Emergency Medicine

Comparative analysis of machine learning approaches for predicting frequent emergency department visits.

BACKGROUND: Emergency Department (ED) overcrowding is an emerging risk to patient safety. This study aims to assess and compare the predictive ability of machine learning (ML) models for predicting frequent ED users.

Clinical decision support for severe trauma patients: Machine learning based definition of a bundle of care for hemorrhagic shock and traumatic brain injury.

BACKGROUND: Deviation from guidelines is frequent in emergency situations, and this may lead to increased mortality. Probably because of time constraints, 55% is the greatest reported guidelines compliance rate in severe trauma patients. This study aimed to identify among all available recommendations a reasonable bundle of items that should be followed to optimize the outcome of hemorrhagic shock...

A New Time-Window Prediction Model For Traumatic Hemorrhagic Shock Based on Interpretable Machine Learning.

Early warning prediction of traumatic hemorrhagic shock (THS) can greatly reduce patient mortality and morbidity. We aimed to develop and validate mod...

Electronic health record machine learning model predicts trauma inpatient mortality in real time: A validation study.

INTRODUCTION: Patient outcome prediction models are underused in clinical practice because of lack of integration with real-time patient data. The ele...

Transfer Learning of the ResNet-18 and DenseNet-121 Model Used to Diagnose Intracranial Hemorrhage in CT Scanning.

OBJECTIVE: The aim of the study was to verify the ability of the deep learning model to identify five subtypes and normal images in non-contrast enhan...

Use of deep learning methods for hand fracture detection from plain hand radiographs.

BACKGROUND: Patients with hand trauma are usually examined in emergency departments of hospitals. Hand fractures are frequently observed in patients w...

An Early Warning System Using Machine Learning for the Detection of Intracranial Hematomas in the Emergency Trauma Setting.

AIM: To present an early warning system (EWS) that employs a supervised machine learning algorithm for the rapid detection of extra-axial hematomas (E...

[Percellome Project: research on molecular mechanisms of toxicological responses based on transcriptomics and epigenetics].

We are constructing the "Percellome Database" containing many transcriptomes of mice exposed to a series of chemicals to elucidate the molecular mecha...

Recent Updates and Technological Developments in Evaluating Cardiac Syncope in the Emergency Department.

Syncope is a commonly encountered problem in the emergency department (ED), accounting for approximately 3% of presenting complaints. Clinical assessm...

Accuracy of the Traditional COVID-19 Phone Triaging System and Phone Triage-Driven Deep Learning Model.

OBJECTIVES: During the COVID-19 pandemic, a quick and reliable phone-triage system is critical for early care and efficient distribution of hospital r...

The potential applications of artificial intelligence in drug discovery and development.

Development of a new dug is a very lengthy and highly expensive process since only preclinical, pharmacokinetic, pharmacodynamic and toxicological stu...

Predicting outcomes after trauma: Prognostic model development based on admission features through machine learning.

In an overcrowded emergency department (ED), trauma surgeons and emergency physicians need an accurate prognostic predictor for critical decision-maki...

Deep Learning Algorithm to Predict Need for Critical Care in Pediatric Emergency Departments.

BACKGROUND AND OBJECTIVES: Emergency department (ED) overcrowding is a national crisis in which pediatric patients are often prioritized at lower leve...

Early Identification of Trauma-induced Coagulopathy: Development and Validation of a Multivariable Risk Prediction Model.

OBJECTIVE: The aim of this study was to develop and validate a risk prediction tool for trauma-induced coagulopathy (TIC), to support early therapeuti...

Are rib fractures stable? An analysis of progressive rib fracture offset in the acute trauma setting.

BACKGROUND: Rib fractures serve as both a marker of injury severity and a guide for clinical decision making for trauma patients. Although recent stud...

Augmenting Osteoporosis Imaging with Machine Learning.

PURPOSE OF REVIEW: In this paper, we discuss how recent advancements in image processing and machine learning (ML) are shaping a new and exciting era ...

Interpretation and reporting of predictive or diagnostic machine-learning research in Trauma & Orthopaedics.

There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for v...

Artificial intelligence in musculoskeletal conditions.

Artificial intelligence (AI) is an iterative process by which information is captured, transformed into knowledge and processed to produce adaptive ch...

New Method for Surgical Diagnostics - a Robotic Telemedical Approach.

Apart from the tremendous increase in the demand for telemedicine during the COVID-19 pandemic, the use of telemedical technology offers many advantag...

Artificial Neural Networks Predict 30-Day Mortality After Hip Fracture: Insights From Machine Learning.

OBJECTIVES: Accurately stratifying patients in the preoperative period according to mortality risk informs treatment considerations and guides adjustm...