We developed a machine learning method that consistently and accurately identified dominant patterns of disease progression in amyotrophic later sclerosis (ALS), Alzheimer’s disease and Parkinson’s disease. Of note, the model was able to identify nonlinear progression trajectories in ALS, a finding that has clinical implications for patient stratification and clinical trial design.
This is a preview of subscription content, log in via an institution to check access.
References
Cedarbam, J. M. The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function. J. Neurol. Sci. 169, 13–21 (1999). An article that presents the ALSFRS-R, a common measure of disease progression in ALS.
Fournier, C. N. et al. development and validation of the Rasch-Built Overall Amyotrophic Lateral Sclerosis Disability Scale (ROADS). JAMA Neurol. 77, 480–488 (2020). An article that presents ROADS, an updated clinical measure of ALS progression.
Kiernan, M. C. et al. Improving clinical trial outcomes in amyotrophic lateral sclerosis. Nat. Rev. Neurol. 17, 104–118 (2020). A Review that discusses the evolution of clinical trial designs combined with improved methods for patient stratification.
Rasmussen, C. E. & Ghahramani, Z. Infinite mixtures of Gaussian process experts. In Advances in Neural Information Processing Systems 14 (eds Dietterich, T., Becker, S. & Ghahramani, Z.) 881–888 (MIT Press, 2001). An article that presents an early version of a mixture of Gaussian processes model.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This is a summary of: Ramamoorthy, D. et al. Identifying patterns in amyotrophic lateral sclerosis progression from sparse longitudinal data. Nat. Comput. Sci. https://doi.org/10.1038/s43588-022-00299-w (2022).
Rights and permissions
About this article
Cite this article
Machine learning approach finds nonlinear patterns of neurodegenerative disease progression. Nat Comput Sci 2, 565–566 (2022). https://doi.org/10.1038/s43588-022-00300-6
Published:
Issue Date:
DOI: https://doi.org/10.1038/s43588-022-00300-6
- Springer Nature America, Inc.