First published 10/2/2026, 12:09:15 AM · Last updated 10/2/2026, 12:09:15 AM

Thai Depression Detection with LLM-Assisted Annotation and PHQ-9 Multi-Task Classification

IEEE proceedings article published 2026-08-01 reporting research on using large language model–assisted annotation and PHQ-9 multi-task classification for depression detection in a Thai context. The source record provided is limited to bibliographic details and the paper topic.

Illustrative image: This is a photo of a monument in Thailand identified by the ID — Preecha.MJ/Wikimedia Commons, CC BY-SA 4.0

Categories: public-health, technology

Generated scores

Scores are based on the cited reporting and use a 1–10 scale. Read the methodology.

Confidence: 5/10How certain the assessment is based on the clarity and evidence in the cited reporting.
Geographic reach: 4/10How widely the event reaches, from a local area (1) to worldwide impact (10).
Global importance: 2/10The event's broader significance, from limited importance (1) to major global importance (10).
Impact magnitude: 2/10The scale or severity of the real-world impact, from minor (1) to extreme (10).
Positivity: 7/10How harmful or beneficial the event is, from strongly harmful (1) to strongly positive (10).
Urgency: 1/10How time-sensitive the event is, from low urgency (1) to immediate attention needed (10).

Why it matters

The paper documents a technical approach to automated depression detection that is relevant to health research and clinical screening tool development.

Location

Sources

Thai Depression Detection with LLM-Assisted Annotation and PHQ-9 Multi-Task Classification

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