Fibromyalgia

Toward Scalable Electromyography in Oncology: A Narrative Review of Normalization Challenges and Machine Learning Innovations.

OBJECTIVES: Electromyography (EMG) is increasingly applied in oncology to monitor neuromuscular impairment, treatment toxicities, and rehabilitation outcomes. However, reliance on maximal voluntary contraction (MVC) normalization limits scalability, as many patients cannot perform safe and reliable MVCs due to fatigue, pain, or treatment-induced impairments. This narrative review evaluates the fea...

Developing and Validating a Prediction Model for the Severe Pain-Fatigue-Sleep Disturbance Symptom Cluster in Patients with Lung Cancer Following Chemotherapy: A Machine Learning Analysis.

OBJECTIVES: Pain-fatigue-sleep disturbance symptom (PFS) cluster is the most common symptom cluster in patients with lung cancer following chemotherapy, which significantly impacts their quality of life. This study aims to develop and validate a machine learning-based prediction model for the severe PFS cluster and identify the relevant factors in patients with lung cancer following chemotherapy. ...

DoubleBlock-ViT: A MaxViT-based enhancement and dual-path skip connections for brain tumor segmentation in MRI scans.

BACKGROUND: Manual interpretation of brain tumor regions in MRI scans demands substantial medical expertise, is time-consuming, and is prone to human ...

SET-DGCN: An end-to-end electroencephalography-based fatigue detection method for young drivers.

Driver fatigue poses a critical threat to global road safety, particularly among young drivers. Nevertheless, policy-level interventions remain fragme...

MSDA-Net: Multiscale Spatiotemporal Dual-Attention Network for EEG-Based Driver Fatigue Detection.

Driver fatigue poses a severe risk to road safety, contributing to approximately 20% of fatal accidents worldwide. While EEG signals are the gold stan...

An adaptive decoupling learning system informed by the brain functional structure for EEG decoding.

Neuroscientific investigations have revealed the presence of regional pathway connections among functional brain areas, as well as the asymmetrical st...

Real-time fatigue monitoring system for motorcycle riders using sensors and a random forest algorithm.

Fatigue is a major determinant of motorcycle accidents, posing a critical threat to road safety by impairing riders' psychophysiological performance a...

Technical system of electroencephalography-based brain-computer interface: Advances, applications, and challenges.

Electroencephalography-based brain-computer interfaces have revolutionized the integration of neural signals with technological systems, offering tran...

Clinical Decision Support Systems in Generalist Practice: Utilizing Clinical Decision Support Systems Tools to Improve Clinical Decisions and Patient Outcomes.

Clinical decision support systems (CDSS) can impact clinical care through improved care processes and improved patient outcomes. For the generalist, C...

Deep learning-based quantitative assessment of renal chronicity indices in lupus nephritis.

Renal chronicity indices (CI) have been identified as strong predictors of long-term outcomes in lupus nephritis (LN) patients. However, assessment by...

Advances in non-invasive diagnostic tools for endometriosis: A narrative review of the past ten years.

Endometriosis is a chronic condition defined by endometrium-like tissue outside the uterus, with symptoms including pain, infertility, and fatigue. De...

A Participatory Artificial Intelligence Driven Shift-Scheduling Application for Improving Sleep Among Shift-Working Caregivers: A 4-Month Non-Randomised Controlled Study With Cross-Over Design.

Here, we examine the effectiveness of a participatory artificial intelligence (AI)-driven shift-scheduling mobile application (which reflects the loca...

A Scoping Review on AI-Supported Interventions for Non-Pharmacological Management of Chronic Rheumatic Diseases.

This review summarizes AI-supported non-pharmacological interventions for adults with chronic rheumatic diseases, detailing their components, purpose,...

Exploiting heart rate variability for driver drowsiness detection using wearable sensors and machine learning.

Driver drowsiness is a critical issue in transportation systems and a leading cause of traffic accidents. Common factors contributing to accidents inc...

Inflammatory, fibrotic and endothelial biomarker profiles in COVID-19 patients during and following hospitalization.

Survivors of severe COVID-19 often suffer from long-term respiratory issues, but the molecular drivers of this damage remain unclear. This study explo...

Robot-Assisted Retrograde Intrarenal Surgery: A Mini Review of the Literature on Robotic Surgery Platforms for Endoscopic Stone Surgery.

Retrograde intrarenal surgery (RIRS) has become a cornerstone in renal stone management, with robotic platforms recently entering clinical practice. T...

Experimental and machine learning-driven assessment of IS 2062 steel under double-base propellant combustion conditions.

The degradation behavior of IS 2062 structural steel under extreme combustion conditions induced by double-base propellant exposure is crucial for aer...

Advanced machine learning applications in fibromyalgia to assess the relationship between 3D spinal alignment with clinical outcomes.

This study leveraged machine learning (ML) models to explore the relationship between three-dimensional (3D) spinal alignment parameters and clinical ...

Exploring the Acceptance and Opportunities of Using a Specific Generative AI Chatbot to Assist Parents in Managing Pediatric Rheumatological Chronic Health Conditions: Mixed Methods Study.

BACKGROUND: Health care chatbots can be used to support patients and their families with everyday decision-making. While there is some research on int...

A machine learning model using clinical notes to identify physician fatigue.

Clinical notes should capture important information from a physician-patient encounter, but they may also contain signals indicative of physician fati...