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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Abstract

This study investigates the use of Natural Language Processing (NLP) to predict recurrent chat contact in a 24/7 German crisis service for youth. Using approximately 800,000 messages from 19,000 users, an XGBoost classifier achieved an AUROC of 0.68 in predicting recontact within six months. Explainability analysis revealed that younger age, female gender, and mentions of self-harm and suicidal thoughts were associated with a higher likelihood of recontact. The findings suggest NLP's potential for personalized care in chat-based hotlines.

Publisher

npj Digital Medicine

Published On

May 18, 2024

Authors

Silvan Hornstein, Jonas Scharfenberger, Ulrike Lueken, Richard Wundrack, Kevin Hilbert

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