Rheumatology

Latest AI and machine learning research in rheumatology for healthcare professionals.

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Bayesian machine learning to discover Bruton's tyrosine kinase inhibitors.

Bruton's tyrosine kinase (BTK) has a crucial role in multiple cell signaling pathways including B-ce...

Liquid chromatography-mass spectrometry method for the quantification of an anti-sclerostin monoclonal antibody in cynomolgus monkey serum.

Liquid chromatography tandem mass spectrometry (LC-MS/MS) has gradually become a promising alternati...

Machine-learning approach expands the repertoire of anti-CRISPR protein families.

The CRISPR-Cas are adaptive bacterial and archaeal immunity systems that have been harnessed for the...

Patterns of 1,748 Unique Human Alloimmune Responses Seen by Simple Machine Learning Algorithms.

Allele specific antibody response against the polymorphic system of HLA is the allogeneic response m...

Digital health technologies: opportunities and challenges in rheumatology.

The past decade in rheumatology has seen tremendous innovation in digital health technologies, inclu...

Deep learning based HEp-2 image classification: A comprehensive review.

Classification of HEp-2 cell patterns plays a significant role in the indirect immunofluorescence te...

Is there any association between low level of serum nesfatin-1 and fibromyalgia syndrome?

OBJECTIVES: This study aims to investigate the relationship between serum level of nesfatin-1 and fi...

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis.

Glomerular cell death is a pathological feature of myeloperoxidase anti neutrophil cytoplasmic antib...

Pediatric Acute-Onset Neuropsychiatric Syndrome: A Data Mining Approach to a Very Specific Constellation of Clinical Variables.

Pediatric acute onset neuropsychiatric syndrome (PANS) is a clinically heterogeneous disorder prese...

Colour Doppler ultrasound of temporal arteries for the diagnosis of giant cell arteritis: a multicentre deep learning study.

OBJECTIVES: Giant cell arteritis (GCA) is the most common systemic vasculitis in adults. In recent y...

Viewpoint on Time Series and Interrupted Time Series Optimum Modeling for Predicting Arthritic Disease Outcomes.

PURPOSE OF REVIEW: The propose of this viewpoint is to improve or facilitate the clinical decision-m...

Artificial intelligence may offer insight into factors determining individual TSH level.

The factors that determine Serum Thyrotropin (TSH) levels have been examined through different metho...

A Strictly Unsupervised Deep Learning Method for HEp-2 Cell Image Classification.

Classifying the images that portray the Human Epithelial cells of type 2 (HEp-2) represents one of t...

A semi-automated machine-learning based workflow for ellipsoid zone analysis in eyes with macular edema: SCORE2 pilot study.

BACKGROUND AND OBJECTIVE: To develop a semi-automated, machine-learning based workflow to evaluate t...

Identification of herbal categories active in pain disorder subtypes by machine learning help reveal novel molecular mechanisms of algesia.

Chronic pain is highly prevalent and poorly controlled, of which the accurate underlying mechanisms ...

A Machine Learning Approach for High-Dimensional Time-to-Event Prediction With Application to Immunogenicity of Biotherapies in the ABIRISK Cohort.

Predicting immunogenicity for biotherapies using patient and drug-related factors represents nowaday...

Diagnosis of Thyroid Nodule with New Ultrasound Imaging Modalities.

B-mode ultrasound (US) technology is an integral part of diagnosing and assessing risk stratificati...

Machine Learning Diagnostic Modeling for Classifying Fibromyalgia Using B-mode Ultrasound Images.

Fibromyalgia (FM) diagnosis remains a challenge for clinicians due to a lack of objective diagnostic...

Resilient fault-tolerant anti-synchronization for stochastic delayed reaction-diffusion neural networks with semi-Markov jump parameters.

This paper deals with the anti-synchronization issue for stochastic delayed reaction-diffusion neura...

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