TY  - JOUR
PY  - 2024//
TI  - Decoding loneliness: can explainable AI help in understanding language differences in lonely older adults?
JO  - Psychiatry research
A1  - Wang, Ning
A1  - Goel, Sanchit
A1  - Ibrahim, Stephanie
A1  - Badal, Varsha D.
A1  - Depp, Colin
A1  - Bilal, Erhan
A1  - Subbalakshmi, Koduvayur
A1  - Lee, Ellen
SP  - e116078
EP  - e116078
VL  - 339
IS  - 
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. <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>
LA  - en
SN  - 0165-1781
UR  - http://dx.doi.org/10.1016/j.psychres.2024.116078
ID  - ref1
ER  -