The probabilistic model predicted 38% of suicides

Us scientists have trained a probabilistic model on patient data to predict the likelihood of suicide for two years before it occurs. To do this, they used data on about 3.7 million patients, of which have committed suicide about 39 thousand, and trained them to a Bayesian classifier. Was able to predict 38% of all suicides with an accuracy of up to 90 percent. Among the factors predicting the likelihood of suicide, the most significant were mental disorders and substance abuse, write the scientists in the JAMA Network Open.

According to the latest who report, published in September 2019, suicide is the second most common cause of death among young people aged 15 to 29 years (the first place is a death in a traffic accident). For the regulation of such dynamics, some governments impose various restrictive measures and the more commonly used strategies are restricting access to potential suicide methods (for example, a ban on toxic substances) and media censorship. The number of suicide still continues to grow — not only in developing countries but also in developed.

A more effective method of reducing the number of suicides can be timely prevention, but to predict a suicide attempt can certainly be very difficult. So, last year, a meta-analysis showedthat the presence of suicidal thoughts is not always correlated with actual subsequent suicide attempt: according to the findings, about 60 percent of the suicide is not spoken about their intentions. Interestingly, no such dependence was observed as psychiatric patients and healthy population.

This, of course, a certain correlation between the risk of suicide and health indicators are still there: for example, prone to suicidal thoughts people this risk increases with the abuse of some substances. Jordan Smoller (Jordan Smoller) from the General Clinical hospital of Massachusetts and his colleagues decided to analyze in detail the totality of such factors and their influence on the risk of suicide. For this, they used data about the health of 3.7 million participants in five different medical research. For each sample, the researchers assessed the relationship between specific indicators of the health of participants and suicides using a simple probabilistic model — naïve Bayesian classifier: half of the data from each study was used for training, half for testing.

For all the research and analysis were registered 39162 cases of suicide — approximately 1.1 percent of the total sample. Factors correlated with death by suicide was different for different samples in the first place due to differing demographics.

The most common factors were diagnosed with mental disorders and States (in particular, bipolar disorder and borderline personality disorder) and drug and alcohol abuse and also dependence on them. In addition, the list of factors included, for example, open wounds of the limbs and endured the attack with a bladed weapon. These factors can be indirect: the use of opioids against pain can lead to abuse and addiction, but the attack — the symptoms of post-traumatic stress disorder.

Overall, across the five samples, the probabilistic models were able to predict 38% of the suicides in an average 2.1 years before it happens with accuracy of up to 90 percent. Of course, to use such a model as the only method of prediction and early prevention of suicide is impossible — largely because of a lack of efficiency. With this selection of plausible factors might help in the prevention of physicians — even if these factors, such as open wounds — most likely indirect.

Another objective factor that can predict the likelihood of suicide, — insensitivity to signals of the body and discomfort: as recently found out by scientists, people who attempt suicide, unable longer to hold your breath and keep your hand in cold water.

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