Emergency Medicine

Using machine learning to increase access to and engagement with trauma-focused interventions for posttraumatic stress disorder.

BACKGROUND: Post-traumatic stress disorder (PTSD) poses a global public health challenge. Evidence-b...

Evaluating Deep Learning Techniques for Detecting Aneurysmal Subarachnoid Hemorrhage: A Comparative Analysis of Convolutional Neural Network and Transfer Learning Models.

OBJECTIVE: Machine learning and deep learning techniques offer a promising multidisciplinary solutio...

The application of deep learning in abdominal trauma diagnosis by CT imaging.

BACKGROUND: Abdominal computed tomography (CT) scan is a crucial imaging modality for creating cross...

Can Machine Learning Personalize Cardiovascular Therapy in Sepsis?

Large randomized trials in sepsis have generally failed to find effective novel treatments. This is ...

Intensive longitudinal assessment following index trauma to predict development of PTSD using machine learning.

There are significant challenges to identifying which individuals require intervention following exp...

Evaluating the Quality of Postpartum Hemorrhage Nursing Care Plans Generated by Artificial Intelligence Models.

BACKGROUND: With the rapidly advancing technological landscape of health care, evaluating the potent...

Enhancing pneumonia prognosis in the emergency department: a novel machine learning approach using complete blood count and differential leukocyte count combined with CURB-65 score.

BACKGROUND: Pneumonia poses a major global health challenge, necessitating accurate severity assessm...

Two-Stage Machine Learning-Based Approach to Predict Points of Departure for Human Noncancer and Developmental/Reproductive Effects.

Chemical points of departure (PODs) for critical health effects are crucial for evaluating and manag...

Adversarial attacks and adversarial training for burn image segmentation based on deep learning.

Deep learning has been widely applied in the fields of image classification and segmentation, while ...

Deep Omni-Supervised Learning for Rib Fracture Detection From Chest Radiology Images.

Deep learning (DL)-based rib fracture detection has shown promise of playing an important role in pr...

Implementation and Equity Are the Keys for the Future of Artificial Intelligence in Emergency Medicine.

Bringing Artificial Intelligence (AI) into Environmental Toxicology Studies: A Perspective of AI-Enabled Zebrafish High-Throughput Screening.

The booming development of artificial intelligence (AI) has brought excitement to many research fiel...

Coupling machine learning and theoretical models to compare key properties of biochar in adsorption kinetics rate and maximum adsorption capacity for emerging contaminants.

Insights into key properties of biochar with a fast adsorption rate and high adsorption capacity are...

Development of a Predictive Model for Survival Over Time in Patients With Out-of-Hospital Cardiac Arrest Using Ensemble-Based Machine Learning.

As of now, a model for predicting the survival of patients with out-of-hospital cardiac arrest has n...

Emergency department risk model: timely identification of patients for outpatient care coordination.

OBJECTIVE: Major depressive disorder (MDD) is linked to a 61% increased risk of emergency department...

Artificial Intelligence for Emergency Care Triage-Much Promise, but Still Much to Learn.

[ChatGPT for use in technology-enhanced learning in anesthesiology and emergency medicine and potential clinical application of AI language models : Between hype and reality around artificial intelligence in medical use].

BACKGROUND: The utilization of AI language models in education and academia is currently a subject o...

Exploring the Promise and Challenges of Artificial Intelligence in Biomedical Research and Clinical Practice.

Artificial intelligence (AI) is poised to revolutionize how science, and biomedical research in part...

Preclinical identification of acute coronary syndrome without high sensitivity troponin assays using machine learning algorithms.

Preclinical management of patients with acute chest pain and their identification as candidates for ...

Machine Learning Discrimination and Ultrasensitive Detection of Fentanyl Using Gold Nanoparticle-Decorated Carbon Nanotube-Based Field-Effect Transistor Sensors.

The opioid overdose crisis is a global health challenge. Fentanyl, an exceedingly potent synthetic o...

Predicting postoperative rehemorrhage in hypertensive intracerebral hemorrhage using noncontrast CT radiomics and clinical data with an interpretable machine learning approach.

In hypertensive intracerebral hemorrhage (HICH) patients, while emergency surgeries effectively redu...