title: 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.
  
  
  METHODS
  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 compared to "augmented" classifiers that incorporated newer scoring methods. Outcome variables included AD diagnosis at baseline, MCI conversion, increase in Clinical Dementia Rating-Sum of Boxes (CDR-SOB) score, or decrease in Financial Capacity Instrument (FCI) score. Receiver operating characteristic (ROC) curves were constructed for each classifier and the area under the curve (AUC) was calculated. We compared AUC between raw and augmented classifiers using Delong's test and assessed validity and reliability of the augmented classifier.
  
  
  RESULTS
  Augmented classifiers outperformed classifiers based on raw scores for the outcome measures AD diagnosis (AUC .97 vs. .95), MCI conversion (AUC .91 vs. .77), CDR-SOB increase (AUC .90 vs. .79), and FCI decrease (AUC .89 vs. .72). Measures of validity and stability over time support the use of the method.
  
  
  CONCLUSIONS
  Latent information in semantic fluency word lists is useful for predicting cognitive and functional decline among elderly individuals at increased risk for developing AD. Modern machine learning methods may incorporate latent information to enhance the diagnostic value of semantic fluency raw scores. These methods could yield information valuable for patient care and clinical trial design with a relatively small investment of time and money.
authors.affiliation: Birmingham VA Medical Center, USA; Department of Neurology, University of Alabama at Birmingham, USA. Electronic address: dgclark@uab.edu.
authors.forename: D G
authors.initials: DG
authors.lastname: Clark
authors.affiliation: Department of Biomedical Sciences, Georgia Health Science University, USA.
authors.forename: P
authors.initials: P
authors.lastname: Kapur
authors.affiliation: Department of Neurology, University of Alabama at Birmingham, USA.
authors.forename: D S
authors.initials: DS
authors.lastname: Geldmacher
authors.affiliation: Department of Neurology, University of Alabama at Birmingham, USA.
authors.forename: J C
authors.initials: JC
authors.lastname: Brockington
authors.affiliation: Department of Neurology, University of Alabama at Birmingham, USA.
authors.forename: L
authors.initials: L
authors.lastname: Harrell
authors.affiliation: Department of Psychology and Behavioral Neuroscience, University of Alabama at Birmingham, USA.
authors.forename: T P
authors.initials: TP
authors.lastname: DeRamus
authors.affiliation: Birmingham VA Medical Center, USA.
authors.forename: P D
authors.initials: PD
authors.lastname: Blanton
authors.affiliation: Birmingham VA Medical Center, USA.
authors.forename: K
authors.initials: K
authors.lastname: Lokken
authors.affiliation: Birmingham VA Medical Center, USA; Department of Neurology, University of Alabama at Birmingham, USA.
authors.forename: A P
authors.initials: AP
authors.lastname: Nicholas
authors.affiliation: Department of Neurology, University of Alabama at Birmingham, USA.
authors.forename: D C
authors.initials: DC
authors.lastname: Marson
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000150:complications / Q000175:diagnosis* / Q000503:physiopathology; D019540:Area Under Curve; D001185:Artificial Intelligence; D016022:Case-Control Studies; D060825:Cognitive Dysfunction / Q000150:complications / Q000175:diagnosis* / Q000503:physiopathology; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D012372:ROC Curve; D015203:Reproducibility of Results; D012306:Risk; D012660:Semantics; D013060:Speech / Q000502:physiology*; D013064:Speech Disorders / Q000175:diagnosis* / Q000209:etiology / Q000503:physiopathology
keywords: Alzheimer's disease; Cognitive neuropsychology; Dementia; MCI (mild cognitive impairment); Machine learning; Random forests