TY - JOUR
T1 - Decoding loneliness
T2 - Can explainable AI help in understanding language differences in lonely older adults?
AU - Wang, Ning
AU - Goel, Sanchit
AU - Ibrahim, Stephanie
AU - Badal, Varsha D.
AU - Depp, Colin
AU - Bilal, Erhan
AU - Subbalakshmi, Koduvayur
AU - Lee, Ellen
N1 - Publisher Copyright:
© 2024
PY - 2024/9
Y1 - 2024/9
N2 - Study objectives: Loneliness impacts the health of many older adults, yet effective and targeted interventions are lacking. Compared to surveys, speech data can capture the personalized experience of loneliness. In this proof-of-concept study, we used Natural Language Processing to extract novel linguistic features and AI approaches to identify linguistic features that distinguish lonely adults from non-lonely adults. Methods: Participants completed UCLA loneliness scales and semi-structured interviews (sections: social relationships, loneliness, successful aging, meaning/purpose in life, wisdom, technology and successful aging). We used the Linguistic Inquiry and Word Count (LIWC-22) program to analyze linguistic features and built a classifier to predict loneliness. Each interview section was analyzed using an explainable AI (XAI) model to classify loneliness. Results: The sample included 97 older adults (age 66–101 years, 65 % women). The model had high accuracy (Accuracy: 0.889, AUC: 0.8), precision (F1: 0.8), and recall (1.0). The sections on social relationships and loneliness were most important for classifying loneliness. Social themes, conversational fillers, and pronoun usage were important features for classifying loneliness. Conclusions: XAI approaches can be used to detect loneliness through the analyses of unstructured speech and to better understand the experience of loneliness.
AB - Study objectives: Loneliness impacts the health of many older adults, yet effective and targeted interventions are lacking. Compared to surveys, speech data can capture the personalized experience of loneliness. In this proof-of-concept study, we used Natural Language Processing to extract novel linguistic features and AI approaches to identify linguistic features that distinguish lonely adults from non-lonely adults. Methods: Participants completed UCLA loneliness scales and semi-structured interviews (sections: social relationships, loneliness, successful aging, meaning/purpose in life, wisdom, technology and successful aging). We used the Linguistic Inquiry and Word Count (LIWC-22) program to analyze linguistic features and built a classifier to predict loneliness. Each interview section was analyzed using an explainable AI (XAI) model to classify loneliness. Results: The sample included 97 older adults (age 66–101 years, 65 % women). The model had high accuracy (Accuracy: 0.889, AUC: 0.8), precision (F1: 0.8), and recall (1.0). The sections on social relationships and loneliness were most important for classifying loneliness. Social themes, conversational fillers, and pronoun usage were important features for classifying loneliness. Conclusions: XAI approaches can be used to detect loneliness through the analyses of unstructured speech and to better understand the experience of loneliness.
KW - Aging
KW - Artificial intelligence
KW - Language
KW - Natural language processing
KW - Speech
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U2 - 10.1016/j.psychres.2024.116078
DO - 10.1016/j.psychres.2024.116078
M3 - Article
C2 - 39003802
AN - SCOPUS:85198557971
SN - 0165-1781
VL - 339
JO - Psychiatry Research
JF - Psychiatry Research
M1 - 116078
ER -