Emergency Medicine

Simulating exposure-related behaviors using agent-based models embedded with needs-based artificial intelligence.

Exposure to a chemical is a critical consideration in the assessment of risk, as it adds real-world context to toxicological information. Descriptions of where and how individuals spend their time are important for characterizing exposures to chemicals in consumer products and in indoor environments. Herein we create an agent-based model (ABM) that simulates longitudinal patterns in human behavior...

Deep Learning in Drug Discovery and Medicine; Scratching the Surface.

The practice of medicine is ever evolving. Diagnosing disease, which is often the first step in a cure, has seen a sea change from the discerning hands of the neighborhood physician to the use of sophisticated machines to use of information gleaned from biomarkers obtained by the most minimally invasive of means. The last 100 or so years have borne witness to the enormous success story of allopath...

The Impact of Health Equity Coaching on Patient's Perceptions of Cultural Competency and Communication in a Pediatric Emergency Department: An Intervention Design.

PURPOSE: American Indian (AI) children experience significant disparities in health-care access. As a result, they are more likely to use the emergenc...

Improving the Accuracy of Scores to Predict Gastrostomy after Intracerebral Hemorrhage with Machine Learning.

BACKGROUND: Gastrostomy placement after intracerebral hemorrhage indicates the need for continued medical care and predicts patient dependence. Our ob...

Microanalysis of video from a robotic surgical procedure: implications for observational learning in the robotic environment.

Without haptic feedback, robotic surgeons rely on visual processing to interpret the operative field. To provide guidance for teaching in this environ...

The Elephant in the Room: Outbreak of Carfentanil Deaths in Minnesota and the Importance of Multiagency Collaboration.

Over a three-month period in early 2017, the Hennepin County Medical Examiner's Office investigated nine apparent opioid toxicity deaths that occurred...

A deep convolutional neural network approach for astrocyte detection.

Astrocytes are involved in various brain pathologies including trauma, stroke, neurodegenerative disorders such as Alzheimer's and Parkinson's disease...

Incidence of Remote Cerebellar Hemorrhage in Patients with a Dural Tear during Spinal Surgery: A Retrospective Observational Analysis.

INTRODUCTION: The incidence of remote cerebellar hemorrhage (RCH) in patients with a dural tear during spinal surgery is unclear. The purpose of this ...

Study on screening potential allergenic proteins from infant milk powders based on human mast cell membrane chromatography and histamine release assays.

Cow's milk allergy is mainly observed in infants and young children. Most allergic reactions affect the skin, followed by the gastrointestinal and res...

A rapid LC-MS/MS method for simultaneous determination of quetiapine and duloxetine in rat plasma and its application to pharmacokinetic interaction study.

Combinations of new antidepressants like duloxetine and second-generation antipsychotics like quetiapine are used in clinical treatment of major depre...

Automated deep-neural-network surveillance of cranial images for acute neurologic events.

Rapid diagnosis and treatment of acute neurological illnesses such as stroke, hemorrhage, and hydrocephalus are critical to achieving positive outcome...

Hybrid 3D/2D Convolutional Neural Network for Hemorrhage Evaluation on Head CT.

BACKGROUND AND PURPOSE: Convolutional neural networks are a powerful technology for image recognition. This study evaluates a convolutional neural net...

A Prediction Model Using Machine Learning Algorithm for Assessing Stone-Free Status after Single Session Shock Wave Lithotripsy to Treat Ureteral Stones.

PURPOSE: The aim of this study was to develop and validate a decision support model using a machine learning algorithm to predict treatment success af...

Predicting hospital admission at emergency department triage using machine learning.

OBJECTIVE: To predict hospital admission at the time of ED triage using patient history in addition to information collected at triage.

Estimating risk of severe neonatal morbidity in preterm births under 32 weeks of gestation.

A large recent study analyzed the relationship between multiple factors and neonatal outcome and in preterm births. Study variables included the reas...

Artificial intelligence and machine learning in emergency medicine.

Interest in artificial intelligence (AI) research has grown rapidly over the past few years, in part thanks to the numerous successes of modern machin...

Adverse Outcomes due to Aggressive Fluid Resuscitation in Children: A Prospective Observational Study.

Fluid management has a major impact on the duration, severity, and outcome of critically ill children. The aim of this study was to examine the relati...

Dynamic Covalent Chemistry at Interfaces: Development of Tougher, Healable Composites through Stress Relaxation at the Resin-Silica Nanoparticles Interface.

The interfacial region in composites that incorporate filler materials of dramatically different modulus relative to the resin phase acts as a stress ...

Assessment of the Feasibility of automated, real-time clinical decision support in the emergency department using electronic health record data.

BACKGROUND: The use of big data and machine learning within clinical decision support systems (CDSSs) has the potential to transform medicine through ...

Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED.

OBJECTIVE: The prediction of emergency department (ED) disposition at triage remains challenging. Machine learning approaches may enhance prediction. ...