Emergency Medicine

Machine learning techniques for mortality prediction in emergency departments: a systematic review.

OBJECTIVES: This systematic review aimed to assess the performance and clinical feasibility of machi...

Artificial Intelligence in Cardiovascular Imaging: "Unexplainable" Legal and Ethical Challenges?

Nowhere is the influence of artificial intelligence (AI) likely to be more profoundly felt than in h...

Advances in Appropriate Postoperative Triage and the Role of Real-time Machine-Learning Models: The Goldilocks Dilemma.

Role of Digital Health During Coronavirus Disease 2019 Pandemic and Future Perspectives.

Coronavirus disease 2019 revolutionized the digital health care. This pandemic was the catalyst for ...

A deep learning model for burn depth classification using ultrasound imaging.

Identification of burn depth with sufficient accuracy is a challenging problem. This paper presents ...

Release of Hyaluronan in Aneurysmal Subarachnoid Hemorrhage and Cerebral Vasospasm: A Pilot Study Indicating a Shedding of the Endothelial Glycocalyx.

BACKGROUND: This pilot study investigated plasma concentrations of hyaluronan, heparan sulfate, and ...

Deep Learning Image Analysis of High-Throughput Toxicology Assay Images.

High-throughput chemical screening approaches often employ microscopy to capture photomicrographs fr...

Improving Machine Learning 30-Day Mortality Prediction by Discounting Surprising Deaths.

BACKGROUND: Machine learning (ML) is an emerging tool for predicting need of end-of-life discussion ...

Using Machine Learning to Establish Predictors of Mortality in Patients Undergoing Laparotomy for Emergency General Surgical Conditions.

INTRODUCTION: Patients undergoing laparotomy for emergency general surgery (EGS) conditions, constit...

Toward an Adaptive Threshold on Cooperative Bandwidth Management Based on Hierarchical Reinforcement Learning.

With the increase in Internet of Things (IoT) devices and network communications, but with less band...

Augmenting BDI Agency with a Cognitive Service: Architecture and Validation in Healthcare Domain.

Autonomous intelligent systems are starting to influence clinical practice, as ways to both readily ...

Curriculum learning for improved femur fracture classification: Scheduling data with prior knowledge and uncertainty.

An adequate classification of proximal femur fractures from X-ray images is crucial for the treatmen...

Predictive value of red blood cell distribution width in septic shock patients with thrombocytopenia: A retrospective study using machine learning.

BACKGROUND: Sepsis-associated thrombocytopenia (SAT) is common in critical patients and results in t...

Applications of Machine Learning in Bone and Mineral Research.

In this unprecedented era of the overwhelming volume of medical data, machine learning can be a prom...

Artificial Intelligence in Toxicological Pathology: Quantitative Evaluation of Compound-Induced Follicular Cell Hypertrophy in Rat Thyroid Gland Using Deep Learning Models.

Digital pathology has recently been more broadly deployed, fueling artificial intelligence (AI) appl...

Machine Learning for Workflow Applications in Screening Mammography: Systematic Review and Meta-Analysis.

Background Advances in computer processing and improvements in data availability have led to the dev...

Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score.

An early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emer...

Weakly supervised multitask learning models to identify symptom onset time of unclear-onset intracerebral hemorrhage.

BACKGROUND: Approximately one-third of spontaneous intracerebral hemorrhage patients did not know th...

Public Perception and Reception of Robotic Applications in Public Health Emergencies Based on a Questionnaire Survey Conducted during COVID-19.

Various intelligent technologies have been applied during COVID-19, which has become a worldwide pub...

A prehospital diagnostic algorithm for strokes using machine learning: a prospective observational study.

High precision is optimal in prehospital diagnostic algorithms for strokes and large vessel occlusio...

The feasibility of deep learning-based synthetic contrast-enhanced CT from nonenhanced CT in emergency department patients with acute abdominal pain.

Our objective was to investigate the feasibility of deep learning-based synthetic contrast-enhanced ...