Pain Management

Recognizing and explaining driving stress using a Shapley additive explanation model by fusing EEG and behavior signals.

Driving stress is a critical factor leading to road traffic accidents. Despite numerous studies that have been conducted on driving stress recognition, most of them only focus on accuracy improvement without taking model interpretability into account. In this study, an explainable driving stress recognition framework was presented to quantify stress based on electroencephalography (EEG) and behavi...

Moving towards the use of artificial intelligence in pain management.

BACKGROUND AND OBJECTIVE: While the development of artificial intelligence (AI) technologies in medicine has been significant, their application to acute and chronic pain management has not been well characterized. This systematic review aims to provide an overview of the current state of AI in acute and chronic pain management.

Predicting frailty in older patients with chronic pain using explainable machine learning: A cross-sectional study.

Frailty is common among older adults with chronic pain, and early identification is crucial in preventing adverse outcomes like falls, disability, and...

Crowdsourcing Adverse Events Associated With Monoclonal Antibodies Targeting Calcitonin Gene-Related Peptide Signaling for Migraine Prevention: Natural Language Processing Analysis of Social Media.

BACKGROUND: Clinical trials demonstrate the efficacy and tolerability of medications targeting calcitonin gene-related peptide (CGRP) signaling for mi...

Assessing polyomic risk to predict Alzheimer's disease using a machine learning model.

INTRODUCTION: Alzheimer's disease (AD) is the most common form of dementia in the elderly. Given that AD neuropathology begins decades before symptoms...

Automated acute pain prediction in domestic goats using deep learning-based models on video-recordings.

Facial expressions are essential in animal communication, and facial expression-based pain scales have been developed for different species. Automated...

Multi-lesion segmentation guided deep attention network for automated detection of diabetic retinopathy.

Accurate multi-lesion segmentation together with automated grading on fundus images played a vital role in diagnosing and treating diabetic retinopath...

Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivity.

Functional connectivity network (FCN) data from functional magnetic resonance imaging (fMRI) is increasingly used for the diagnosis of brain disorders...

Plasma protein-based identification of neuroimage-driven subtypes in mild cognitive impairment via protein-protein interaction aware explainable graph propagational network.

As an early indicator of dementia, mild cognitive impairment (MCI) requires specialized treatment according to its subtypes for the effective preventi...

Combining Fiber Bragg Grating and Artificial Intelligence Technologies for Supporting Epidural Procedures.

OBJECTIVE: Loss of resistance (LOR) is a widely accepted method for performing epidural punctures in clinical settings. However, the risk of failure a...

Sparse Coding Inspired LSTM and Self-Attention Integration for Medical Image Segmentation.

Accurate and automatic segmentation of medical images plays an essential role in clinical diagnosis and analysis. It has been established that integra...

Artificial intelligence in obstetric anaesthesiology - the future of patient care?

The use of artificial intelligence (AI) in obstetric anaesthesiology shows great potential in enhancing our practice and delivery of care. In this nar...

Discriminating fingerprints of chronic neuropathic pain following spinal cord injury using artificial neural networks and mass spectrometry analysis of female mice serum.

Spinal cord injury (SCI) often leads to central neuropathic pain, a condition associated with significant morbidity and is challenging in terms of the...

The emerging role of artificial intelligence in neuropathology: Where are we and where do we want to go?

The field of neuropathology, a subspecialty of pathology which studies the diseases affecting the nervous system, is experiencing significant changes ...

The Efficacy of a Food Supplement in the Treatment of Tinnitus with Comorbid Headache: A Statistical and Machine Learning Analysis with a Literature Review.

INTRODUCTION: Tinnitus, the perception of sound without an external auditory stimulus, affects approximately 10-15% of the population and is often ass...

Dynamic meta-graph convolutional recurrent network for heterogeneous spatiotemporal graph forecasting.

Spatiotemporal Graph (STG) forecasting is an essential task within the realm of spatiotemporal data mining and urban computing. Over the past few year...

DyGraphformer: Transformer combining dynamic spatio-temporal graph network for multivariate time series forecasting.

Transformer-based models demonstrate tremendous potential for Multivariate Time Series (MTS) forecasting due to their ability to capture long-term tem...

"Dictionary of immune responses" reveals the critical role of monocytes and the core target IRF7 in intervertebral disc degeneration.

BACKGROUND: Intervertebral disc degeneration (IDD) is widely regarded as the primary contributor to low back pain(LBP). As an immune-privileged organ,...

Amaurosis after Inferior Alveolar Nerve Block Injection in a Seven-Year-Old Girl: A Case Report and Review of the Literature.

A seven-year-old girl was referred for the treatment of her primary teeth. An inferior alveolar nerve block was administered using 1 mL of 2% lidocain...

Spatial patterns of rural opioid-related hospital emergency department visits: A machine learning analysis.

As opioid-related overdose emergency department visits continue to rise in the United States, there is a need to understand the location and magnitude...