Artificial intelligence can predict the suicide
For years, Facebook has been investing in artificial intelligence fields like machine learning and deep neural nets to build its core business—selling you things better than anyone else in the world. But earlier this month, the company began turning some of those AI tools to a more noble goal: stopping people from taking their own lives. Doctors at research hospitals and even the US Department of Veterans Affairs are piloting new, AI-driven suicide-prevention platforms that capture more data than ever before.
The goal: build predictive models to tailor interventions earlier. Because preventative medicine is the best medicine, especially when it comes to mental health. Suicide rates surged to a year high inthe last year for which the Centers for Disease Control and Prevention has data. The problem is, for more than 50 years doctors have relied on correlating suicide-risk with depression and drug abuse.
And the research says they're only slightly better at it than a coin flip. But artificial intelligence offers the possibility to identify suicide-prone people more accurately, creating opportunities to intervene long before thoughts turn to action. A study publishing later this month used machine learning to predict with 80 to 90 percent accuracy whether or not someone will attempt suicide, as far off as two years in the future.
Their technique is similar to the text mining Facebook is using on its wall posts. The social network already had a system in which users can report posts that suggest a user is at risk of self harm.Government will use artificial intelligence to predict suicides
In a personal post, Mark Zuckerberg described how the company is integrating the pilot with other suicide prevention measures, like the ability to reach out to someone during a live video stream.
The next step would be to use AI to analyze video, audio, and text comments simultaneously. But in a live stream, the only text comes from commenters. Pills would be way harder. Ideally though, you can intervene even earlier. Cogito, a Darpa-funded, MIT-spinoff company, is currently testing an app that creates a picture of your mental health just by listening to the sound of your voice.
Called Companion, the opt-in software passively gathers all the things users say in a day, picking up on vocal cues that signal depression and other mood changes. As opposed to the content of their words, Companion analyzes the tone, energy, fluidity of speaking and levels of engagement with a conversation. The VA is currently piloting the platform with a few hundred veterans—a particularly high-risk group.
Those are exactly the kinds of shifts that might not be obvious to a primary care provider unless they were self-reported. David K.
Think about it. Between all the sensors in your phone, its camera and microphone and messages, that device's data could tell a lot about you. More so, potentially, than you could see about yourself. But to a machine finely tuned to your habits and warning signs that gets smarter the more time it spends with your data, that might be a red flag.
Getty Images.How can we tell if someone is a suicide risk? Many individuals with suicidal thoughts attempt to mask them. With suicide now a leading cause of death in the United States, researchers and medical professionals must think outside the box and devise new strategies for assessment and prevention. In alone, 45, people lost their lives to suicide across the US. Roughly 54 percent of those who died by suicide were never even diagnosed with a psychiatric condition.
Intervention to prevent an attempted suicide will not be initiated because no warnings were detected. Some people who die by suicide may exhibit a healthy outward appearance, have a healthy social life, and a good career, without risk factors being detected. Identifying and assessing suicide risks requires several innovative approaches, as traditional methods may not always be effective. In medical settings, some doctors and medical staff are trained to identify suicide risks in patients who are treated for physical injuries and other health conditions.
They may take demographics, medical history, and access to guns into account. The algorithm was found to be 84 percent accurate at predicting near-future suicide attempts when tested on more than 5, patients who were admitted for self-harm or attempted suicide.
In addition, the algorithm was 80 percent accurate when predicting potential suicide attempts within the next two years. Machine learning algorithms are not currently used in the United States but Colin Walsh, the Vanderbilt data scientist who led the project, urges widespread use of this technology in all medical settings.
Facebook currently has similar algorithms that work on a much smaller scale but may quickly flag posts that suggest a user may be suicidal. It then connects users with mental health services. Walsh seeks to someday make this a reality and is currently working with doctors to devise an intervention program based on artificial intelligence. If the program is fully implemented, patients found to be at high risk may be admitted to a hospital for several days under supervision.
If a lower risk is identified, patients may be referred to mental health specialists for other treatment options. Walsh expects this program to be widely used within the next two years. Until then, the algorithm may require further testing and development, as its effectiveness may not always be reliable. Incorrect algorithm data must be corrected during the development of this technology.
For example, if the algorithm found a patient to be suicidal, but further assessment from a doctor determined otherwise, that information must be fed to the algorithm.
On the other hand, if a patient was not found to be suicidal or found to be low riskbut dies by suicide weeks later, that data must also be updated. In addition, ethical issues must be addressed, such as ensuring patients are informed when they are assessed for suicide risks and that their privacy is protected.
Moreover, this technology should only be used to identify suicide risks, so medical professionals can further assess patients. The idea of holding patients against their will due to an inaccurate screening raises further future ethical issues. The Law Offices of Skip Simpson will keep an eye on these developments. We are hopeful that using machine learning to analyze data in medical settings will identify suicide risks in outwardly healthy individuals and prompt doctors to provide preventative treatment.
Machine learning has the potential to greatly improve our prediction of suicidal behavior. The technology may also raise legal issues if a patient is harmed due to a faulty screening. For example, if a patient was suicidal at the time of a screening, but an inaccurate screening determined otherwise, and medical staff failed to provide assessment and intervention, those responsible should be held accountable. Suicide prevention in medical settings should be multifaceted.
There must be a balance between technological methods and the judgment of trained doctors and psychiatric specialists. If you lost a loved one to suicide following medical treatment, nationally known suicide attorney Skip Simpson would like to discuss your matter with you and explore your legal options.The journal chose to begin the series with suicide which has been on the increase in the U.
The article underscores the ways that psychologists from a wide range of specialties along with the aid of other scientists are tackling the crisis. For example, scientists from a variety of disciplines are examining associated brain changes and risk factors while others are looking at new ways to discover who is at risk. Similarly, clinical researchers are testing new interventions while front-line clinicians are helping to deliver those treatments to those who are suffering.
I went on to read the article because the topic of suicide was a focus in my early career. Right after I finished my PhD in clinical psychology at the University of Michigan, I was hired by Counseling Services there to set up a Suicide Hotline for the campus and surrounding community and to train the phone volunteers.
So, I needed to be a quick study and come up to speed with what was known about suicide prevention at that time. Of course, there has been an explosion of research since that time. Consequently my podcasting and blogging have focused on ways hi-tech is impacting our field and society at large.
How artificial intelligence will save lives: Prediction of future suicide risk
The statistics I cut my teeth on back in those early days were T-tests, correlation coefficients, Chi square, analysis of variance, and such. As computers were beginning to be available, factor analysis was the hot new thing. Given that I was bound for becoming a therapist, I never bothered to get my hands dirty with factor analysis.
The place where Machine Learning really excels is with very large data sets. The software is able to sort through it all to discover relationships that would elude our human brains. But I digress. For people ages 35 to 54, it ranks fourth, and for to year-olds, second. In the past, sample sizes were too small to provide the statistical power needed. There are obvious risk factors associated with increased suicide risk, including depressionanxiety, sociodemographic factors and substance use.
The ability to accurately predict suicide is important for future research into the causes of suicide as well as an aide to making clinical decisions about the likelihood of a given patient committing suicide. In other words, the clinician might as well just flip a coin to judge whether the person in front of them was likely to kill himself or herself! Predicting risk of suicide attempts over time through machine learning.
Clinical Psychological Science. They applied machine learning to the electronic health records of more than 5, adults who had a history of self-injury. They developed an algorithm that predicted suicide attempts based on combinations of risk factors including demographic data, previous diagnoses, medication history and past health-care utilization. There have been similar reports of Deep Learning systems superior identification of a variety of eye diseases using retinal scans.
However, my initial enthusiasm has been tempered somewhat as a result of additional reading on the Internet. An online article in the Washington Post cautions that suicide prediction technology is revolutionary but badly needs oversight. According to the article. IBM would take the breakthrough technology it showed off on television—mainly, the ability to understand natural language—and apply it to medicine.
In fact, the projects that IBM announced that first day did not yield commercial products. In the eight years since, IBM has trumpeted many more high-profile efforts to develop AI-powered medical technology—many of which have fizzled, and a few of which have failed spectacularly.
Clearly I need to throttle back my techno-enthusiasm, but I do expect we will see other significant contributions from AI to psychological science and practice in the future, beyond the demonstrated ability to improve suicide prediction.
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By clicking below to subscribe, you acknowledge that your information will be transferred to Mailchimp for processing. Learn more about Mailchimp's privacy practices here. Your email address will not be published.Ai-Da, the humanoid robot artist. For an example of the remainingresearchers at the University of California, San Francisco have been ready this year to angle individuals as much as brain displays and generate herbal-sounding artificial accent out of bald brain activity. The aim is to supply people who have lost the means to talk — as a result of a strokeA.
One area the A. Simple affliction physicians may also be mediocre at recognizing if an affected person is heartbroken, or at predicting who is about to become depressed. Many people consider suicidenevertheless, it is very difficult to inform who is actually serious about it. Using A. The crisis textual content line is a suicide prevention hotline in which people communicate via texting as an alternative of mobile phone calls.
The usage of A. On its site, the crisis text band posts the words that individuals who are critically considering suicide commonly utilize of their texts. Abounding agencies are using A.
