Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.
Advertisement
Nature Computational Science (2022)
2
Metrics details
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.
Your institute does not have access to this article
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
$29.99
monthly
Subscribe to Journal
Get full journal access for 1 year
$99.00
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Buy article
Get time limited or full article access on ReadCube.
$32.00
All prices are NET prices.
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.
Article Google Scholar
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.
Article Google Scholar
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.
Download references
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).
Reprints and Permissions
Machine learning approach finds nonlinear patterns of neurodegenerative disease progression. Nat Comput Sci (2022). https://doi.org/10.1038/s43588-022-00300-6
Download citation
Published: 08 September 2022
DOI: https://doi.org/10.1038/s43588-022-00300-6
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
Advertisement
Advanced search
© 2022 Springer Nature Limited
Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.