@article{ref1,
title="A machine learning approach to identifying suicide risk among text-based crisis counseling encounters",
journal="Frontiers in psychiatry",
year="2023",
author="Broadbent, Meghan and Medina Grespan, Mattia and Axford, Katherine and Zhang, Xinyao and Srikumar, Vivek and Kious, Brent and Imel, Zac",
volume="14",
number="",
pages="e1110527-e1110527",
abstract="INTRODUCTION: With the increasing utilization of text-based suicide crisis counseling, new means of identifying at risk clients must be explored. Natural language processing (NLP) holds promise for evaluating the content of crisis counseling; here we use a data-driven approach to evaluate NLP methods in identifying client suicide risk. <br><br>METHODS: De-identified crisis counseling data from a regional text-based crisis encounter and mobile tipline application were used to evaluate two modeling approaches in classifying client suicide risk levels. A manual evaluation of model errors and system behavior was conducted. <br><br>RESULTS: The neural model outperformed a term frequency-inverse document frequency (tf-idf) model in the false-negative rate. While 75% of the neural model's false negative encounters had some discussion of suicidality, 62.5% saw a resolution of the client's initial concerns. Similarly, the neural model detected signals of suicidality in 60.6% of false-positive encounters. <br><br>DISCUSSION: The neural model demonstrated greater sensitivity in the detection of client suicide risk. A manual assessment of errors and model performance reflected these same findings, detecting higher levels of risk in many of the false-positive encounters and lower levels of risk in many of the false negatives. NLP-based models can detect the suicide risk of text-based crisis encounters from the encounter's content.<p /> <p>Language: en</p>",
language="en",
issn="1664-0640",
doi="10.3389/fpsyt.2023.1110527",
url="http://dx.doi.org/10.3389/fpsyt.2023.1110527"
}