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Predicting recurrent chat contact in a psychological intervention for the youth using natural language processing

Psychology

Predicting recurrent chat contact in a psychological intervention for the youth using natural language processing

S. Hornstein, J. Scharfenberger, et al.

This study by Silvan Hornstein, Jonas Scharfenberger, Ulrike Lueken, Richard Wundrack, and Kevin Hilbert explores how Natural Language Processing can predict recurrent chat contacts in a German youth crisis service. With an XGBoost classifier achieving an AUROC of 0.68, the research reveals intriguing insights into the demographics and conditions associated with recontact, highlighting NLP's potential for tailored care in chat-based hotlines.

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~3 min • Beginner • English
Abstract
Chat-based counseling hotlines emerged as a promising low-threshold intervention for youth mental health. However, despite the resulting availability of large text corpora, little work has investigated Natural Language Processing (NLP) applications within this setting. Therefore, this preregistered approach (OSF: XA4PN) utilizes a sample of approximately 19,000 children and young adults that received a chat consultation from a 24/7 crisis service in Germany. Around 800,000 messages were used to predict whether chatters would contact the service again, as this would allow the provision of or redirection to additional treatment. We trained an XGBoost Classifier on the words of the anonymized conversations, using repeated cross-validation and Bayesian optimization for hyperparameter search. The best model was able to achieve an AUROC score of 0.68 (p < 0.01) on the previously unseen 3942 newest consultations. A shapely-based explainability approach revealed that words indicating younger age or female gender and terms related to self-harm and suicidal thoughts were associated with a higher chance of recontacting. We conclude that NLP-based predictions of recurrent contact are a promising path toward personalized care at chat hotlines.
Publisher
npj Digital Medicine
Published On
May 18, 2024
Authors
Silvan Hornstein, Jonas Scharfenberger, Ulrike Lueken, Richard Wundrack, Kevin Hilbert
Tags
Natural Language Processing
NLP
crisis service
youth care
predictive analysis
self-harm
mental health
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