs and patients in ...

AI in Learning Anatomy and Restoring Central Incisors: A Comparative Study.

More than 1 billion individuals worldwide have experienced dental trauma, particularly children aged 7 to 12 y, predominantly affecting the anterior t...