Identifying patterns in amyotrophic lateral sclerosis progression from sparse longitudinal data
Name
s43588-022-00299-w.pdf
Description
Published version
Size
13.52 MB
Format
Adobe PDF
Checksum (MD5)
8cde591a98705932850f97c0b67d9a9a
Author(s)
Fraenkel, Ernest
Date Issued
2022
Journal
Nature Computational Science
Publisher
Springer Science and Business Media LLC
Citation
Fraenkel, Ernest. 2022. "Identifying patterns in amyotrophic lateral sclerosis progression from sparse longitudinal data." Nature Computational Science, 2 (9).
Version
Final published version
Abstract
AbstractThe clinical presentation of amyotrophic lateral sclerosis (ALS), a fatal neurodegenerative disease, varies widely across patients, making it challenging to determine if potential therapeutics slow progression. We sought to determine whether there were common patterns of disease progression that could aid in the design and analysis of clinical trials. We developed an approach based on a mixture of Gaussian processes to identify clusters of patients sharing similar disease progression patterns, modeling their average trajectories and the variability in each cluster. We show that ALS progression is frequently nonlinear, with periods of stable disease preceded or followed by rapid decline. We also show that our approach can be extended to Alzheimer’s and Parkinson’s diseases. Our results advance the characterization of disease progression of ALS and provide a flexible modeling approach that can be applied to other progressive diseases.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/S43588-022-00299-W
