Emergency Medicine

Use of Machine Learning to Develop a Risk-Stratification Tool for Emergency Department Patients With Acute Heart Failure.

STUDY OBJECTIVE: We use variables from a recently derived acute heart failure risk-stratification ru...

Automated Cerebral Hemorrhage Detection Using RAPID.

BACKGROUND AND PURPOSE: Intracranial hemorrhage (ICH) is an important event that is diagnosed on hea...

Validation of the Al-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) Calculator in Patients 65 Years and Older.

OBJECTIVE: We sought to assess the performance of the Predictive OpTimal Trees in Emergency Surgery ...

Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models.

In recent times, machine learning has become increasingly prominent in predictive toxicology as it h...

Assessing the risk of dengue severity using demographic information and laboratory test results with machine learning.

BACKGROUND: Dengue virus causes a wide spectrum of disease, which ranges from subclinical disease to...

Combining Data with Predictions for Modeling Hepatic Steatosis by Using Stratified Bagging and Conformal Prediction.

Hepatic steatosis (fatty liver) is a severe liver disease induced by the excessive accumulation of f...

Machine Learning Revealed New Correlates of Chronic Pelvic Pain in Women.

Chronic pelvic pain affects one in seven women worldwide, and there is an urgent need to reduce its ...

Improving rib fracture detection accuracy and reading efficiency with deep learning-based detection software: a clinical evaluation.

OBJECTIVES: To investigate the impact of deep learning (DL) on radiologists' detection accuracy and ...

Multimodal fusion with deep neural networks for leveraging CT imaging and electronic health record: a case-study in pulmonary embolism detection.

Recent advancements in deep learning have led to a resurgence of medical imaging and Electronic Medi...

Low-soluble TREM-like transcript-1 levels early after severe burn reflect increased coagulation disorders and predict 30-day mortality.

BACKGROUND: Patients with severe burns often show systemic coagulation changes in the early stage an...

Development of a Self-Harm Monitoring System for Victoria.

The prevention of suicide and suicide-related behaviour are key policy priorities in Australia and i...

Predicting the Individual Treatment Effect of Neurosurgery for Patients with Traumatic Brain Injury in the Low-Resource Setting: A Machine Learning Approach in Uganda.

Traumatic brain injury (TBI) disproportionately affects low- and middle-income countries (LMICs). In...

An Intelligent Augmented Reality Training Framework for Neonatal Endotracheal Intubation.

Neonatal Endotracheal Intubation (ETI) is a critical resuscitation skill that requires tremendous pr...

Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test.

BACKGROUND: The early clinical course of COVID-19 can be difficult to distinguish from other illness...

Fully automated detection and segmentation of intracranial aneurysms in subarachnoid hemorrhage on CTA using deep learning.

In aneurysmal subarachnoid hemorrhage (aSAH), accurate diagnosis of aneurysm is essential for subseq...

A randomized trial of robot-based distraction to reduce children's distress and pain during intravenous insertion in the emergency department.

OBJECTIVES: Our objectives were to evaluate the effectiveness of humanoid robot-based distraction on...

Machine learning to predict hemorrhage and thrombosis during extracorporeal membrane oxygenation.

Moderate to severe leukocytosis with vasopressor use is associated with increased mortality in trauma patients.

BACKGROUND: Leukocytosis is a rise in white blood cell (WBC) count and clinical outcomes of moderate...

Association of Serum Vitamin D Levels and Stress Fractures in Collegiate Athletes.

BACKGROUND: Low vitamin D levels along with high-intensity athletic training may put an athlete at i...

Iterative reconstruction and deep learning algorithms for enabling low-dose computed tomography in midfacial trauma.

OBJECTIVES: The objective of this study was to quantitatively assess the image quality of Advanced M...

Using Deep Learning Artificial Intelligence Algorithms to Verify N-Nitroso-N-Methylurea and Urethane Positive Control Proliferative Changes in Tg-RasH2 Mouse Carcinogenicity Studies.

In Tg-rasH2 carcinogenicity mouse models, a positive control group is treated with a carcinogen such...