Pain Management

TASC: a time-aware sequence clustering framework with uncertainty quantification for electronic health record trajectories.

Longitudinal electronic health record (EHR) trajectories are highly heterogeneous, sparse, and irregular, making unsupervised temporal pattern discovery and uncertainty quantification challenging. We developed a Time-Aware Sequence Clustering (TASC) framework that integrates temporal spacing, clinical semantic similarity, and probabilistic subtype characterization to identify longitudinal trajecto...

Artificial intelligence in pediatric pain: a systematic review.

BACKGROUND: Pain is a challenging, multifaceted symptom reported by most pediatric patients. This systematic review aims to explore the progress and effectiveness of applying artificial intelligence (AI) technology in pediatric pain management. METHODS: A comprehensive search of PubMed, Embase, Web of Science, Cochrane, Scopus, IEEE Xplore, ACM Library, and ClinicalTrials.gov was conducted. The se...

Clinical indicators associated with pericardial effusion in rheumatoid arthritis: a machine learning-based analysis.

BACKGROUND: Pericardial effusion (PE) is a frequent yet underdiagnosed complication of rheumatoid arthritis (RA), with substantial mortality risk. Nev...

EXPRESS: Transcriptome profiling and experimental validation identify FOS, RAC2, and TYROBP as potential biomarkers for Fu's subcutaneous needling in neuropathic pain treatment.

BACKGROUND: This study aimed to explore potential biomarkers and mechanisms underlying in the treatment of neuropathic pain(NP) with Fu's subcutaneous...

Fluorescence spectroscopy and machine learning methods for detection of Alzheimer's disease from circulating white blood cells.

BackgroundAlzheimer's disease (AD) is the most common cause of dementia whose prevalence is projected to increase significantly in the coming decades....

GMC-Bind: A Multimodal Framework for RNA-Protein Binding Site Prediction with Bidirectional Cross-Attentional Fusion.

Accurate identification of RNA-protein binding sites is crucial for understanding gene regulation and disease mechanisms. However, existing deep learn...

An explainable meta-learned hybrid CNN-transformer model with dual attention for leukemia diagnosis from peripheral blood smears.

Acute Lymphoblastic Leukemia (ALL) is one of the most aggressive hematological malignancies, and its early diagnosis remains challenging due to non-sp...

ScaHybNet: a scalogram-based hybrid ensemble network for ECG arrhythmia classification.

Cardiovascular diseases are the leading cause of death in the world, requiring the accurate and timely detection of arrhythmias to prevent sudden card...

Tri-MCA fusion: cross-modal attention and dynamic gating for multimodal sentiment analysis.

Multimodal sentiment analysis aims to automatically infer human emotions by jointly analyzing information from multiple modalities, such as text, audi...

Accuracy of machine learning in predicting recurrence risk of lumbar disc herniation after percutaneous endoscopic lumbar discectomy: a systematic review and meta-analysis.

BACKGROUND: Lumbar disc herniation (LDH) is a major cause of low back pain and radicular leg pain. Percutaneous endoscopic lumbar discectomy (PELD) is...

Cross-study interpretable machine learning for prediction of NH3 and H2S emissions and identification of operational windows during composting.

Accurate prediction of NH3 and H2S emissions is essential for odor-risk control and process management during composting, but remains challenging beca...

Bioactive compounds for neuroinflammation and neuropathic pain management: molecular and cellular mechanisms.

Bioactive compounds are a promising multi-target strategy for managing neuroinflammation and chronic pain. Bioactive compounds such as Paeonol, Kaempf...

EfficientCovNet: Modeling the Pairwise Voxel Dependency for Brain ROI Segmentation.

Segmenting the brain magnetic resonance (MR) images to region-of-interest (ROI) is a fundamental step for many medical image analysis tasks. Convoluti...

Mixed-methods study on GenAI Usage, dependence behaviors, and standardized application paths among Chinese medical students.

Generative artificial intelligence (GenAI) is reshaping medical education while fostering technological dependence among students. This study employed...

FSSM-DDI: Fusion State Space Model for predicting drug-drug interaction using social-media and drug descriptions.

BACKGROUND: Predicting drug-drug interactions (DDIs) from social-media and drug descriptions is crucial for healthcare, drug regulation, and pharmaceu...

Comorbidities for Predicting Progression Independent of Relapse Activity in Multiple Sclerosis Treated With B-Cell Depletion.

BACKGROUND: Progression independent of relapse activity (PIRA) has been shown to account for a majority of the disability accumulation in relapsing-re...

Identification of Pathological Secretions in Bronchoscopic Images Based on Edge Features and Multi-Scale Attention Mechanism.

The accumulation of pathological bronchial secretions compromises ventilation and oxygenation in critically ill patients and may lead to atelectasis o...

Feasibility and Preliminary Effectiveness of the ChulaCancer Mobile Chatbot for Supportive Care of Patients With Breast or Colorectal Cancer Receiving Chemotherapy: Pilot Randomized Controlled Trial.

BACKGROUND: Chemotherapy-related toxicities often lead to unscheduled health care use and diminished quality of life. Digital health interventions, su...

Carfentanil in the illicit drug supply: A machine learning approach for trace detection using surface-enhanced Raman spectroscopy.

BACKGROUND: Carfentanil is an extremely potent synthetic fentanyl analogue often present at trace levels alongside other fentanyl analogues and long-a...

Machine learning models for predicting postpartum convulsions using clinical indicators from PMA Ethiopia data.

Postpartum convulsions, defined as seizure episodes occurring after childbirth during the postpartum period, remain a major cause of maternal morbidit...