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Machine learning approach finds nonlinear patterns of neurodegenerative disease progression

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.

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Fig. 1: Nonlinear ALS progression patterns.


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This is a summary of: Ramamoorthy, D. et al. Identifying patterns in amyotrophic lateral sclerosis progression from sparse longitudinal data. Nat. Comput. Sci. (2022).

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Machine learning approach finds nonlinear patterns of neurodegenerative disease progression. Nat Comput Sci 2, 565–566 (2022).

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