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Development and validation of a 1-year survival prognosis estimation model for Amyotrophic Lateral Sclerosis using manifold learning algorithm UMAP

Abstract : Amyotrophic Lateral Sclerosis (ALS) is an inexorably progressive neurodegenerative condition with no effective disease modifying therapies. The development and validation of reliable prognostic models is a recognised research priority. We present a prognostic model for survival in ALS where result uncertainty is taken into account. Patient data were reduced and projected onto a 2D space using Uniform Manifold Approximation and Projection (UMAP), a novel non-linear dimension reduction technique. Information from 5,220 patients was included as development data originating from past clinical trials, and real-world population data as validation data. Predictors included age, gender, region of onset, symptom duration, weight at baseline, functional impairment, and estimated rate of functional loss. UMAP projection of patients shows an informative 2D data distribution. As limited data availability precluded complex model designs, the projection was divided into three zones with relevant survival rates. These rates were defined using confidence bounds: high, intermediate, and low 1-year survival rates at respectively 90% (±4%), 80% (±4%) and 58% (±4%). Predicted 1-year survival was estimated using zone membership. This approach requires a limited set of features, is easil y updated, improves with additional patient data, and accounts for results uncertainty.
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https://hal.archives-ouvertes.fr/hal-03401712
Contributor : Jean-François Pradat-Peyre Connect in order to contact the contributor
Submitted on : Monday, October 25, 2021 - 2:22:03 PM
Last modification on : Tuesday, November 16, 2021 - 4:53:04 AM

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Vincent Grollemund, Gaétan Le Chat, Marie-Sonia Secchi-Buhour, François Delbot, Jean-François Pradat-Peyre, et al.. Development and validation of a 1-year survival prognosis estimation model for Amyotrophic Lateral Sclerosis using manifold learning algorithm UMAP. Scientific Reports, Nature Publishing Group, 2020, 10 (1), pp.13378. ⟨10.1038/s41598-020-70125-8⟩. ⟨hal-03401712⟩

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