Document Type

Conference Proceeding

Language

eng

Publication Date

2020

Publisher

Information Systems for Crisis Response and Management (ISCRAM)

Source Publication

ISCRAM 2020 Conference Proceedings

Source ISSN

97819493732712

Abstract

This paper seeks to establish a machine learning driven method by which a military veteran with Post-Traumatic Stress Disorder (PTSD) is classified as being in a crisis situation or not, based upon a given set of criteria. Optimizing alerting decision rules is critical to ensure that veterans at highest risk for mental health crisis rapidly receive additional attention. Subject matter experts in our team (a psychologist, a medical anthropologist, and an expert veteran), defined acute crisis, early warning signs and long-term crisis from this dataset. First, we used a decision tree to find an early time point when the peer mentors (who are also veterans) need to observe the behavior of veterans to make a decision about conducting an intervention. Three different machine learning algorithms were used to predict long term crisis using acute crisis and early warning signs within the determined time point.

Comments

Published version. Published as part of the proceedings of the conference, ISCRAM 2020 Conference Proceedings (2020): 122-133. Publisher link. © 2020 Information Systems for Crisis Response and Management (ISCRAM). Used with permission.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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