Predicting Working Memory in Healthy Older Adults Using Real-Life Language and Social Context Information:A Machine Learning Approach
AffiliationDepartment of Psychology, University of Arizona
Electronically Activated Recorder (EAR)
natural language processing
MetadataShow full item record
PublisherJMIR Publications Inc.
CitationFerrario, A., Luo, M., Polsinelli, A. J., Moseley, S. A., Mehl, M. R., Yordanova, K., Martin, M., & Demiray, B. (2022). Predicting Working Memory in Healthy Older Adults Using Real-Life Language and Social Context Information:A Machine Learning Approach. JMIR Aging.
RightsCopyright © Andrea Ferrario, Minxia Luo, Angelina J Polsinelli, Suzanne A Moseley, Matthias R Mehl, Kristina Yordanova, Mike Martin, Burcu Demiray. Originally published in JMIR Aging (https://aging.jmir.org), 08.03.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractBackground: Language use and social interactions have demonstrated a close relationship with cognitive measures. It is important to improve the understanding of language use and behavioral indicators from social context to study the early prediction of cognitive decline among healthy populations of older adults. Objective: This study aimed at predicting an important cognitive ability, working memory, of 98 healthy older adults participating in a 4-day-long naturalistic observation study. We used linguistic measures, part-of-speech (POS) tags, and social context information extracted from 7450 real-life audio recordings of their everyday conversations. Methods: The methods in this study comprise (1) the generation of linguistic measures, representing idea density, vocabulary richness, and grammatical complexity, as well as POS tags with natural language processing (NLP) from the transcripts of real-life conversations and (2) the training of machine learning models to predict working memory using linguistic measures, POS tags, and social context information. We measured working memory using (1) the Keep Track test, (2) the Consonant Updating test, and (3) a composite score based on the Keep Track and Consonant Updating tests. We trained machine learning models using random forest, extreme gradient boosting, and light gradient boosting machine algorithms, implementing repeated cross-validation with different numbers of folds and repeats and recursive feature elimination to avoid overfitting. Results: For all three prediction routines, models comprising linguistic measures, POS tags, and social context information improved the baseline performance on the validation folds. The best model for the Keep Track prediction routine comprised linguistic measures, POS tags, and social context variables. The best models for prediction of the Consonant Updating score and the composite working memory score comprised POS tags only. Conclusions: The results suggest that machine learning and NLP may support the prediction of working memory using, in particular, linguistic measures and social context information extracted from the everyday conversations of healthy older adults. Our findings may support the design of an early warning system to be used in longitudinal studies that collects cognitive ability scores and records real-life conversations unobtrusively. This system may support the timely detection of early cognitive decline. In particular, the use of a privacy-sensitive passive monitoring technology would allow for the design of a program of interventions to enable strategies and treatments to decrease or avoid early cognitive decline. © 2022 JMIR Publications Inc.. All right reserved.
NoteOpen access journal
VersionFinal published version
Except where otherwise noted, this item's license is described as Copyright © Andrea Ferrario, Minxia Luo, Angelina J Polsinelli, Suzanne A Moseley, Matthias R Mehl, Kristina Yordanova, Mike Martin, Burcu Demiray. Originally published in JMIR Aging (https://aging.jmir.org), 08.03.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).