Emergency Medicine

Ankle Fracture Detection Utilizing a Convolutional Neural Network Ensemble Implemented with a Small Sample, De Novo Training, and Multiview Incorporation.

To determine whether we could train convolutional neural network (CNN) models de novo with a small dataset, a total of 596 normal and abnormal ankle cases were collected and processed. Single- and multiview models were created to determine the effect of multiple views. Data augmentation was performed during training. The Inception V3, Resnet, and Xception convolutional neural networks were constru...

Identifying the Long-term Brain Changes Associated With Childhood Trauma Exposure Using Data-Driven Machine Learning Techniques.

Bundled Care for Hip Fractures: A Machine-Learning Approach to an Untenable Patient-Specific Payment Model.

OBJECTIVES: With the transition to a value-based model of care delivery, bundled payment models have been implemented with demonstrated success in ele...

Artificial neural networks can predict trauma volume and acuity regardless of center size and geography: A multicenter study.

BACKGROUND: Trauma has long been considered unpredictable. Artificial neural networks (ANN) have recently shown the ability to predict admission volum...

Artificial intelligence, osteoporosis and fragility fractures.

PURPOSE OF REVIEW: Artificial intelligence tools have found new applications in medical diagnosis. These tools have the potential to capture underlyin...

Comparison of Machine Learning Optimal Classification Trees With the Pediatric Emergency Care Applied Research Network Head Trauma Decision Rules.

IMPORTANCE: Computed tomographic (CT) scanning is the standard for the rapid diagnosis of intracranial injury, but it is costly and exposes patients t...

Training and Interpreting Machine Learning Algorithms to Evaluate Fall Risk After Emergency Department Visits.

BACKGROUND: Machine learning is increasingly used for risk stratification in health care. Achieving accurate predictive models do not improve outcomes...

A Robust Machine Learning Architecture for a Reliable ECG Rhythm Analysis during CPR.

Chest compressions delivered during cardiopulmonary resuscitation (CPR) induce artifacts in the ECG that may make the shock advice algorithms (SAA) of...

1D Convolutional Neural Networks for Estimation of Compensatory Reserve from Blood Pressure Waveforms.

We propose a Deep Convolutional Neural Network (CNN) architecture for computing a Compensatory Reserve Metric (CRM) for trauma victims suffering from ...

Representation Learning of 3D Brain Angiograms, an Application for Cerebral Vasospasm Prediction.

Stroke is the fifth leading cause of death in the United States. Subarachnoid hemorrhage (SAH) is a type of stroke often caused by the spontaneous rup...

Improving Sensitivity on Identification and Delineation of Intracranial Hemorrhage Lesion Using Cascaded Deep Learning Models.

Highly accurate detection of the intracranial hemorrhage without delay is a critical clinical issue for the diagnostic decision and treatment in an em...

Combining Natural Language Processing of Electronic Medical Notes With Administrative Data to Determine Racial/Ethnic Differences in the Disclosure and Documentation of Military Sexual Trauma in Veterans.

BACKGROUND: Despite national screening efforts, military sexual trauma (MST) is underreported. Little is known of racial/ethnic differences in MST rep...

A Systems Toxicology Approach for the Prediction of Kidney Toxicity and Its Mechanisms In Vitro.

The failure to predict kidney toxicity of new chemical entities early in the development process before they reach humans remains a critical issue. He...

Opportunities and challenges using artificial intelligence in ADME/Tox.

A recent conference organized a panel of scientists representing small and big pharma companies, who work at the interface of machine learning (ML) an...

Burn wound classification model using spatial frequency-domain imaging and machine learning.

Accurate assessment of burn severity is critical for wound care and the course of treatment. Delays in classification translate to delays in burn mana...

Development of an automatic classifier for the prediction of hearing impairment from industrial noise exposure.

The ISO-1999 [(2013). International Organization for Standardization, Geneva, Switzerland] standard is the most commonly used approach for estimating ...

Natural language processing and machine learning to identify alcohol misuse from the electronic health record in trauma patients: development and internal validation.

OBJECTIVE: Alcohol misuse is present in over a quarter of trauma patients. Information in the clinical notes of the electronic health record of trauma...

Continuous Glucose Monitoring Linked to an Artificial Intelligence Risk Index: Early Footprints of Intraventricular Hemorrhage in Preterm Neonates.

OBJECTIVE: To develop and validate a new risk score for intraventricular hemorrhage (IVH) in preterm neonates based on continuous glucose monitoring (...

Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department.

Early identification of high-risk septic patients in the emergency department (ED) may guide appropriate management and disposition, thereby improving...

Recent Advances in Machine Learning Methods for Predicting Heat Shock Proteins.

BACKGROUND: As molecular chaperones, Heat Shock Proteins (HSPs) not only play key roles in protein folding and maintaining protein stabilities, but ar...