Cognitive biomarker prioritization in Alzheimer's Disease using brain morphometric data.

ABSTRACT:

Background

Cognitive assessments represent the most common clinical routine for the diagnosis of Alzheimer's Disease (AD). Given a large number of cognitive assessment tools and time-limited office visits, it is important to determine a proper set of cognitive tests for different subjects. Most current studies create guidelines of cognitive test selection for a targeted population, but they are not customized for each individual subject. In this manuscript, we develop a machine learning paradigm enabling personalized cognitive assessments prioritization.

Method

We adapt a newly developed learning-to-rank approach [Formula: see text] to implement our paradigm. This method learns the latent scoring function that pushes the most effective cognitive assessments onto the top of t

SUBMITTER: Peng B 

PROVIDER: S-EPMC7709267 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature