Poster: Rapid Deployment of a Machine Learning-based Derived Biomarker using Publicly Available Data Sources for Covariate Adjusted Descriptive Modeling

Presented at the 2019 ASA Symposium on Data Science and Statistics in Bellevue, Washington on May 31, 2019.   Background: Defining baseline characteristics for covariate-adjusted analyses to increase study power is not new. Multifactorial heterogeneous diseases...

Poster: Increasing ALS Clinical Trial Efficiency using Machine Learning Models

Presented at the 2019 MDA Clinical and Scientific Conference in Orlando, Florida on April 15, 2019.  Re-presented at the 71st Annual Meeting of the American Academy of Neurology in Philadelphia, Pennsylvania on May 7, 2019, and at the ENCALS Meeting 2019 in Tours,...

Poster: Validation of a Suite of Machine Learning Models using the Longitudinal VITALITY-ALS Data Set

Presented at the 29th International Symposium on ALS/MND in Glasgow, Scotland on December 8, 2018. Background: We have previously reported on the development of models for predicting ALSFRS-R, vital capacity and survival. Here we report on the development of...

Pilot trial of inosine to elevate urate levels in amyotrophic lateral sclerosis

Katharine Nicholson, James Chan, Eric A. Macklin, Mark Levine‐Weinberg, Christopher Breen, Rachit Bakshi, Daniela L. Grasso, Anne‐Marie Wills, Samad Jahandideh, Albert A. Taylor, Danielle Beaulieu, David L. Ennist, Ovidiu Andronesi, Eva‐Maria Ratai, Michael A....

Poster: Machine Learning Applications for Increasing the Efficiency of ALS Clinical Trials

Presented at the 17th Annual NEALS Meeting in Clearwater, Florida, October 3, 2018. Objective: We demonstrate several applications to improve the efficiency of ALS drug development clinical trials, including a novel method of subgroup analysis, “detectable effect...

Poster: Increasing Study Power using a Machine Learning Approach

Presented July 30, 2018 at the Joint Statistical Meeting (JSM) of the American Statistical Association (ASA) in Vancouver, British Columbia, Canada. Objective: We hypothesize that using predicted outcomes from our ML models as covariates in the analysis of a clinical...