Machine learning in rheumatology approaches the clinic

Machine learning in rheumatology approaches the clinic

Machine learning and high-throughput technologies hold promise for the classification, diagnosis and treatment of patients with rheumatic diseases, with the ultimate goal of precision medicine. Several studies in 2019 highlight the feasibility and clinical utility of using machine learning in rheuma...

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Bibliographic Details
Published in:Nature reviews. Rheumatology 2020-02, Vol.16 (2), p.69-70
Main Authors: Pandit, Aridaman, Radstake, Timothy R D J
Format: Article
Language:eng
Subjects:
Adalimumab
Arthritis
Arthritis, Rheumatoid - diagnosis
Arthritis, Rheumatoid - drug therapy
Big Data
Care and treatment
Clinical Trials as Topic
Health aspects
High-Throughput Screening Assays
Learning algorithms
Machine Learning
Medical research
Medicine, Experimental
Models, Theoretical
Patients
Practice
Precision Medicine
Rheumatoid arthritis
Rheumatoid factor
Rheumatology
Tumor necrosis factor
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Summary:Machine learning and high-throughput technologies hold promise for the classification, diagnosis and treatment of patients with rheumatic diseases, with the ultimate goal of precision medicine. Several studies in 2019 highlight the feasibility and clinical utility of using machine learning in rheumatology to stratify patients and/or predict treatment responses. Key advances Synovial transcriptomic analysis and a machine learning-based approach identified subgroups of patients with rheumatoid arthritis (RA) and enabled the development of a model that could predict treatment response to TNF inhibition.sup.2. A machine learning-based model, developed as part of a crowdsourced open competition, could predict changes in disease activity and predict the treatment response of patients with RA.sup.3. Analysis of patterns of joint involvement and a machine learning-based approach enabled the development of a model that could predict the disease course of patients with juvenile idiopathic arthritis.sup.4.
ISSN:1759-4790
1759-4804