For instance, after listening to tens of millions of conversations, machines can select depressed people based on their talking patterns. When individuals plagued by depression speak, the latitude and angle of their voice tend to be on the decrease. There are greater pauses, starts and stops amid words. Individuals whose speech has a breathy first-class are more likely to reattempt suicide.
Machines can observe these items greater than humans. The next evolutionary leap in artificial intelligence is before now driving ROI swimming robot. There are also visible patterns.
Depressed people move their heads much less regularly. One analysis group led by Andrew Reece and Christopher Danforth analyzed, photos from people and identified who turned into sorrowful with percent accurateness, which is more suitable than popular observe doctors. There are alternative routes to accomplish these diagnoses. A corporation known as Mindstrong is attempting to measure intellectual fitness by way of how people exhaust their smartphones: how they write and scroll, how commonly they delete characters.
It accurately estimated makes an attempt practically substantial percentage of the time. By means of accumulating facts of real-world interactions similar to laughter and acrimony, an algorithm in an identical study was in a position to attain a good percent accuracy. In an interview, Topol emphasized how bad we are at diagnosing ailment throughout specialties and determining when to test and how to treat.
Medicine is hard because, as A. There is no single weight loss plan method that is foremost for all people, as a result, we all process meals in our personal distinct manner. Diet, like other treatments, must be personalized. You will also be cool out by means of the privateness-advancing energy of A. You can imagine how problematic this can be if such information is received by the administration or the state.
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Your Career. He was Director and Principal Investigator of the Durkheim Projecta nonpofit big-data collaboration that developed the technology he describes in the book and this story. Gregory Peterson was a board member of the Durkheim Project. As Veterans Day is observed throughout the United States, the country faces a persistent crisis in veterans committing suicide — an average of 22 veterans take their own lives every day.
Concern over the rise in veteran suicides has brought renewed attention from military personnel, elected officials, concerned families and medical professionals. A new technology that uses big data to predict suicide risk in real time could help medical professionals and social workers intervene and prevent suicide. This artificial intelligence technology is featured in the book Artificial Intelligence in Behavioral and Mental Health Carerecently published by Elsevier.
US Army records on suicide rates only go back to the early s, so no one has historical data to provide a long-term perspective on this tragic problem.
If these hearings are a fair indicator, America's military leaders are recognizing the limitations of current medical approaches to suicide prevention. The military narrative also seems to be acknowledging a need to supplement traditional mental health treatment with innovative, if not well defined, approaches.
In a recent Congressional testimony, Dr. And Lt. General James C. McConville of the US Army said:. Both of these quotes are, in fact, accurate descriptions of how technology should be used in the fight against suicide. The suicide crisis in veterans requires the marriage of traditional medicine with emerging technologies that have proved efficacious, both in suicide intervention and for mental health issues more generally.
The Durkheim Project analyzes unstructured linguistic data from social and mobile sources to predict mental health risk. This is facilitated by a predictive analytics engine Predictus.
The predictive engine is integrated with the latest big data technologies e. This combination facilitates a clinical dashboard so timely interventions can be taken to save those at risk.
The Durkheim Project was a nonprofit research effort that ran from late to earlyfocusing on using big data to inform knowledge on suicide. This initiative was named in honor of Emile Durkheim, a sociologist who pioneered linguistics as a tool to model human behavior, including suicide, as described in his book Suicide. The project was run by a multidisciplinary team of artificial intelligence and medical experts from the Geisel School of Medicine at Dartmouththe U.
Together these professionals formed a team dedicated to applying big data research on suicide at the intersection of artificial intelligence and medicine. The project completed its last testing phase earlier this year. In late October, Rep. Hunter also advocated the use of artificial intelligence technology, which has proven more effective than traditional medical methods in health monitoring and the detection of suicide risk among at-risk veterans.
Specifically, the Congressman cited research findings from The Durkheim Project. In the letter, he wrote:. This study, known as the Durkheim Project, took advantage of new data sources, better technologies, and advances in predictive analytics to successfully isolate suicidal intent better than state-of-the-art medical practice. In the letter, Rep. I respectfully request your response to the questions raised in this letter and ask that you address the DOD's core strategy for a risk reduction plan — in particular how DoD can employ the tools used in The Durkheim Project to reduce the high rate of suicide among our active duty personnel and our veterans.When someone commits suicide, their family and friends can be left with the heartbreaking and answerless question of what they could have done differently.
Walsh and his colleagues have created machine-learning algorithms that predict, with unnerving accuracy, the likelihood that a patient will attempt suicide. And, once confidant that the model is sound, Walsh hopes to work with a larger team to establish a suitable method of intervening. He expects to have an intervention program in testing within the next two years.
Read the whole story: Quartz. Your email address will not be published. This site uses Akismet to reduce spam. Learn how your comment data is processed. APS Fellow Alan Leshnerwho has held some of the most prestigious scientific leadership positions in the United States, calls on policymakers to support research into the real causes of mass shootings instead of scapegoating people who struggle with mental disorders.
The World Health Organization has turned to recent report in Psychological Science in the Public Interest to help assess vaccination practices across the globe. Among those who used prescription pain relievers regularly, Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
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Quartz : When someone commits suicide, their family and friends can be left with the heartbreaking and answerless question of what they could have done differently. Leave a Comment Cancel reply Your email address will not be published. Related Stop Blaming Mental Illness APS Fellow Alan Leshnerwho has held some of the most prestigious scientific leadership positions in the United States, calls on policymakers to support research into the real causes of mass shootings instead of scapegoating people who struggle with mental disorders.
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Necessary Always Enabled. Non-necessary Non-necessary.As a child, well before his years in public service, his best friend's father took his own life. So when Gerace heard about a call last July from a Facebook security team member in Ireland to a dispatcher at the center in his county, it struck a familiar chord.
The Facebook representative was calling to alert local officials about a resident who needed urgent assistance. We're trying to intervene when there's a crisis. The Chautauqua County case, first reported in August by the local Post-Journalwas pursued by Facebook because the company had been informed that a woman "had posted threats of harming herself on her Facebook page," the newspaper said.
For years, the company has allowed users to report suicidal content to in-house reviewers, who evaluate it and decide whether a person should be offered support from a suicide prevention hotline or, in extreme cases, have Facebook's law enforcement response team intervene. But this is Facebook, the land of algorithms and artificial intelligence, so there must be an engineering solution. About a year ago, Facebook added technology that automatically flags posts with expressions of suicidal thoughts for the company's human reviewers to analyze.
And in November, Facebook showed proof that the new system had made an impact. Facebook now says the enhanced program is flagging 20 times more cases of suicidal thoughts for content reviewers, and twice as many people are receiving Facebook's suicide prevention support materials. The company has been deploying the updated system in more languages and improving suicide prevention in Instagram, though tools there are at an earlier stage of development.
On Wednesday, Facebook provided more details on the underlying technology. While posts showing suicidal thoughts are very rare — they might make up one in a million — suicide is a pervasive threat. It's one of the top 10 causes of death in the U.
For Facebook, which has over 2. Facebook has built up considerable AI talent and technology sincewhen it established an AI research lab and hired prominent researcher Yann LeCun to lead it. The lab has created an impressive roster of new capabilitiesincluding technology that recognizes objects in users' photos, translates text posts into other languages and transcribes conversations in video ads.
All the tech giants, including AmazonAppleGoogle and Microsoft, have been investing in AI for use across their services and platforms. But suicide prevention hasn't been a hot topic among AI researchers. I have a personal interest in this. Facebook has been exploring suicide prevention for more than a decade.
Artificial intelligence can now predict suicide with remarkable accuracy
It started with information in the social network's help center, where people in need could go to find resources, said Lizzy Donahue, an engineer on the compassion team and a member of the LGBTQ community, which has been disproportionately affected by suicide.
The next step for Facebook was making it possible for users to report suicidal content to the company for human review. That's basically as far as Google and Microsoft are today. For example, a Google search for "I want to kill myself" brings up a phone number for the National Suicide Prevention Lifeline. Microsoft shows relevant crisis center information in search results and lets users report self-harm on its Xbox Live services.
For Facebook, this was just the start. The company now has more than 7, community operations staffers reviewing cases of potential self-punishment, as well as other sensitive issues like bullying and sexual violence. More than a decade ago, employees struggling with deaths of people they knew were reaching out because they felt they had to take action, Reidenberg said.
The first thing Reidenberg did was deliver a list of phrases that are most commonly used by people at risk of suicide. Reidenberg started working with the company on technology summits that Facebook facilitates every two years, where representatives of smaller and larger companies discuss challenges and issues in the field. But the compassion team saw a bolder opportunity to make a difference, taking advantage of Facebook's vast engineering resources. It turned to the company's AI lab.
Early last year, Muriello and others from the compassion team gave an internal talk at the company's Silicon Valley headquarters on how they were starting to use AI in the realm of suicide prevention. Ozertem attended the talk with a colleague from the Applied Machine Learning group, which helps various teams implement core technology from Facebook's research lab. The discussion went on for an hour or more. Muriello said that the goal was to create an automated system that understands context.
For example, a post that says, "If I hear this song one more time, I'm going to kill myself," needs to be understood not as suicidal, he said.