Emergency Medicine

Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial.

BACKGROUND: Currently, physicians are limited in their ability to provide an accurate prognosis for COVID-19 positive patients. Existing scoring systems have been ineffective for identifying patient decompensation. Machine learning (ML) may offer an alternative strategy. A prospectively validated method to predict the need for ventilation in COVID-19 patients is essential to help triage patients, ...

Robot-assisted double screw fixation of minimally displaced scaphoid waist fracture nonunions or delayed unions without bone graft.

We retrospectively reviewed 12 minimally displaced fractures of the scaphoid waist in 12 patients who developed delayed or nonunions with or without conservative treatment. Mean time between injury and surgery was 6 months (range 3-12). The fractures were stabilized with double screws, which were percutaneously inserted with robot assistance, and without bone grafting. All fractures united at a me...

Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea.

BACKGROUND: Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The...

Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood.

BACKGROUND: Visual and auditory signs of patient functioning have long been used for clinical diagnosis, treatment selection, and prognosis. Direct me...

Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides.

Exercise-induced pulmonary hemorrhage (EIPH) is a common condition in sport horses with negative impact on performance. Cytology of bronchoalveolar la...

Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol.

Predicting crash injury severity is a crucial constituent of reducing the consequences of traffic crashes. This study developed machine learning (ML) ...

Machine Learning Approaches for Fracture Risk Assessment: A Comparative Analysis of Genomic and Phenotypic Data in 5130 Older Men.

The study aims were to develop fracture prediction models by using machine learning approaches and genomic data, as well as to identify the best model...

Acceptance and Commitment Therapy Delivered via a Mobile Phone Messaging Robot to Decrease Postoperative Opioid Use in Patients With Orthopedic Trauma: Randomized Controlled Trial.

BACKGROUND: Acceptance and commitment therapy (ACT) is a pragmatic approach to help individuals decrease avoidable pain.

Erratum: Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT Hemorrhage Challenge.

[This corrects the article DOI: 10.1148/ryai.2020190211.].

The automaton as a surgeon: the future of artificial intelligence in emergency and general surgery.

BACKGROUND: Artificial intelligence (AI) is a field involving computational simulation of human intelligence processes; these applications of deep lea...

Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach.

Deep learning techniques have recently made considerable advances in the field of artificial intelligence. These methodologies can assist psychologist...

Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning.

The practice of non-testing approaches in nanoparticles hazard assessment is necessary to identify and classify potential risks in a cost effective an...

Early Prediction of Acute Kidney Injury in the Emergency Department With Machine-Learning Methods Applied to Electronic Health Record Data.

STUDY OBJECTIVE: Acute kidney injury occurs commonly and is a leading cause of prolonged hospitalization, development and progression of chronic kidne...

Novel application of an automated-machine learning development tool for predicting burn sepsis: proof of concept.

Sepsis is the primary cause of burn-related mortality and morbidity. Traditional indicators of sepsis exhibit poor performance when used in this uniqu...

Magnetic Resonance Guided Navigation of Untethered Microgrippers.

Microsurgical tools offer a path to less invasive clinical procedures with improved access, reduced trauma, and better recovery outcomes. There are a ...

Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods.

In a digitally enabled healthcare setting, we posit that an individual's current location is pivotal for supporting many virtual care services-such as...

A machine learning approach for mortality prediction only using non-invasive parameters.

At present, the traditional scoring methods generally utilize laboratory measurements to predict mortality. It results in difficulties of early mortal...

Predictors of emergency department opioid administration and prescribing: A machine learning approach.

INTRODUCTION: The opioid epidemic has altered normative clinical perceptions on addressing both acute and chronic pain, particularly within the Emerge...

Outcome prediction with resting-state functional connectivity after cardiac arrest.

Predicting outcome in comatose patients after successful cardiopulmonary resuscitation is challenging. Our primary aim was to assess the potential con...

Early triage of critically ill COVID-19 patients using deep learning.

The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to ide...