Advancing Rheumatoid Arthritis Care: Exploring Technological Breakthroughs and Future Directions
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-2031-6_5
Reference14 articles.
1. Radu, A.-F., Bungau, S.G.: Nanomedical approaches in the realm of rheumatoid arthritis. Age. Res. Rev 101927 (2023)
2. Stoel, B.C.: Artificial intelligence in detecting early RA. Semin. Arthritis Rheum. 49(3), S25–S28 (2019). https://doi.org/10.1016/j.semarthrit.2019.09.020
3. Kose, P., Aparna, H., Mangaonkar, K.: Application of machine learning in rheumatoid arthritis diseases research: review and future directions. Combinatorial Chem. High Throughput Screen. 26(13), 2259–2266 (2023)
4. Du, Y., Almajalid, R., Shan, J., Zhang, M.: A novel method to predict knee osteoarthritis progression on MRI using machine learning methods. IEEE Trans. Nanobiosci. 17(3), 228–236 (2018). https://doi.org/10.1109/TNB.2018.2840082
5. Koo, B.S., et al.: Machine learning model for identifying important clinical features for predicting remission in patients with rheumatoid arthritis treated with biologics. Arthritis. Res. Ther. 23(1) (2021). https://doi.org/10.1186/s13075-021-02567-y
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