Artificially intelligent homes for Alzheimer's patients coming: scientists

AITopics Original Links

Scientists in Toronto are developing an artificial intelligence system that would help people with Alzheimer's disease or other cognitive impairments live safely at home. The Toronto Rehabilitation Institute is working with University of Toronto researchers to make home-based computer systems that would assist elderly people with memory loss in living independently. More than 750,000 Canadians will have Alzheimer's or a related dementia by 2031, according to the researchers. "Often when a person gets moderate to severe levels of impairment, they are taken out of their home and put into a care facility," lead scientist Alex Mihailidis said in a written statement. "We are using artificial intelligence to support aging-in-place so that people can remain in their homes for as long as possible."

Study Finds Link Between Heavy Use Of Digital Media And ADHD In Teens, But Questions Remain

Forbes - Tech

Concerns continue rising about how much time we're spending on our smartphones and the effects digital immersion is having on both who we are and what we're becoming. A new study adds to the uneasiness, suggesting that heavy use of digital media via smartphones and other devices is significantly linked to more symptoms of Attention Deficit Hyperactivity Disorder (ADHD) among teens over time. Researchers monitored nearly 2,600 high school students, ages 15 and 16, over a two-year period and found that the heaviest users of digital media platforms were almost twice as likely to develop ADHD symptoms. The students reported how frequently they used 14 platforms, including social media, texting and streaming video, and the researchers used their responses to establish three categories of use: no use; medium use and high use. Assessments from the students about their levels of multiple ADHD symptoms were monitored every six months between 2014 and 2016 (from 10th grade to 12th grade).

Insight into Social Support of Autism Blogger Community in Microblogging Platform

AAAI Conferences

At current time social media seems to be an easy and popular media where parents of autistic kids, share and relate their experiences of families of children with ASD and build a social bonding. Semantic analysis of vast amounts of social media content such as blogs, tweets, and Facebook postings can be proved to be a cost effective, compelling and practical learning tool for what parents with autistic kids say about effective ways to deal with various challenges as experienced by the caretakers. The purpose of the current study is to provide a research-based understanding of social media conversations among families dealing with autism. Through such interactions, the study would further analyze the efficacy of the various forms of daily intervention strategies and therapies to help kids with ASD to cope with daily life challenges. Furthermore, by systematically analyzing the users’ interactions and feedback about an issue or a topic, strategic health messaging and the claims/arguments surrounding a health issue can be crafted with accuracy and sensitivity which can guide the care service provider to reduce costs and be more effective.

Computational Content Analysis of Negative Tweets for Obesity, Diet, Diabetes, and Exercise Machine Learning

Social media based digital epidemiology has the potential to support faster response and deeper understanding of public health related threats. This study proposes a new framework to analyze unstructured health related textual data via Twitter users' post (tweets) to characterize the negative health sentiments and non-health related concerns in relations to the corpus of negative sentiments, regarding Diet Diabetes Exercise, and Obesity (DDEO). Through the collection of 6 million Tweets for one month, this study identified the prominent topics of users as it relates to the negative sentiments. Our proposed framework uses two text mining methods, sentiment analysis and topic modeling, to discover negative topics. The negative sentiments of Twitter users support the literature narratives and the many morbidity issues that are associated with DDEO and the linkage between obesity and diabetes. The framework offers a potential method to understand the publics' opinions and sentiments regarding DDEO. More importantly, this research provides new opportunities for computational social scientists, medical experts, and public health professionals to collectively address DDEO-related issues.

Understanding Anti-Vaccination Attitudes in Social Media

AAAI Conferences

The anti-vaccination movement threatens public health by reducing the likelihood of disease eradication. With social media’s purported role in disseminating anti-vaccine information, it is imperative to understand the drivers of attitudes among participants involved in the vaccination debate on a communication channel critical to the movement: Twitter. Using four years of longitudinal data capturing vaccine discussions on Twitter, we identify users who persistently hold pro and anti attitudes, and those who newly adopt anti attitudes towards vaccination. After gathering each user’s entire Twitter timeline, totaling to over 3 million tweets, we explore differences in the individual narratives across the user cohorts. We find that those with long-term anti-vaccination attitudes manifest conspiratorial thinking, mistrust in government, and are resolute and in-group focused in language. New adoptees appear to be predisposed to form anti-vaccination attitudes via similar government distrust and general paranoia, but are more social and less certain than their long-term counterparts. We discuss how this apparent predisposition can interact with social media-fueled events to bring newcomers into the anti-vaccination movement. Given the strong base of conspiratorial thinking underlying anti-vaccination attitudes, we conclude by highlighting the need for alternatives to traditional methods of using authoritative sources such as the government when correcting misleading vaccination claims.