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

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 View