A machine learning algorithm that predicts a suicide attempt has recently undergone testing. a prospective trial at the institution where it was developed, the Vanderbilt University Medical Center. The results of the trial have been published in JAMA Network Open.
The algorithm, called Vanderbilt model Suicide Attempt and Ideation Likelihood (VSAIL), uses routine information from electronic health records (EHR) to estimate the 30-day risk of visits for suicide attempts and, by extension, suicidal ideation.
VSAIL
During the 11-month trial, about 78,000 adult patients were seen at VUMC's hospital, emergency room and surgical clinics.
To the stratify adult patients In eight groups based on their algorithmic suicide risk scores, the top stratum alone accounted for more than a third of all suicide attempts documented in the study and about half of all cases of suicidal ideation.
As documented in the EHR, one in 23 people in this high-risk group reported suicidal thoughts and one in 271 attempted suicide.
Currently, suicide has increased in new generations in many first world countries. But, even without taking such spikes into account, in countries like Spain, where there are comparatively few suicides, there are ten times as many suicides as homicides. In 2017, more than 47,000 Americans died by suicide and it is estimated that 1.4 million suicide attempts.
There are an average of 129 suicides in the United States per day, and Tennessee accounts for an above-average rate of one suicide every eight hours. Suicide is the tenth leading cause of death in the United States.
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The news
This algorithm is capable of calculating the risk of suicide, one of the main causes of unnatural death
was originally published in
Xataka Science
by
Sergio Parra
.