Scientists use AI tools to assess the level of loneliness of the elderly. TUCHONG
We know that artificial intelligence (AI) is smart enough to do a few things our minds cannot, and with incredible accuracy. And now, it seems it also has the capacity to detect loneliness in humans, which is an otherwise challenging task.
我们知道，人工智能足够聪明，能以极高的精确度实现人类大脑做不到的一些事。而如今，人工智能似乎还能做到又一项极具挑战的任务 —— 检测出人类的孤独指数。
A new study, led by researchers at the University of California San Diego School of Medicine, US, has shown how AI tools can predict levels of loneliness from a person’s speech with an accuracy rate of 94 percent.
The study focused on 80 participants aged 66 to 94, a population particularly vulnerable to loneliness. The subjects were asked 20 questions from the University of California Los Angeles (UCLA) Loneliness Scale, which uses a four-point rating scale for questions such as “How often do you feel left out?” and “How often do you feel part of a group of friends?”
They were also interviewed in private conversations, which were recorded and transcribed by researchers. The transcripts were then examined using natural language processing tools, including IBM Watson Natural Language Understanding (WNLU) software, to quantify expressed emotions.
The interesting thing about this system is that it not only uses dictionary-based methods, such as searching for specific words that express fear, but also presents corresponding patterns by testing the words used in the response.
Varsha Badal, the first author of the study, noted that the WNLU software system uses deep learning to extract data from keywords, categories, emotions and grammar.
“Natural language processing and machine learning can systematically examine long interviews from multiple individuals and explore how subtle speech features such as emotions may indicate loneliness,” Badal said. “Similar emotion analyses by humans would be open to bias, lack consistency, and require extensive training to standardize.”
The more lonely a person felt, the longer their responses to direct questions regarding loneliness. The system was capable of not just detecting the degree of loneliness in each subject, but also showing differences between the way men and women spoke about loneliness. The men were found to use more fearful and joyful words in their responses, while the women tended to acknowledge feeling lonely during interviews.
Co-author Dilip Jeste said that the IBM-UC San Diego Center is now exploring natural language patterns 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 understanding of aging and to help contribute to successful aging,” he said.