Poster: Machine Learning Models for the Assessment of Potential ALS Biomarkers

Presented at the 2018 Meeting of the European Network to Cure ALS (ENCALS), on June 21, 2018. Background: We previously developed regression models for total ALSFRS-R score, ALSFRS-R subscores, vital capacity (VC), and percent expected VC, and time-to-event models for...

Improved Stratification of ALS Clinical Trials Using Predicted Survival

James D. Berry, Albert A. Taylor, Danielle Beaulieu, Lisa Meng, Amy Bian, Jinsy Andrews, Mike Keymer, David L. Ennist, Bernard Ravina. Published online in Annals of Clinical and Translational Neurology, the official journal of the American Neurological...

Longitudinal Modeling to Predict Vital Capacity in Amyotrophic Lateral Sclerosis

Samad Jahandideh, Albert A. Taylor, Danielle Beaulieu, Mike Keymer, Lisa Meng, Amy Bian, Nazem Atassi, Jinsy Andrews & David L. Ennist Published in Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, on December 20, 2017.  This article is accessible...

Poster: Machine Learning Models for the Assessment of Potential ALS Biomarkers

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...

Poster: Validation of Predictive ALS Machine Learning Models with a Contemporary, External Dataset and Application to Trial Simulations

Presented at the 16th Annual NEALS Meeting in Clearwater, Florida, October 4, 2017, and also at the 28th International Symposium on ALS/MND in Boston, Massachusetts, on December 9, 2017. Background: Disease heterogeneity is widely believed to be a confounding factor...

Poster 162: Machine Learning Models for the Clinical Development of Gene and Cell Therapies

Presented at the 20th ASGCT Annual Meeting in Washington, DC, May 10-13, 2017. Objectives: We hypothesized that computer models incorporating both survival and disease progression predictions could serve as tools to develop virtual controls and to stratify patients...