Emergency Medicine

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

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Showing 1870-1890 of 5,252 articles
Machine learning models predict coagulopathy in spontaneous intracerebral hemorrhage patients in ER.

AIMS: Coagulation abnormality is one of the primary concerns for patients with spontaneous intracere...

Artificial Intelligence Image-Assisted Knee Ligament Trauma Repair Efficacy Analysis and Postoperative Femoral Nerve Block Analgesia Effect Research.

OBJECTIVE: To analyze artificial intelligence image-assisted knee ligament injury repair and femoral...

Detection of Suicidality Among Opioid Users on Reddit: Machine Learning-Based Approach.

BACKGROUND: In recent years, both suicide and overdose rates have been increasing. Many individuals ...

Artificial Intelligence and Acute Stroke Imaging.

Artificial intelligence technology is a rapidly expanding field with many applications in acute stro...

Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death.

Despite the salient benefits of the intravenous tissue plasminogen activator (tPA), symptomatic intr...

DeepCOVID-XR: An Artificial Intelligence Algorithm to Detect COVID-19 on Chest Radiographs Trained and Tested on a Large U.S. Clinical Data Set.

Background There are characteristic findings of coronavirus disease 2019 (COVID-19) on chest images....

Deep-learning algorithms for the interpretation of chest radiographs to aid in the triage of COVID-19 patients: A multicenter retrospective study.

The recent medical applications of deep-learning (DL) algorithms have demonstrated their clinical ef...

Classification of femur trochanteric fracture: Evaluating the reliability of Tang classification.

INTRODUCTION: Given the drawbacks of a femoral intertrochanteric fracture classification based on 2-...

Classifying the superfamily of small heat shock proteins by using g-gap dipeptide compositions.

Small heat shock protein (sHSP) is a superfamily of molecular chaperone and is found from archaea to...

Using the National Trauma Data Bank (NTDB) and machine learning to predict trauma patient mortality at admission.

A 400-estimator gradient boosting classifier was trained to predict survival probabilities of trauma...

Machine Learning and Improved Quality Metrics in Acute Intracranial Hemorrhage by Noncontrast Computed Tomography.

OBJECTIVE: The timely reporting of critical results in radiology is paramount to improved patient ou...

Machine Learning to Predict Delayed Cerebral Ischemia and Outcomes in Subarachnoid Hemorrhage.

OBJECTIVE: To determine whether machine learning (ML) algorithms can improve the prediction of delay...

Electrically Activated Soft Robots: Speed Up by Rolling.

Soft robots show excellent body compliance, adaptability, and mobility when coping with unstructured...

Effect of cross-cultural differences on thickness, firmness and sweetness sensitivity.

Sensitivity of the somatosensory system may be influenced by multiple physiological parameters. Vari...

Deep-learning-assisted detection and segmentation of rib fractures from CT scans: Development and validation of FracNet.

BACKGROUND: Diagnosis of rib fractures plays an important role in identifying trauma severity. Howev...

Triage of documents containing protein interactions affected by mutations using an NLP based machine learning approach.

BACKGROUND: Information on protein-protein interactions affected by mutations is very useful for und...

A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT.

This project aimed to develop and evaluate a fast and fully-automated deep-learning method applying ...

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