This paper is in the following e-collection/theme issue:

Cognitive Training for the Elderly (95) Natural Language Processing (692) Dementia and Cognitive Decline (481) Machine Learning (1465) Diagnostic Tools in Mental Health (242) Aging in Place (283) Big Data Analytics for Elder Care (22) Memory (43)

Published on in Vol 5, No 1 (2022): Jan-Mar

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28333, first published .
Predicting Working Memory in Healthy Older Adults Using Real-Life Language and Social Context Information: A Machine Learning Approach

Predicting Working Memory in Healthy Older Adults Using Real-Life Language and Social Context Information: A Machine Learning Approach

Predicting Working Memory in Healthy Older Adults Using Real-Life Language and Social Context Information: A Machine Learning Approach

Authors of this article:

Andrea Ferrario1, 2 Author Orcid Image ;   Minxia Luo3, 4 Author Orcid Image ;   Angelina J Polsinelli5 Author Orcid Image ;   Suzanne A Moseley6 Author Orcid Image ;   Matthias R Mehl7 Author Orcid Image ;   Kristina Yordanova8 Author Orcid Image ;   Mike Martin3, 4 Author Orcid Image ;   Burcu Demiray3, 4 Author Orcid Image
Article Authors Cited by (7) Tweetations (11) Metrics

Journals

  1. Röcke C, Luo M, Bereuter P, Katana M, Fillekes M, Gehriger V, Sofios A, Martin M, Weibel R. Charting everyday activities in later life: Study protocol of the mobility, activity, and social interactions study (MOASIS). Frontiers in Psychology 2023;13 View
  2. Ferrario A, Loi M. The Robustness of Counterfactual Explanations Over Time. IEEE Access 2022;10:82736 View
  3. Zolnoori M, Vergez S, Sridharan S, Zolnour A, Bowles K, Kostic Z, Topaz M. Is the patient speaking or the nurse? Automatic speaker type identification in patient–nurse audio recordings. Journal of the American Medical Informatics Association 2023;30(10):1673 View
  4. Ferrario A, Demiray B. Understanding reminiscence and its negative functions in the everyday conversations of young adults: A machine learning approach. Heliyon 2024;10(1):e23825 View
  5. Badal V, Reinen J, Twamley E, Lee E, Fellows R, Bilal E, Depp C. Investigating Acoustic and Psycholinguistic Predictors of Cognitive Impairment in Older Adults: Modeling Study. JMIR Aging 2024;7:e54655 View
  6. Song Y, Sun Y, Weng Q, Yi L. Using machine learning model for predicting risk of memory decline: A cross sectional study. Heliyon 2024;10(20):e39575 View
  7. Rao A, Mujib M, Qazi S, Alokaily A, Ikhlaq A, Mirza E, Aldohbeyb A, Hasan M. Predicting the effectiveness of binaural beats on working memory. NeuroReport 2024;35(17):1082 View