Accounting for uncertainty in training data to improve machine learning performance in predicting new disease activity in early multiple sclerosis.

MLA

Tayyab, Maryam, et al. “Accounting for Uncertainty in Training Data to Improve Machine Learning Performance in Predicting New Disease Activity in Early Multiple Sclerosis.” Frontiers in Neurology, June 2023, pp. 1–8. EBSCOhost, https://doi.org/10.3389/fneur.2023.1165267.

APA

Tayyab, M., Metz, L. M., Li, D. K. B., Kolind, S., Carruthers, R., Traboulsee, A., & Tam, R. C. (2023). Accounting for uncertainty in training data to improve machine learning performance in predicting new disease activity in early multiple sclerosis. Frontiers in Neurology, 1–8. https://doi.org/10.3389/fneur.2023.1165267

Chicago

Tayyab, Maryam, Luanne M. Metz, David K. B. Li, Shannon Kolind, Robert Carruthers, Anthony Traboulsee, and Roger C. Tam. 2023. “Accounting for Uncertainty in Training Data to Improve Machine Learning Performance in Predicting New Disease Activity in Early Multiple Sclerosis.” Frontiers in Neurology, June, 1–8. doi:10.3389/fneur.2023.1165267.