Pain Management

Multiomic Network Analysis Identifies Dysregulated Neurobiological Pathways in Opioid Addiction.

BACKGROUND: Opioid addiction is a worldwide public health crisis. In the United States, for example,...

Hybrid contrastive multi-scenario learning for multi-task sequential-dependence recommendation.

Multi-scenario and multi-task learning are crucial in industrial recommendation systems to deliver h...

Data - Knowledge driven machine learning model for cancer pain medication decisions.

BACKGROUND: Cancer pain is one of the most common symptoms in cancer patients, and drug decision-mak...

Impact of artificial intelligence in managing musculoskeletal pathologies in physiatry: a qualitative observational study evaluating the potential use of ChatGPT versus Copilot for patient information and clinical advice on low back pain.

BACKGROUND: The self-management of low back pain (LBP) through patient information interventions off...

Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine.

Glaucoma is defined as progressive optic neuropathy that damages the structural appearance of the op...

Developing predictive models for residual back pain after percutaneous vertebral augmentation treatment for osteoporotic thoracolumbar compression fractures based on machine learning technique.

BACKGROUND: Machine learning (ML) has been widely applied to predict the outcomes of numerous diseas...

Time Efficiency and Ergonomic Assessment of a Robotic Wheelchair Transfer System.

: Caregivers experience high rates of occupational injuries, especially during wheelchair transfers,...

Identification of a machine learning-based diagnostic model for axial spondyloarthritis in rheumatological routine care using a random forest approach.

OBJECTIVES: In axial spondyloarthritis (axSpA), early diagnosis is crucial, but diagnostic delay rem...

Current update on the neurological manifestations of long COVID: more questions than answers.

Since the outbreak of the COVID-19 pandemic, there has been a global surge in patients presenting wi...

Precision Opioid Prescription in ICU Surgery: Insights from an Interpretable Deep Learning Framework.

PURPOSE: Appropriate opioid management is crucial to reduce opioid overdose risk for ICU surgical pa...

Revisiting the problem of learning long-term dependencies in recurrent neural networks.

Recurrent neural networks (RNNs) are an important class of models for learning sequential behavior. ...

Artificial intelligence chatbots versus traditional medical resources for patient education on "Labor Epidurals": an evaluation of accuracy, emotional tone, and readability.

BACKGROUND: Labor epidural analgesia is a widely used method for pain relief in childbirth, yet info...

Multi-scale region selection network in deep features for full-field mammogram classification.

Early diagnosis and treatment of breast cancer can effectively reduce mortality. Since mammogram is ...

The Development and Validation of an Artificial Intelligence Chatbot Dependence Scale.

In recent years, a plethora of artificial intelligence (AI) chatbots have been developed and made av...

Artificial intelligence and pain medicine education: Benefits and pitfalls for the medical trainee.

OBJECTIVES: Artificial intelligence (AI) represents an exciting and evolving technology that is incr...

Predicting Epidural Hematoma Expansion in Traumatic Brain Injury: A Machine Learning Approach.

IntroductionTraumatic brain injury (TBI) is a leading cause of disability and mortality worldwide, w...

Artificial Intelligence to Diagnose Complications of Diabetes.

Artificial intelligence (AI) is increasingly being used to diagnose complications of diabetes. Artif...

Artificial Intelligence in Interventional Pain Management: Opportunities, Challenges, and Future Directions.

Deep temporal representation learning for language identification.

Language identification (LID) is a key component in downstream tasks. Recently, the self-supervised ...

Classification of hand movements from EEG using a FusionNet based LSTM network.

. Accurate classification of electroencephalogram (EEG) signals is crucial for advancing brain-compu...

DAPNet: multi-view graph contrastive network incorporating disease clinical and molecular associations for disease progression prediction.

BACKGROUND: Timely and accurate prediction of disease progress is crucial for facilitating early int...