Social isolation and loneliness, which have been increasing in recent years strongly contribute toward suicide rates. Although social isolation and loneliness are not currently recorded within the US National Violent Death Reporting System's (NVDRS) structured variables, natural language processing (NLP) techniques can be used to identify these constructs in law enforcement and coroner medical examiner narratives. Using topic modeling to generate lexicon development and supervised learning classifiers, we developed high-quality classifiers (average F1: .86, accuracy: .82). Evaluating over 300,000 suicides from 2002 to 2020, we identified 1,198 mentioning chronic social isolation. Decedents had higher odds of chronic social isolation classification if they were men (OR = 1.44; CI: 1.24, 1.69, p<.0001), gay (OR = 3.68; 1.97, 6.33, p<.0001), or were divorced (OR = 3.34; 2.68, 4.19, p<.0001). We found significant predictors for other social isolation topics of recent or impending divorce, child custody loss, eviction or recent move, and break-up. Our methods can improve surveillance and prevention of social isolation and loneliness in the United States.

The aging society urgently requires scalable methods to monitor cognitive decline and identify social and psychological factors indicative of dementia risk in older adults. Our machine learning (ML) models captured facial, acoustic, linguistic, and cardiovascular features from 39 individuals with normal cognition or Mild Cognitive Impairment derived from remote video conversations and classified cognitive status, social isolation, neuroticism, and psychological well-being. Our model could distinguish Clinical Dementia Rating Scale (CDR) of 0.5 (vs. 0) with 0.78 area under the receiver operating characteristic curve (AUC), social isolation with 0.75 AUC, neuroticism with 0.71 AUC, and negative affect scales with 0.79 AUC. Recent advances in machine learning offer new opportunities to remotely detect cognitive impairment and assess associated factors, such as neuroticism and psychological well-being. Our experiment showed that speech and language patterns were more useful for quantifying cognitive impairment, whereas facial expression and cardiovascular patterns using photoplethysmography (PPG) were more useful for quantifying personality and psychological well-being.

Fox News' Eben Brown reports that a New-York based health plan is experimenting with robotic pets to help alleviate social isolation. If you or someone you know is having thoughts of suicide, please contact the Suicide & Crisis Lifeline at 988 or 1-800-273-TALK (8255). For some seniors, retirement brings the unbridled joy of more time with loved ones -- but for others, the golden years can end up being quite blue. More than a third of older adults said they feel lonely at least once a week, according to the University of Michigan's National Poll on Healthy Aging. The U.S. Surgeon General even called loneliness and social isolation a "serious health epidemic" in his Advisory on the Healing Effects of Social Connection and Community.

Background: Social support (SS) and social isolation (SI) are social determinants of health (SDOH) associated with psychiatric outcomes. In electronic health records (EHRs), individual-level SS/SI is typically documented as narrative clinical notes rather than structured coded data. Natural language processing (NLP) algorithms can automate the otherwise labor-intensive process of data extraction. Data and Methods: Psychiatric encounter notes from Mount Sinai Health System (MSHS, n=300) and Weill Cornell Medicine (WCM, n=225) were annotated and established a gold standard corpus. A rule-based system (RBS) involving lexicons and a large language model (LLM) using FLAN-T5-XL were developed to identify mentions of SS and SI and their subcategories (e.g., social network, instrumental support, and loneliness). Results: For extracting SS/SI, the RBS obtained higher macro-averaged f-scores than the LLM at both MSHS (0.89 vs. 0.65) and WCM (0.85 vs. 0.82). For extracting subcategories, the RBS also outperformed the LLM at both MSHS (0.90 vs. 0.62) and WCM (0.82 vs. 0.81). Discussion and Conclusion: Unexpectedly, the RBS outperformed the LLMs across all metrics. Intensive review demonstrates that this finding is due to the divergent approach taken by the RBS and LLM. The RBS were designed and refined to follow the same specific rules as the gold standard annotations. Conversely, the LLM were more inclusive with categorization and conformed to common English-language understanding. Both approaches offer advantages and are made available open-source for future testing.

Loneliness and social isolation are serious and widespread problems among older people, affecting their physical and mental health, quality of life, and longevity. In this paper, we propose a ChatGPT-based conversational companion system for elderly people. The system is designed to provide companionship and help reduce feelings of loneliness and social isolation. The system was evaluated with a preliminary study. The results showed that the system was able to generate responses that were relevant to the created elderly personas. However, it is essential to acknowledge the limitations of ChatGPT, such as potential biases and misinformation, and to consider the ethical implications of using AI-based companionship for the elderly, including privacy concerns.

ChatGPT is an AI-powered conversational model developed by OpenAI that has revolutionized the way we communicate with machines. However, like any new technology, there are potential risks and dangers associated with its use. In this article, we will explore the dark side of ChatGPT AI technology and discuss the unseen dangers that lurk beneath its seemingly harmless exterior. The chatbot you are using has been trained on a lot of information from different sources like books, websites, social media posts, and articles on the internet. There's a chance that it has even been trained on your own social media posts. It's not clear if the company behind the chatbot got permission from the original authors to use their information.

Looking at your phone makes other people nearby do the same in less than a minute, a new study reveals. Researchers in Italy investigated human'mimicry' or the'chameleon effect' – subconsciously replicating the physical actions of another human. Out of 184 people, half replicated the action of touching and looking at their phone 30 seconds after a subconscious trigger, researchers found. The experts say copying smartphone use is similar to the well-known'contagious yawning' phenomenon, when an individual yawns in response to someone else doing so. Mammals have evolved to subconsciously mimic each others' behaviour without knowing it.

School district Superintendents Dan Stepenosky and Art Javis weigh in on reopening schools amid the coronavirus pandemic. As COVID-19 cases surge across the country, millions of students are once again shifting to all-remote learning. Between Sunday, Nov. 22 and Monday, Nov. 23, the percentage of students exclusively attending school online jumped from 36.9 to 40 percent. Once again, school leaders and government officials are scrambling to figure out logistics. But there's a huge remote learning side effect they've yet to consider: Student loneliness.

Long before the world had ever heard of covid-19, Kay Tye set out to answer a question that has taken on new resonance in the age of social distancing: When people feel lonely, do they crave social interactions in the same way a hungry person craves food? And could she and her colleagues detect and measure this "hunger" in the neural circuits of the brain? "Loneliness is a universal thing. If I were to ask people on the street, 'Do you know what it means to be lonely?' probably 99 or 100% of people would say yes," explains Tye, a neuroscientist at the Salk Institute of Biological Sciences. "It seems reasonable to argue that it should be a concept in neuroscience. It's just that nobody ever found a way to test it and localize it to specific cells. That's what we are trying to do."

There is a wealth of research on the use of social robots, or companion robots, in care and long-term nursing homes. "Paro the robot seal" and other robotic animals have been linked to reductions in depression, agitation, loneliness, nursing staff stress, and medication use -- especially relevant during this period of pandemic-related social isolation. In the new study, researchers measured the microbial load found on the surface of eight different robot animals (Paro, Miro, Pleo rb, Joy for All dog, Joy for All cat, Furby Connect, Perfect Petzzz dog, and Handmade Hedgehog) after interaction with four care home residents, and again after cleaning by a researcher or care home staff member. The animals ranged in material from fur to soft plastic to solid plastic. The cleaning process involved spraying with anti-bacterial product, brushing any fur, and vigorous cleaning with anti-bacterial wipes.