@article{ref1,
title="Decoding loneliness: can explainable AI help in understanding language differences in lonely older adults?",
journal="Psychiatry research",
year="2024",
author="Wang, Ning and Goel, Sanchit and Ibrahim, Stephanie and Badal, Varsha D. and Depp, Colin and Bilal, Erhan and Subbalakshmi, Koduvayur and Lee, Ellen",
volume="339",
number="",
pages="e116078-e116078",
abstract="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. <br><br>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. <br><br>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. <br><br>CONCLUSIONS: XAI approaches can be used to detect loneliness through the analyses of unstructured speech and to better understand the experience of loneliness.<p /> <p>Language: en</p>",
language="en",
issn="0165-1781",
doi="10.1016/j.psychres.2024.116078",
url="http://dx.doi.org/10.1016/j.psychres.2024.116078"
}