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Prediction of Loneliness in Older Adults Using Natural Language Processing: Exploring Sex Differences in Speech

  • Varsha D. Badal
    Affiliations
    Department of Psychiatry (VDB, SAG, CAD, DVJ, EEL), University of California San Diego, San Diego, CA

    Sam and Rose Stein Institute for Research on Aging (VDB, SAG, CAD, DVJ, EEL), University of California San Diego, San Diego, CA
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  • Sarah A. Graham
    Affiliations
    Department of Psychiatry (VDB, SAG, CAD, DVJ, EEL), University of California San Diego, San Diego, CA

    Sam and Rose Stein Institute for Research on Aging (VDB, SAG, CAD, DVJ, EEL), University of California San Diego, San Diego, CA
    Search for articles by this author
  • Colin A. Depp
    Affiliations
    Department of Psychiatry (VDB, SAG, CAD, DVJ, EEL), University of California San Diego, San Diego, CA

    Sam and Rose Stein Institute for Research on Aging (VDB, SAG, CAD, DVJ, EEL), University of California San Diego, San Diego, CA

    VA San Diego Healthcare System (CAD, EEL), La Jolla, CA
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  • Kaoru Shinkawa
    Affiliations
    Accessibility and Aging, IBM Research-Tokyo (KS, YY), Tokyo, Japan
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  • Yasunori Yamada
    Affiliations
    Accessibility and Aging, IBM Research-Tokyo (KS, YY), Tokyo, Japan
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  • Lawrence A. Palinkas
    Affiliations
    Suzanne Dworak Peck School of Social Work (LAP), University of Southern California, Los Angeles, CA
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  • Ho-Cheol Kim
    Affiliations
    AI and Cognitive Software, IBM Research-Almaden (HCK), San Jose, CA
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  • Dilip V. Jeste
    Affiliations
    Department of Psychiatry (VDB, SAG, CAD, DVJ, EEL), University of California San Diego, San Diego, CA

    Sam and Rose Stein Institute for Research on Aging (VDB, SAG, CAD, DVJ, EEL), University of California San Diego, San Diego, CA

    Department of Neurosciences (DVJ), University of California San Diego, La Jolla, CA
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  • Ellen E. Lee
    Correspondence
    Send correspondence and reprint requests to Ellen E. Lee, M.D., Department of Psychiatry, University of California San Diego, 9500 Gilman Dr. #0664, La Jolla, CA 92023-0664.
    Affiliations
    Department of Psychiatry (VDB, SAG, CAD, DVJ, EEL), University of California San Diego, San Diego, CA

    Sam and Rose Stein Institute for Research on Aging (VDB, SAG, CAD, DVJ, EEL), University of California San Diego, San Diego, CA

    VA San Diego Healthcare System (CAD, EEL), La Jolla, CA
    Search for articles by this author
Published:September 11, 2020DOI:https://doi.org/10.1016/j.jagp.2020.09.009

      Highlights

      • What is the primary question addressed by this study? This paper explores the use of natural language processing techniques and machine learning models to predict loneliness in older community-dwelling adults.
      • What is the main finding of this study? There are structural differences in how older men and women talk about loneliness that can be detected using natural language processing techniques. Text features can be used to predict loneliness with reasonable validity.
      • What is the meaning of the finding? NLP and machine learning approaches provide a novel way to analyze text data to identify loneliness, while accounting for key sociodemographic factors like sex and age.

      Abstract

      Objective

      The growing pandemic of loneliness has great relevance to aging populations, though assessments are limited by self-report approaches. This paper explores the use of artificial intelligence (AI) technology to evaluate interviews on loneliness, notably, employing natural language processing (NLP) to quantify sentiment and features that indicate loneliness in transcribed speech text of older adults.

      Design

      Participants completed semi-structured qualitative interviews regarding the experience of loneliness and a quantitative self-report scale (University of California Los Angeles or UCLA Loneliness scale) to assess loneliness. Lonely and non-lonely participants (based on qualitative and quantitative assessments) were compared.

      Setting

      Independent living sector of a senior housing community in San Diego County.

      Participants

      Eighty English-speaking older adults with age range 66–94 (mean 83 years).

      Measurements

      Interviews were audiotaped and manually transcribed. Transcripts were examined using NLP approaches to quantify sentiment and expressed emotions.

      Results

      Lonely individuals (by qualitative assessments) had longer responses with greater expression of sadness to direct questions about loneliness. Women were more likely to endorse feeling lonely during the qualitative interview. Men used more fearful and joyful words in their responses. Using linguistic features, machine learning models could predict qualitative loneliness with 94% precision (sensitivity = 0.90, specificity = 1.00) and quantitative loneliness with 76% precision (sensitivity = 0.57, specificity = 0.89).

      Conclusions

      AI (e.g., NLP and machine learning approaches) can provide unique insights into how linguistic features of transcribed speech data may reflect loneliness. Eventually linguistic features could be used to assess loneliness of individuals, despite limitations of commercially developed natural language understanding programs.

      Key Words

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