Emergency Medicine

Latest AI and machine learning research in emergency medicine for healthcare professionals.

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Drivers and social implications of Artificial Intelligence adoption in healthcare during the COVID-19 pandemic.

The COVID-19 pandemic continues to impact people worldwide-steadily depleting scarce resources in he...

The Role of Machine Learning in Cardiovascular Pathology.

Machine learning has seen slow but steady uptake in diagnostic pathology over the past decade to ass...

Adrenal Insufficiency in Septic Patients Admitted to Intensive Care Unit: Prevalence and Associated Factors.

Adrenal insufficiency (AI) is associated with an increase in the risk of mortality in ICU-admitted ...

Lactate levels as a prognostic predict in cardiogenic shock under venoarterial extracorporeal membrane oxygenation support.

INTRODUCTION AND OBJECTIVES: Lactate and its evolution are associated with the prognosis of patients...

External validation of a commercially available deep learning algorithm for fracture detection in children.

PURPOSE: The purpose of this study was to conduct an external validation of a fracture assessment de...

New Models to Predict the Acute and Chronic Toxicities of Representative Species of the Main Trophic Levels of Aquatic Environments.

To assess the impact of chemicals on an aquatic environment, toxicological data for three trophic le...

Machine learning approach for the prediction of postpartum hemorrhage in vaginal birth.

Postpartum hemorrhage is the leading cause of maternal morbidity. Clinical prediction of postpartum ...

Deep Learning-Based Diagnosis Method of Emergency Colorectal Pathology.

One of the most common malignant tumors of the digestive tract is emergency colorectal cancer. In re...

Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study.

BACKGROUND: Histopathological assessment of transplant biopsies is currently the standard method to ...

Automated detection and segmentation of intracranial hemorrhage suspect hyperdensities in non-contrast-enhanced CT scans of acute stroke patients.

OBJECTIVES: Artif icial intelligence (AI)-based image analysis is increasingly applied in the acute ...

[Reflections on the state of the art].

If the progress made so far allows to save more and more lives, the resuscitation of 2021 is still a...

[Development of severity and mortality prediction models for covid-19 patients at emergency department including the chest x-ray].

OBJECTIVES: To develop prognosis prediction models for COVID-19 patients attending an emergency depa...

Natural language processing of head CT reports to identify intracranial mass effect: CTIME algorithm.

BACKGROUND: The Mortality Probability Model (MPM) is used in research and quality improvement to adj...

Classification of target tissues of Eisenia fetida using sequential multimodal chemical analysis and machine learning.

Acquiring comprehensive knowledge about the uptake of pollutants, impact on tissue integrity and the...

Development and Validation of a Deep Learning Strategy for Automated View Classification of Pediatric Focused Assessment With Sonography for Trauma.

OBJECTIVE: Pediatric focused assessment with sonography for trauma (FAST) is a sequence of ultrasoun...

Identifying clinical phenotypes in extremely low birth weight infants-an unsupervised machine learning approach.

There is increasing evidence that patient heterogeneity significantly hinders advancement in clinica...

Using explainable machine learning to identify patients at risk of reattendance at discharge from emergency departments.

Short-term reattendances to emergency departments are a key quality of care indicator. Identifying p...

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