genandlam/multi-modal-depression-detection
Official codebase for "Context Aware Deep Learning for Multi Modal Depression Detection" [ICASSP 2019, Oral]
This project offers an automated way to screen for depression from clinical interviews. By analyzing the audio and text from interview recordings, it can help mental health professionals or researchers identify signs of depression. It takes recorded interview data (audio and transcripts) and provides a prediction or score indicating the likelihood of depression, streamlining the initial assessment process for those working in mental health.
No commits in the last 6 months.
Use this if you need an automated, data-driven tool to help detect depression from spoken clinical interviews.
Not ideal if you're looking for a diagnostic tool or a solution that uses visual cues (like video) from interviews.
Stars
11
Forks
—
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Dec 26, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/genandlam/multi-modal-depression-detection"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
galihru/MentalHealth
A comprehensive mental health monitoring application using modern web technologies.
giastahmad/DSS-Anxiety-Evaluator
Web app for anxiety assessment using DASS-42 and an ML model to predict key contributing factors.
indranil143/Mental-Health-Sentiment-Analysis-using-Deep-Learning
A deep learning project using fine-tuned RoBERTa to classify mental health sentiments from text,...
zeinhasan/Suicidal-Detection-Sentiment-Analysis
Suicidal Ideation Detection Using Natural Languange Processing and Machine Learning - Deep...
mistr3ated/AI-Psychometrics-Nigel
This repository contains code from my experiments with AI for psychological measurement. Read...