Emergency Medicine

Machine learning for outcome predictions of patients with trauma during emergency department care.

OBJECTIVES: To develop and evaluate a machine learning model for predicting patient with trauma mort...

[Effectiveness of robot assisted percutaneous kyphoplasty for treatment of single/double-segment osteoporotic vertebral compression fractures].

OBJECTIVE: To compare the effectiveness of robot assisted and C-arm assisted percutaneous kyphoplast...

Deep-Learning-Based Diagnosis of Bedside Chest X-ray in Intensive Care and Emergency Medicine.

OBJECTIVES: Validation of deep learning models should separately consider bedside chest radiographs ...

[Clinical application of Orthopedic Tianji Robot in surgical treatment of thoracolumbar fracture].

OBJECTIVE: To explore the clinical application of Orthopedic Tianji Robot in the treatment of thorac...

Towards real-time diagnosis for pediatric sepsis using graph neural network and ensemble methods.

OBJECTIVE: The rapid onset of pediatric sepsis and the short optimal time for resuscitation pose a s...

Trauma outcome predictor: An artificial intelligence interactive smartphone tool to predict outcomes in trauma patients.

BACKGROUND: Classic risk assessment tools often treat patients' risk factors as linear and additive....

Predicting the Need For Vasopressors in the Intensive Care Unit Using an Attention Based Deep Learning Model.

BACKGROUND: Previous models on prediction of shock mostly focused on septic shock and often required...

Use of ColonFlag score for prioritisation of endoscopy in colorectal cancer.

OBJECTIVE: Colorectal cancer (CRC) is the fourth most common cancer in UK. Symptomatic patients are ...

Big data and predictive modelling for the opioid crisis: existing research and future potential.

A need exists to accurately estimate overdose risk and improve understanding of how to deliver treat...

Development of a field artificial intelligence triage tool: Confidence in the prediction of shock, transfusion, and definitive surgical therapy in patients with truncal gunshot wounds.

BACKGROUND: In-field triage tools for trauma patients are limited by availability of information, li...

Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction.

IMPORTANCE: Accurate prediction of adverse outcomes after acute myocardial infarction (AMI) can guid...

Rib fracture detection in computed tomography images using deep convolutional neural networks.

To evaluate the rib fracture detection performance in computed tomography (CT) images using a softwa...

Development and validation of a machine learning model to predict mortality risk in patients with COVID-19.

New York City quickly became an epicentre of the COVID-19 pandemic. An ability to triage patients wa...

Evaluating recommender systems for AI-driven biomedical informatics.

MOTIVATION: Many researchers with domain expertise are unable to easily apply machine learning (ML) ...

Prediction Models in Aneurysmal Subarachnoid Hemorrhage: Forecasting Clinical Outcome With Artificial Intelligence.

BACKGROUND: Predicting outcome after aneurysmal subarachnoid hemorrhage (aSAH) is known to be challe...

Machine Learning-Driven Metabolomic Evaluation of Cerebrospinal Fluid: Insights Into Poor Outcomes After Aneurysmal Subarachnoid Hemorrhage.

BACKGROUND: Aneurysmal subarachnoid hemorrhage (aSAH) is associated with a high mortality and poor n...

[Effect of rehabilitation robot rehabilitation training synchronizing acupuncture exercise therapy on postoperative rehabilitation with hip fracture].

OBJECTIVE: To compare the therapeutic effect between rehabilitation robot rehabilitation training sy...

Opportunities and challenges for the computational interpretation of rare variation in clinically important genes.

Genome sequencing is enabling precision medicine-tailoring treatment to the unique constellation of ...

Can artificial intelligence and machine learning help reduce the harms of emergency department crowding?

Towards Equitable AI Interventions for People Who Use Drugs: Key Areas That Require Ethical Investment.

There has been growing investment in artificial intelligence (AI) interventions to combat the opioid...

Emergency department frequent user subgroups: Development of an empirical, theory-grounded definition using population health data and machine learning.

Frequent emergency department (ED) use has been operationalized in research, clinical practice, and ...