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README.md

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Identification of Imminent Suicide Risk Among Young Adults using Text Messages

Documentation

Suicide is the second leading cause of death among young adults but the challenges of preventing suicide are significant because the signs often seem invisible. Research has shown that clinicians are not able to reliably predict when someone is at greatest risk. In this paper, we describe the design, collection, and analysis of text messages from individuals with a history of suicidal thoughts and behaviors to build a model to identify periods of suicidality (i.e., suicidal ideation and non-fatal suicide attempts). By reconstructing the timeline of recent suicidal behaviors through a retrospective clinical interview, this study utilizes a prospective research design to understand if text communications can predict periods of suicidality versus depression. Identifying subtle clues in communication indicating when someone is at heightened risk of a suicide attempt may allow for more effective prevention of suicide.

Installation

There are git in this repository; to clone all the needed files, please use:

git clone --recursive https://github.com/BarnesLab/Identification-of-Imminent-Suicide-Risk-Among-Young-Adults-using-Text-Messages.git

The primary requirements for this package are Python 3 with Tensorflow. The requirements.txt file contains a listing of the required Python packages; to install all requirements, run the following:

pip -r install requirements.txt

Or

pip3  install -r requirements.txt

Or:

conda install --file requirements.txt

If the above command does not work, use the following:

sudo -H pip  install -r requirements.txt

General:

Error and Comments:

Send an email to kk7nc@virginia.edu

Citation:

@inproceedings{nobles2018identification,
  title={Identification of Imminent Suicide Risk Among Young Adults using Text Messages},
  author={Nobles, Alicia L. and Glenn, Jeffrey J. and Kowsari, Kamran and Teachman, Bethany A. and Barnes, Laura E.},
  booktitle={Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems},
  year={2018},
  organization={ACM},
  doi={10.1145/3173574.3173987}
}
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