Artificial Intelligence Helps Predict Loneliness

In older adults, the use of artificial intelligence and natural language patterns (NLP) help discern degrees of loneliness, revealed a new proof-of-concept paper, published in the American Journal of Geriatric Psychiatry, carried out by a //team led by researchers at University of California San Diego School of Medicine. “Most studies use either a direct question of ‘ how often do you feel lonely,’ which can lead to biased responses due to stigma associated with loneliness or the UCLA Loneliness Scale which does not explicitly use the word ‘lonely,’” said senior author Ellen Lee, MD, assistant professor of psychiatry at UC San Diego School of Medicine. “For this project, we used natural language processing or NLP, an unbiased quantitative assessment of expressed emotion and sentiment, in concert with the usual loneliness measurement tools.”

Numerous studies have documented the rise of loneliness among older adults. For example, a study conducted by UC San Diego researchers revealed that 85 percent of residents living in an independent senior housing community reported moderate to severe loneliness levels.

The new study focused on 80 participants aged 66 to 94. The participants were interviewed by trained study staff in more unstructured conversations analyzed using NLP-understanding software developed by IBM, plus other machine-learning tools.

“NLP and machine learning allow us to systematically examine long interviews from many individuals and explore how subtle speech features like emotions may indicate loneliness. Similar emotion analyses by humans would be open to bias, lack consistency, and require extensive training to standardize,” said first author Varsha Badal, PhD, a postdoctoral research fellow.

Among the findings:

• Lonely older adults had more extended responses in the qualitative interview and greatly expressed sadness to direct questions about loneliness.
• Women were more likely than men to acknowledge feeling lonely during interviews.
• Men used more fearful and joyful words in their responses compared to women.

The study demonstrated the feasibility of using NLP analyses of transcribed speech to better parse and understand complex emotions like loneliness. Machine-learning models predicted qualitative loneliness with 94 percent accuracy.

“Our IBM-UC San Diego Center is now exploring NLP signatures of loneliness and wisdom, which are inversely linked in older adults. Speech data can be combined with our other assessments of cognition, mobility, sleep, physical activity and mental health to improve our understanding of aging and to help promote successful aging” said study co-author Dilip Jeste, MD, senior associate dean for healthy aging and senior care and co-director of the IBM-UC San Diego Center for Artificial Intelligence for Healthy Living.

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Source-Medindia