ayaanzhaque/SDCNL

Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction (ICANN 2021)

35
/ 100
Emerging

This project helps mental health professionals and researchers analyze online text to distinguish between general depression and more severe suicidal tendencies. You input social media posts or similar free-form text, and it outputs a classification indicating whether the text suggests depression or suicidal ideation. This tool is designed for mental health practitioners, clinical researchers, or public health analysts who work with online behavioral data.

No commits in the last 6 months.

Use this if you need an automated way to classify text for early detection of suicidal ideation within a depressed population, especially when dealing with noisy, self-reported online data.

Not ideal if you require classifications based on formal clinical diagnoses or structured medical records, as this tool is specifically designed for informal online text.

mental-health-screening suicide-prevention depression-analysis social-media-monitoring public-health-research
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 8 / 25
Maturity 8 / 25
Community 19 / 25

How are scores calculated?

Stars

66

Forks

19

Language

Python

License

Last pushed

Aug 23, 2021

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/ayaanzhaque/SDCNL"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.