ArticleFrontiers in psychiatry2023
A machine learning approach to identifying suicide risk among text-based crisis counseling encounters.
Article in Frontiers in psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 17 citations in OpenAlex.
- Diagnostic accuracy of machine learning approaches for suicide‑related outcomes: a meta‑analysis.Annals of general psychiatry · 2026Article
- Predicting imminent suicide risk in a crisis hotline chat using machine learning.Scientific reports · 2025Article
- Changes in Mental State for Help-Seekers of Lifeline Australia's Online Chat Service: Lexical Analysis Approach.JMIR formative research · 2025Article
- Predicting Satisfaction With Chat-Counseling at a 24/7 Chat Hotline for the Youth: Natural Language Processing Study.JMIR AI · 2025Article
- Association of machine-learning-rated supportive counseling skills with psychotherapy outcome.Journal of consulting and clinical psychology · 2025Article
- An Explainable Artificial Intelligence Text Classifier for Suicidality Prediction in Youth Crisis Text Line Users: Development and Validation Study.JMIR public health and surveillance · 2025Article
- Applications of Large Language Models in the Field of Suicide Prevention: Scoping Review.Journal of medical Internet research · 2025Article
- Efficacy of ChatGPT in Cantonese Sentiment Analysis: Comparative Study.Journal of medical Internet research · 2024Article
- Logic-driven Indirect Supervision: An Application to Crisis Counseling.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2023Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
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. 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. 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. 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.