Clinical Trial Details
— Status: Completed
Administrative data
NCT number |
NCT04052477 |
Other study ID # |
RECHMPL19_0320 |
Secondary ID |
|
Status |
Completed |
Phase |
|
First received |
|
Last updated |
|
Start date |
January 1, 2017 |
Est. completion date |
December 30, 2020 |
Study information
Verified date |
September 2021 |
Source |
University Hospital, Montpellier |
Contact |
n/a |
Is FDA regulated |
No |
Health authority |
|
Study type |
Observational
|
Clinical Trial Summary
According to a recent and alarming WHO (World Health Organisation) report (September 4,
2014), one person dies of suicide every 40 seconds in the world. Suicide is the third-leading
cause of death for 15- to 24-year-olds, according to the Centers for Disease Control and
Prevention , after accidents and homicide.
This major public health issue need prevention strategies especially directed to at-risk
populations. Since 2013, more than 2 billion users are enrolled in social networks such as
Twitter or Facebook. Young adults (ages 18 to 29) are the most likely to use social media -
fully 90% do.
Consequently, in this project, we focus on suicide prevention in social media network..
The aim of this project is the validation of the algorithm. This algorithm build a decision
support system that monitor young people at-risk based on large volume of heterogeneous data
collected through social media to improve suicide prevention.
Description:
This study is composed of two steps :
1. 9 subjects were recruited. After patients agreement, computer scientists were accessing
to patient social network profile. Computer scientists were not able to visualize the
content of publications, just run the algorithm that will analyse the content of
messages (text, frequency, emoticons…)
The algorithm defines the 3 most at-risk periods of suicide behaviors, on the next
month. This result were compared to periods found by psychiatric interview. The
psychiatrist then confirmed or not to LIRM whether periods found by the algorithm
conrrespond to those defined by the psychitrist. No data of the social network were
collected.
2. the 2nd step aim to improve the algorithm by collecting sociodemographic and clincal
data related to patients included.