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Prediction of Loneliness in Older Adults Using Natural Language Processing: Exploring Sex Differences in Speech.
Badal, Varsha D; Graham, Sarah A; Depp, Colin A; Shinkawa, Kaoru; Yamada, Yasunori; Palinkas, Lawrence A; Kim, Ho-Cheol; Jeste, Dilip V; Lee, Ellen E.
Afiliación
  • Badal VD; 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.
  • Graham SA; 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.
  • Depp CA; 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.
  • Shinkawa K; Accessibility and Aging, IBM Research-Tokyo (KS, YY), Tokyo, Japan.
  • Yamada Y; Accessibility and Aging, IBM Research-Tokyo (KS, YY), Tokyo, Japan.
  • Palinkas LA; Suzanne Dworak Peck School of Social Work (LAP), University of Southern California, Los Angeles, CA.
  • Kim HC; AI and Cognitive Software, IBM Research-Almaden (HCK), San Jose, CA.
  • Jeste DV; 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 Dieg
  • Lee EE; 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. Electronic
Am J Geriatr Psychiatry ; 29(8): 853-866, 2021 08.
Article en En | MEDLINE | ID: mdl-33039266
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.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Habla / Soledad Tipo de estudio: Prognostic_studies / Qualitative_research / Risk_factors_studies Límite: Aged / Aged80 / Female / Humans / Male Idioma: En Revista: Am J Geriatr Psychiatry Asunto de la revista: GERIATRIA / PSIQUIATRIA Año: 2021 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Habla / Soledad Tipo de estudio: Prognostic_studies / Qualitative_research / Risk_factors_studies Límite: Aged / Aged80 / Female / Humans / Male Idioma: En Revista: Am J Geriatr Psychiatry Asunto de la revista: GERIATRIA / PSIQUIATRIA Año: 2021 Tipo del documento: Article