One of the less remarked upon aspects of the COVID-19 pandemic is the increasing isolation many people are feeling as lockdowns and activity restrictions continue.
As if on cue, then, researchers at University of California San Diego School of Medicine have released a “proof of concept” study demonstrating how AI tools can predict the level of loneliness in older adults by analyzing speech patterns.
The team used natural language processing (NLP) tools to analyze the transcripts of interviews conducted with 80 older adults. According to a review of the project, each interviewee “was evaluated using conventional loneliness assessments as well as completing a longer, more conversational, semi-structured interview lasting up to 90 minutes. . . As well as detecting loneliness in subjects not picked up by conventional assessments, the system uncovered differences in the way men and women talk about loneliness.”
According to Varsha Badal, an author of the study, "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.”
Similarly, Ellen Lee, senior author on the new research, suggests loneliness is a particularly difficult psychiatric condition to measure and because doctors generally struggle to quantify loneliness in patients there is a pressing need for some kind of objective measure.
"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,'" explained Lee. "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.”
According to the study’s report, the AI system could qualitatively predict a subject’s loneliness with 94 percent accuracy. “The more lonely a person was feeling, the longer their responses were to direct questions regarding loneliness. The researchers even suggested the presence of a kind of ‘lonely speech’ pattern could be used in the future to monitor the well-being of older subjects.”
Even without incorporating these kinds of NLP tools in current practice, the researchers suggest the study’s findings offer clinicians important insights into the different ways men and women express loneliness.
In conclusion, the researchers said “future studies will need larger samples of diverse individuals, combined with other sensor data-streams (e.g., voice recordings, social interactions, GPS data, physical activity or sleep measures) to personalize the findings. . . Eventually, complex AI systems could intervene in real-time to help individuals to reduce their loneliness by adopting in positive cognitions, managing social anxiety, and engaging in meaningful social activities.”