title: Predicting disease progression in amyotrophic lateral sclerosis.
abstract: It is essential to develop predictive algorithms for Amyotrophic Lateral Sclerosis (ALS) disease progression to allow for efficient clinical trials and patient care. The best existing predictive models rely on several months of baseline data and have only been validated in clinical trial research datasets. We asked whether a model developed using clinical research patient data could be applied to the broader ALS population typically seen at a tertiary care ALS clinic.
  
  
  
  Based on the PRO-ACT ALS database, we developed random forest (RF), pre-slope, and generalized linear (GLM) models to test whether accurate, unbiased models could be created using only baseline data. Secondly, we tested whether a model could be validated with a clinical patient dataset to demonstrate broader applicability.
  
  
  
  We found that a random forest model using only baseline data could accurately predict disease progression for a clinical trial research dataset as well as a population of patients being treated at a tertiary care clinic. The RF Model outperformed a pre-slope model and was similar to a GLM model in terms of root mean square deviation at early time points. At later time points, the RF Model was far superior to either model. Finally, we found that only the RF Model was unbiased and was less subject to overfitting than either of the other two models when applied to a clinic population.
  
  
  
  We conclude that the RF Model delivers superior predictions of ALS disease progression.
authors.affiliation: Origent Data Sciences, Inc. Vienna Virginia.
authors.forename: Albert A
authors.initials: AA
authors.lastname: Taylor
authors.affiliation: Department of Neurology Emory University School of Medicine Atlanta Georgia.
authors.forename: Christina
authors.initials: C
authors.lastname: Fournier
authors.affiliation: Department of Neurology Emory University School of Medicine Atlanta Georgia.
authors.forename: Meraida
authors.initials: M
authors.lastname: Polak
authors.affiliation: Sentrana, Inc. Washington District of Columbia.
authors.forename: Liuxia
authors.initials: L
authors.lastname: Wang
authors.affiliation: Prize4Life Haifa Israel; Present address: Teva Pharmaceutical Industries Ltd Petah Tikva Israel.
authors.forename: Neta
authors.initials: N
authors.lastname: Zach
authors.affiliation: Origent Data Sciences, Inc. Vienna Virginia.
authors.forename: Mike
authors.initials: M
authors.lastname: Keymer
authors.affiliation: Department of Neurology and Department of Pathology & Laboratory Medicine Emory University School of Medicine Atlanta Atlanta Georgia.
authors.forename: Jonathan D
authors.initials: JD
authors.lastname: Glass
authors.affiliation: Origent Data Sciences, Inc. Vienna Virginia.
authors.forename: David L
authors.initials: DL
authors.lastname: Ennist