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
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

2259-2266

Informations de copyright

Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

Auteurs

Aparna Hiren Patil Kose (AH)

GNIRD, Guru Nanak Khalsa College of Arts, Science and Commerce (AUTONOMOUS), Mumbai, 400019, India.

Kiran Mangaonkar (K)

GNIRD, Guru Nanak Khalsa College of Arts, Science and Commerce (AUTONOMOUS), Mumbai, 400019, India.

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Classifications MeSH