Emergency Medicine

Self-fulfilling prophecies and machine learning in resuscitation science.

INTRODUCTION: Growth of machine learning (ML) in healthcare has increased potential for observational data to guide clinical practice systematically. This can create self-fulfilling prophecies (SFPs), which arise when prediction of an outcome increases the chance that the outcome occurs.

Joint modeling strategy for using electronic medical records data to build machine learning models: an example of intracerebral hemorrhage.

BACKGROUND: Outliers and class imbalance in medical data could affect the accuracy of machine learning models. For physicians who want to apply predictive models, how to use the data at hand to build a model and what model to choose are very thorny problems. Therefore, it is necessary to consider outliers, imbalanced data, model selection, and parameter tuning when modeling.

Introduction to the Theme "Development of New Drugs: Moving from the Bench to Bedside and Improved Patient Care".

Investigations in pharmacology and toxicology range from molecular studies to clinical care. Studies in basic and clinical pharmacology and in preclin...

Predicting mortality in the very old: a machine learning analysis on claims data.

Machine learning (ML) may be used to predict mortality. We used claims data from one large German insurer to develop and test differently complex ML p...

Modular robotic platform for precision neurosurgery with a bio-inspired needle: System overview and first in-vivo deployment.

Over the past 10 years, minimally invasive surgery (MIS) has shown significant benefits compared to conventional surgical techniques, with reduced tra...

CORR Insights®: Deep Learning Assistance Closes the Accuracy Gap in Fracture Detection Across Clinician Types.

Biological signatures and prediction of an immunosuppressive status-persistent critical illness-among orthopedic trauma patients using machine learning techniques.

BACKGROUND: Persistent critical illness (PerCI) is an immunosuppressive status. The underlying pathophysiology driving PerCI remains incompletely unde...

Optimal planar leg geometry in robots and crabs for idealized rocky terrain.

Natural terrain is uneven so it may be beneficial to grasp onto the depressions or 'valleys' between obstacles when walking over such a surface. To ex...

Robotic esophagectomy and 3-field lymphadenectomy with intraoperative nerve monitoring.

The standard of care for esophageal malignancies has evolved over the years from open transthoracic esophagectomy to a minimally invasive approach due...

Assessment of automatic rib fracture detection on chest CT using a deep learning algorithm.

OBJECTIVES: To evaluate deep neural networks for automatic rib fracture detection on thoracic CT scans and to compare its performance with that of att...

Using Artificial Intelligence for Predicting the Duration of Emergency Evacuation During Hospital Fire.

OBJECTIVE: A danger threatening hospitals is fire. The most important action following a fire is to urgently evacuate the hospital during the shortest...

Clustering of trauma patients based on longitudinal data and the application of machine learning to predict recovery.

Predicting recovery after trauma is important to provide patients a perspective on their estimated future health, to engage in shared decision making ...

Emergency Information Communication Structure by Using Multimodel Fusion and Artificial Intelligence Algorithm.

With the development of The Times, social events are increasing, and emergency management has gradually become the main helper to solve the crisis in ...

The Impact of Cultural Embeddedness on the Implementation of an Artificial Intelligence Program at Triage: A Qualitative Study.

INTRODUCTION: Triage requires rapid determination of acuity and resources. Current modalities allow for individual judgment, with varied application o...

Systematic analysis of the test design and performance of AI/ML-based medical devices approved for triage/detection/diagnosis in the USA and Japan.

The development of computer-aided detection (CAD) using artificial intelligence (AI) and machine learning (ML) is rapidly evolving. Submission of AI/M...

Evaluation of advanced curve speed warning system to prevent fire truck rollover crashes.

INTRODUCTION: A disproportionately high number of deadly crash-incidents involve fire-tanker rollovers during emergency response driving. Most of thes...

Symmetry-Aware Deep Learning for Cerebral Ventricle Segmentation With Intra-Ventricular Hemorrhage.

Cerebral ventricles are one of the prominent structures in the brain, segmenting which can provide rich information for brain-related disease diagnosi...

Measuring the impact of simulation debriefing on the practices of interprofessional trauma teams using natural language processing.

BACKGROUND: Natural language processing (NLP) may be a tool for automating trauma teamwork assessment in simulated scenarios.

Prediction of Prognosis in Patients with Trauma by Using Machine Learning.

We developed a machine learning algorithm to analyze trauma-related data and predict the mortality and chronic care needs of patients with trauma. W...

Developing Role for Artificial Intelligence in Drug Discovery in Drug Design, Development, and Safety Assessment.

Artificial intelligence (AI) is a rapidly growing discipline in the field of chemical toxicology. Herein, we provide a broad overview of research pres...