We need better ways to help people. What's the medical breakthrough that could save the most lives in the U.S. over the next ten years? In the 2020s, medical research will likely inch forward when it comes to major killers like heart disease and cancer. But the biggest potential to save lives could lie in learning to prevent suicide. The rates of reported suicides have been creeping up over the last two decades.

The post caught the attention of Facebook's AI system, which is programmed to spot potential suicidal language. The system decided it was an emergency and passed it along to moderators for review, who then alerted authorities in Buenos Aires. Before long, first responders were on the scene. "Artificial intelligence can be a very powerful tool," says Enrique del Carril, the investigations director in the district attorney's office in Buenos Aires. "We saved a woman far away in remote Argentina before something terrible happened. Facebook's suicide-alert system is just one of many efforts to use artificial intelligence to help identify people at risk for suicide as early as possible. In these programs, researchers use computers to comb through massive amounts of data, such as electronic health records, social-media posts, and audio and video recordings of patients, to find common threads among people who attempted suicide. Then algorithms can start to predict which new patients are more ...

At 10 public schools in Cincinnati, middle and high school students will have a new app looking out for them this year. When a student from those schools goes to the health clinic for a talk with the staff psychologist, an iPhone app will listen to the conversation and flag those students it considers likely to attempt suicide. There's a dire need for tech that can detect young people who need help. Suicide is the second-leading cause of death for people ages 15 to 24, surpassed only by accidents. The tech, which has been tested in the Cincinnati schools during the past two years, comes from John Pestian, director of the computational medicine lab at Cincinnati Children's Hospital.

Scientists say machine learning is up to 93 percent accurate in identifying a suicidal person based on their responses to interview questions. The algorithm was described in a study published in the journal Suicide and Life-Threatening Behavior. Researchers were able to use the tool to classify patients as being suicidal, mentally ill but not suicidal, or neither. "These computational approaches provide novel opportunities to apply technological innovations in suicide care and prevention, and it surely is needed," study author John Pestian said in a press release. "When you look around healthcare facilities, you see tremendous support from technology, but not so much for those who care for mental illness. Only now are our algorithms capable of supporting those caregivers."

Researchers are developing an app which could help to prevent suicides by flagging those most at risk. Using a computer algorithm, it records conversations, analysing what people say and how they speak. By picking up on a range of subtle verbal and non-verbal cues, it can correctly classify if someone is suicidal with 93 per cent accuracy. At the heart of the app is a machine learning algorithm which classifies the person based on their responses. In an earlier study, researchers enrolled a mix of 379 patients, who were suicidal, diagnosed as mentally ill, or neither.

Dr. John Pestian, professor of pediatrics, psychiatry, and biomedical informatics at Cincinnati Children's Hospital Medical Center within the University of Cincinnati, and his team have created an app that examines language of suicidal teens. They call it SAM (Spreading Activation Mobile). Any parent of a teen knows all too well the ups and downs of those tumultuous years. At best, we feel utterly infuriated. At worst, we feel completely helpless and shaken to our core -- especially if our kids harm, or threaten to kill, themselves.

Dr. John Pestian, professor of pediatrics, psychiatry, and biomedical informatics at Cincinnati Children's Hospital Medical Center within the University of Cincinnati, and his team have created an app that examines language of suicidal teens. They call it SAM (Spreading Activation Mobile). Any parent of a teen knows all too well the ups and downs of those tumultuous years. At best, parents feel utterly infuriated. At worst, completely helpless and shaken to the core -- especially if kids harm, or threaten to kill, themselves.

Efforts to understand suicide risks can be roughly clustered into traits or states. Trait analyses focus on stable characteristics rooted in and measured using biological processes (Costanza et al., 2014; Le-Niculescu et al., 2013), whereas state analyses measure dynamic characteristics like verbal and nonverbal communication, termed "thought markers" (Pestian et al., 2015). Machine learning and natural language processing have successfully identified differences in retrospective suicide notes, newsgroups, and social media (Gomez, 2014; Huang, Goh, & Liew, 2007; Matykiewicz, Duch, & Pestian, 2009). Jashinsky et al. (2015) used multiple annotators to identify the risk of suicide from the keywords and phrases (interrater reliability .79) in geographically based tweets. Thompson, Poulin, and Bryan (2014) and Desmet (2014) used text-based signals to identify suicide risk that ranged from 60% to 90%.

Digital tools using machine learning to analyze a person's spoken or written words could be instrumental in aiding mental health clinicians in assessments determining whether that person is suicidal, researchers have found. A new study published in the journal Suicide and Life-Threatening Behavior found machine learning is 93 percent accurate in correctly identifying a suicidal person, and is 85 percent accurate in determining differential diagnosis of mental illness. The study, led by researchers at the Cincinnati Children's Hospital Medical Center, looked at 379 patients who were recruited from three different sites – two academic medical centers and a rural community hospital. "Death by suicide demonstrates profound personal suffering and societal failure," writes lead author Dr. John Pestian, who is also a professor of biomedical informatics and psychiatry at Cincinnati Children's. "While basic sciences provide the opportunity to understand biological markers related to suicide, computer science provides opportunities to understand suicide thought markers."

Scientists say machine learning is up to 93 percent accurate in identifying a suicidal person based on their responses to interview questions. The algorithm was described in a study published in the journal Suicide and Life-Threatening Behavior. Researchers were able to use the tool to classify patients as being suicidal, mentally ill but not suicidal, or neither. "These computational approaches provide novel opportunities to apply technological innovations in suicide care and prevention, and it surely is needed," study author John Pestian said in a press release. "When you look around healthcare facilities, you see tremendous support from technology, but not so much for those who care for mental illness. Only now are our algorithms capable of supporting those caregivers."