Pain Management

Application of machine learning to identify risk factors for outpatient opioid prescriptions following spine surgery.

INTRODUCTION: Spine surgery is a common source of narcotic prescriptions and carries potential for long-term opioid dependence. As prescription opioids play a role in nearly 25 % of all opioid overdose deaths in the United States, mitigating risk for prolonged postoperative opioid utilization is crucial for spine surgeons.

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, opioids cause more drug overdose deaths than any other substance. However, opioid addiction treatments have limited efficacy, meaning that additional treatments are needed.

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-making in cancer pain management remains challenges. ...

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 offers significant benefits in terms of cost, reduced...

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 optic nerve head and is characterized by permanent b...

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 diseases. The current study aimed to develop a prognosti...

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 remains long and diagnostic criteria do not exist. We...

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

: Caregivers experience high rates of occupational injuries, especially during wheelchair transfers, which often result in back pain and musculoskelet...

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 patients, which can lead to severe complications. Ac...

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 with prolonged or late-onset debilitating sequelae o...

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. However, training RNNs to learn long-term dependen...

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 available to the public. Consequently, an increasing...

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 information accessibility for expectant mothers remain...

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, with epidural hematoma (EDH) being a severe consequ...

Artificial Intelligence to Diagnose Complications of Diabetes.

Artificial intelligence (AI) is increasingly being used to diagnose complications of diabetes. Artificial intelligence is technology that enables comp...

Deep temporal representation learning for language identification.

Language identification (LID) is a key component in downstream tasks. Recently, the self-supervised speech representation learned by Wav2Vec 2.0 (W2V2...

Computer tomography-based radiomics combined with machine learning for predicting the time since onset of epidural hematoma.

Estimation of the age of epidural hematoma (EDH) is a challenge in clinical forensic medicine, and this issue has yet to be conclusively resolved. The...

Machine learning insights into scapular stabilization for alleviating shoulder pain in college students.

Non-specific shoulder pain is a common musculoskeletal condition, especially among college students, and it can have a negative impact on the patient'...

Use of machine learning to identify prognostic variables for outcomes in chronic low back pain treatment: a retrospective analysis.

OBJECTIVES: Most patients seen in physical therapy (PT) clinics for low back pain (LBP) are treated for chronic low back pain (CLBP), yet PT intervent...

Automated video-based pain recognition in cats using facial landmarks.

Affective states are reflected in the facial expressions of all mammals. Facial behaviors linked to pain have attracted most of the attention so far i...