aceEnsemble of Domain-Specific Natural Language Processing and Large Language Models for Detecting Suicidal Ideation and Mental Health Conditions in Social Media Text: Development and Evaluation Study
Background Mental illness contributes substantially to global disability, and public adoption of AI for mental health support is accelerating without commensurate safety evaluation. General-purpose large language models can fail to recognize naturalistic text expressing suicidal ideation, with immediate clinical consequences. Domain-specific natural language processing methods offer a contrasting approach, but ensembles integrating the two have not been formally characterized. Objective We aimed to (1) benchmark…
Combining a domain-specific classifier with a fine-tuned language model at a 60/40 weighting reached about 93.9% test accuracy, above either model used alone on this Reddit and Twitter corpus.
Labels were community-inferred rather than clinical diagnoses and only one corpus was analyzed, so the results are hypotheses needing external validation on clinically characterized data before use in decision support.
Evidence
- Peer-reviewedJMIR Mental Health2026-10-08
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Truvace Impact Record TRV-2026-1621, v1: “Ensemble of Domain-Specific Natural Language Processing and Large Language Models for Detecting Suicidal Ideation and Mental Health Conditions in Social Media Text: Development and Evaluation Study.” Truvace, 2026-10-16. /record/TRV-2026-1621 (accessed at citation time). sha256 60883f352abaac84…
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