Latent information in fluency lists predicts functional decline in persons at risk for Alzheimer disease.

ABSTRACT:

Objective

We constructed random forest classifiers employing either the traditional method of scoring semantic fluency word lists or new methods. These classifiers were then compared in terms of their ability to diagnose Alzheimer disease (AD) or to prognosticate among individuals along the continuum from cognitively normal (CN) through mild cognitive impairment (MCI) to AD.

Method

Semantic fluency lists from 44 cognitively normal elderly individuals, 80 MCI patients, and 41 AD patients were transcribed into electronic text files and scored by four methods: traditional raw scores, clustering and switching scores, "generalized" versions of clustering and switching, and a method based on independent components analysis (ICA). Random forest classifiers based on raw scores were

SUBMITTER: Clark DG 

PROVIDER: S-EPMC4039569 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature