Big Data Analytical Approaches to the NACC Dataset: Aiding Preclinical Trial Enrichment.

ABSTRACT: BACKGROUND:Clinical trials increasingly aim to retard disease progression during presymptomatic phases of Mild Cognitive Impairment (MCI) and thus recruiting study participants at high risk for developing MCI is critical for cost-effective prevention trials. However, accurately identifying those who are destined to develop MCI is difficult. Collecting biomarkers is often expensive. METHODS:We used only noninvasive clinical variables collected in the National Alzheimer's Coordinating Center (NACC) Uniform Data Sets version 2.0 and applied machine learning techniques to build a low-cost and accurate Mild Cognitive Impairment (MCI) conversion prediction calculator. Cross-validation and bootstrap were used to select as few variables as possible accurately predicting MCI conversion within 4 yea

SUBMITTER: Lin M 

PROVIDER: S-EPMC5854492 | biostudies-literature | 2018 Jan-Mar

REPOSITORIES: biostudies-literature