Emergency Medicine

Artificial Intelligence Decision Support for Medical Triage.

Applying state-of-the-art machine learning and natural language processing on approximately one million of teleconsultation records, we developed a triage system, now certified and in use at the largest European telemedicine provider. The system evaluates care alternatives through interactions with patients via a mobile application. Reasoning on an initial set of provided symptoms, the triage appl...

Machine learning combining CT findings and clinical parameters improves prediction of length of stay and ICU admission in torso trauma.

OBJECTIVE: To develop machine learning (ML) models capable of predicting ICU admission and extended length of stay (LOS) after torso (chest, abdomen, or pelvis) trauma, by using clinical and/or imaging data.

Review of deep learning algorithms for the automatic detection of intracranial hemorrhages on computed tomography head imaging.

Artificial intelligence is a rapidly evolving field, with modern technological advances and the growth of electronic health data opening new possibili...

An Automated Deep Learning Method for Tile AO/OTA Pelvic Fracture Severity Grading from Trauma whole-Body CT.

Admission trauma whole-body CT is routinely employed as a first-line diagnostic tool for characterizing pelvic fracture severity. Tile AO/OTA grade ba...

Added value of deep learning-based liver parenchymal CT volumetry for predicting major arterial injury after blunt hepatic trauma: a decision tree analysis.

PURPOSE: In patients presenting with blunt hepatic injury (BHI), the utility of CT for triage to hepatic angiography remains uncertain since simple bi...

A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis.

INTRODUCTION: Patients with sepsis who present to an emergency department (ED) have highly variable underlying disease severity, and can be categorize...

A unified machine learning approach to time series forecasting applied to demand at emergency departments.

BACKGROUND: There were 25.6 million attendances at Emergency Departments (EDs) in England in 2019 corresponding to an increase of 12 million attendanc...

In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department.

Background Classical ST-T waveform changes on standard 12-lead ECG have limited sensitivity in detecting acute coronary syndrome (ACS) in the emergenc...

Machine learning methods to improve bedside fluid responsiveness prediction in severe sepsis or septic shock: an observational study.

BACKGROUND: Passive leg raising (PLR) predicts fluid responsiveness in critical illness, although restrictions in mobilising patients often preclude t...

Early detection of sepsis using artificial intelligence: a scoping review protocol.

BACKGROUND: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. To decrease the high case fatality rat...

Predicting Progression to Septic Shock in the Emergency Department Using an Externally Generalizable Machine-Learning Algorithm.

STUDY OBJECTIVE: Machine-learning algorithms allow improved prediction of sepsis syndromes in the emergency department (ED), using data from electroni...

The Feature Ambiguity Mitigate Operator model helps improve bone fracture detection on X-ray radiograph.

This study was performed to propose a method, the Feature Ambiguity Mitigate Operator (FAMO) model, to mitigate feature ambiguity in bone fracture det...

Predicting adult neuroscience intensive care unit admission from emergency department triage using a retrospective, tabular-free text machine learning approach.

Early admission to the neurosciences intensive care unit (NSICU) is associated with improved patient outcomes. Natural language processing offers new ...

Use of Machine Learning to Re-Assess Patterns of Multivariate Functional Recovery after Fluid Percussion Injury: Operation Brain Trauma Therapy.

Traumatic brain injury (TBI) is a leading cause of death and disability. Yet, despite immense research efforts, treatment options remain elusive. Tran...

An artificial neural network based mathematical model for a stochastic health care facility location problem.

This research is conducted to investigate the problem of locating the trauma centers and helicopters' station in order to optimize the trauma care sys...

Towards a new model and classification of mood disorders based on risk resilience, neuro-affective toxicity, staging, and phenome features using the nomothetic network psychiatry approach.

Current diagnoses of mood disorders are not cross validated. The aim of the current paper is to explain how machine learning techniques can be used to...

Deep learning shows good reliability for automatic segmentation and volume measurement of brain hemorrhage, intraventricular extension, and peripheral edema.

OBJECTIVES: To evaluate for the first time the performance of a deep learning method based on no-new-Net for fully automated segmentation and volumetr...

Screening For Bone Marrow Cellularity Changes in Cynomolgus Macaques in Toxicology Safety Studies Using Artificial Intelligence Models.

Many compounds affect the cellularity of hematolymphoid organs including bone marrow. Toxicologic pathologists are tasked with their evaluation as par...

Effect of Machine Learning on Dispatcher Recognition of Out-of-Hospital Cardiac Arrest During Calls to Emergency Medical Services: A Randomized Clinical Trial.

IMPORTANCE: Emergency medical dispatchers fail to identify approximately 25% of cases of out-of-hospital cardiac arrest (OHCA), resulting in lost oppo...

Machine Learning Versus Usual Care for Diagnostic and Prognostic Prediction in the Emergency Department: A Systematic Review.

OBJECTIVE: Having shown promise in other medical fields, we sought to determine whether machine learning (ML) models perform better than usual care in...