Optimizing Neuropsychological Assessments for Cognitive, Behavioral, and Functional Impairment Classification: A Machine Learning Study
Joint Authors
Salvatore, Christian
Castiglioni, Isabella
Battista, Petronilla
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-19, 19 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-01-31
Country of Publication
Egypt
No. of Pages
19
Main Subjects
Abstract EN
Subjects with Alzheimer’s disease (AD) show loss of cognitive functions and change in behavioral and functional state affecting the quality of their daily life and that of their families and caregivers.
A neuropsychological assessment plays a crucial role in detecting such changes from normal conditions.
However, despite the existence of clinical measures that are used to classify and diagnose AD, a large amount of subjectivity continues to exist.
Our aim was to assess the potential of machine learning in quantifying this process and optimizing or even reducing the amount of neuropsychological tests used to classify AD patients, also at an early stage of impairment.
We investigated the role of twelve state-of-the-art neuropsychological tests in the automatic classification of subjects with none, mild, or severe impairment as measured by the clinical dementia rating (CDR).
Data were obtained from the ADNI database.
In the groups of measures used as features, we included measures of both cognitive domains and subdomains.
Our findings show that some tests are more frequently best predictors for the automatic classification, namely, LM, ADAS-Cog, AVLT, and FAQ, with a major role of the ADAS-Cog measures of delayed and immediate memory and the FAQ measure of financial competency.
American Psychological Association (APA)
Battista, Petronilla& Salvatore, Christian& Castiglioni, Isabella. 2017. Optimizing Neuropsychological Assessments for Cognitive, Behavioral, and Functional Impairment Classification: A Machine Learning Study. Behavioural Neurology،Vol. 2017, no. 2017, pp.1-19.
https://search.emarefa.net/detail/BIM-1139756
Modern Language Association (MLA)
Battista, Petronilla…[et al.]. Optimizing Neuropsychological Assessments for Cognitive, Behavioral, and Functional Impairment Classification: A Machine Learning Study. Behavioural Neurology No. 2017 (2017), pp.1-19.
https://search.emarefa.net/detail/BIM-1139756
American Medical Association (AMA)
Battista, Petronilla& Salvatore, Christian& Castiglioni, Isabella. Optimizing Neuropsychological Assessments for Cognitive, Behavioral, and Functional Impairment Classification: A Machine Learning Study. Behavioural Neurology. 2017. Vol. 2017, no. 2017, pp.1-19.
https://search.emarefa.net/detail/BIM-1139756
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139756