Emergency Medicine

Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records.

BACKGROUND: Emergency admissions are a major source of healthcare spending. We aimed to derive, vali...

Supervised learning for bone shape and cortical thickness estimation from CT images for finite element analysis.

Knowledge about the thickness of the cortical bone is of high interest for fracture risk assessment....

A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation.

BACKGROUND: Cardiologs® has developed the first electrocardiogram (ECG) algorithm that uses a deep n...

Deep Learning for Triage of Chest Radiographs: Should Every Institution Train Its Own System?

Machine learning improves prediction of delayed cerebral ischemia in patients with subarachnoid hemorrhage.

BACKGROUND AND PURPOSE: Delayed cerebral ischemia (DCI) is a severe complication in patients with an...

Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks.

Magnetic resonance imaging (MRI) has been proposed as a complimentary method to measure bone quality...

Defining heatwave thresholds using an inductive machine learning approach.

Establishing appropriate heatwave thresholds is important in reducing adverse human health consequen...

Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms.

BACKGROUND: Convolution neural networks have been considered for automatic analysis of fundus images...

Deep Analysis of Mitochondria and Cell Health Using Machine Learning.

There is a critical need for better analytical methods to study mitochondria in normal and diseased ...

Machine learning to predict lung nodule biopsy method using CT image features: A pilot study.

Computed tomography (CT)-based screening on lung cancer mortality is poised to make lung nodule mana...

Antimicrobial peptide based magnetic recognition elements and Au@Ag-GO SERS tags with stable internal standards: a three in one biosensor for isolation, discrimination and killing of multiple bacteria in whole blood.

In this study, a new biosensor based on a sandwich structure has been developed for the isolation an...

A Machine Learning Shock Decision Algorithm for Use During Piston-Driven Chest Compressions.

GOAL: Accurate shock decision methods during piston-driven cardiopulmonary resuscitation (CPR) would...

Ensemble machine learning prediction of posttraumatic stress disorder screening status after emergency room hospitalization.

Posttraumatic stress disorder (PTSD) develops in a substantial minority of emergency room admits. In...

Intravenous Fluid for the Treatment of Emergency Department Patients With Migraine Headache: A Randomized Controlled Trial.

STUDY OBJECTIVE: The objective of this pilot study is to assess the feasibility and necessity of per...

A data-driven artificial intelligence model for remote triage in the prehospital environment.

In a mass casualty incident, the factors that determine the survival rate of injured patients are di...

A GC-MS method for the determination of furanylfentanyl and ocfentanil in whole blood with full validation.

PURPOSE: Fentanyl analogues are popular in recent years among drug addicts and have been related to ...

Deep neural network improves fracture detection by clinicians.

Suspected fractures are among the most common reasons for patients to visit emergency departments (E...

Machine Learning for Environmental Toxicology: A Call for Integration and Innovation.

Newborn self-inflating manual resuscitators: precision robotic testing of safety and reliability.

AIM: A controlled bench test was undertaken to determine the performance variability among a range o...

Novel screening criteria for post-traumatic venous thromboembolism by using D-dimer.

AIM: Because severe trauma patients frequently manifest coagulopathy, it is extremely important to d...

Validation of deep-learning-based triage and acuity score using a large national dataset.

AIM: Triage is important in identifying high-risk patients amongst many less urgent patients as emer...