RESEARCH · RESEARCH · #997
MIT team develops lexicon-based ML tool to predict imminent suicide risk from Crisis Text Line conversations
Researchers at MIT’s McGovern Institute (Daniel Low and Satra Ghosh) built a curated suicide‑risk lexicon linked to 49 risk factors and trained a machine‑learning model on ~16,000 de‑identified Crisis Text Line conversations to classify non‑suicidal, suicidal ideation, and imminent‑risk cases; the work, published in the Journal of Psychopathology and Clinical Science, found mentions of lethal means and substance use were strong predictors of imminent risk. The lexicon contains about 60 words/phrases per risk factor and was curated with clinician input.
KEY POINTS
- Researchers at MIT’s McGovern Institute (Daniel Low and Satra Ghosh) built a curated suicide‑risk lexicon linked to 49 risk factors and trained a machine‑learning model on ~16,000 de‑identified Crisis Text Line conversations to classify non‑suicidal, suicidal ideation, and imminent‑risk cases; the work, published in the Journal of Psychopathology and Clinical Science, found mentions of lethal means and substance use were strong predictors of imminent risk.
- The lexicon contains about 60 words/phrases per risk factor and was curated with clinician input.
- If validated further, a transparent lexicon‑based tool that highlights which risk factors predict imminent risk could help counselors and clinicians prioritize urgent cases in text‑based crisis support and clinical settings.
WHY IT MATTERS
If validated further, a transparent lexicon‑based tool that highlights which risk factors predict imminent risk could help counselors and clinicians prioritize urgent cases in text‑based crisis support and clinical settings.