Application of Machine Learning in Rheumatoid Arthritis Diseases Research: Review and Future Directions.
Machine learning
artificial intelligence
deep learning
rheumatoid arthritis
supervised learning
unsupervised learning
Journal
Combinatorial chemistry & high throughput screening
ISSN: 1875-5402
Titre abrégé: Comb Chem High Throughput Screen
Pays: United Arab Emirates
ID NLM: 9810948
Informations de publication
Date de publication:
2023
2023
Historique:
received:
27
09
2022
revised:
13
12
2022
accepted:
22
12
2022
medline:
27
6
2023
pubmed:
7
3
2023
entrez:
6
3
2023
Statut:
ppublish
Résumé
Rheumatoid arthritis (RA) is a chronic, destructive condition that affects and destroys the joints of the hand, fingers, and legs. Patients may forfeit the ability to conduct a normal lifestyle if neglected. The requirement for implementing data science to improve medical care and disease monitoring is emerging rapidly as a consequence of advancements in computational technologies. Machine learning (ML) is one of these approaches that has emerged to resolve complicated issues across various scientific disciplines. Based on enormous amounts of data, ML enables the formulation of standards and drafting of the assessment process for complex diseases. ML can be expected to be very beneficial in assessing the underlying interdependencies in the disease progression and development of RA. This could perhaps improve our comprehension of the disease, promote health stratification, optimize treatment interventions, and speculate prognosis and outcomes.
Identifiants
pubmed: 36876833
pii: CCHTS-EPUB-130013
doi: 10.2174/1386207326666230306114626
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
2259-2266Informations de copyright
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