Presented at the 28th International Symposium on ALS/MND in Boston, Massachusetts, on December 9, 2017.

Background: This is the first step of a research study that aims to develop a machine-learning-based platform against which potential biomarkers of ALS disease can be tested as surrogate markers of disease progression. Recently developed baseline and longitudinal forced vital capacity (FVC) models are used as base models for the assessment of potential prognostic ALS biomarkers. These methods have been successfully applied to the evaluation of imaging markers in Parkinson’s disease.

Objectives: We hypothesize that machine-learning-based FVC models can be used as tools to assess the relative importances of potential biomarkers as predictors of ALS disease progression.


  • Baseline and longitudinal models for the prediction of FVC in ALS patient were built as base models for evaluating potential new biomarkers with possible diagnostic and/or prognostic value.
  • Evaluation of models supports the hypothesis that these models can be used to assess the diagnostic and prognostic potential of ALS biomarkers.


Authors: Samad Jahandideh, David L. Ennist


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