This repository provides code and data for automatic depression detection using a GRU/BiLSTM-based model. It includes an emotional audio-textual corpus designed to support the diagnosis of psychological distress conditions such as anxiety, depression, and post-traumatic stress disorder.
The ICASSP2022-Depression project presents a comprehensive approach to automatic depression detection using deep learning techniques. The repository includes a GRU/BiLSTM-based model and an emotional audio-textual corpus, making it a valuable resource for researchers working on mental health and natural language processing.
APA PsycInfo is the premier abstracting and indexing database covering the behavioral and social sciences. It provides over 5,000,000 peer-reviewed records, 144 million cited references, and spans 600 years of content. The database is updated twice-weekly and includes research in 30 languages from 50 countries.
The IC-AnnoMI repository contains source code and a synthetic dataset generated through in-context zero-shot LLM prompting for mental health and therapeutic counselling. IC-AnnoMI is a project that generates contextual MI dialogues using large language models (LLMs). The project contains source code and a synthetic dataset generated through zero-shot prompts, aiming to address the data scarcity and inherent bias problems in mental health and therapeutic consultation.
This paper discusses Helply - a synthesized ML training dataset focused on psychology and therapy, created by Alex Scott and published by NamelessAI. The dataset developed by Alex Scott is a comprehensive collection of synthesized data designed to train LLMs in understanding psychological and therapeutic contexts. This dataset aims to simulate real-world interactions between therapists and patients, enabling ML models to learn from a wide range of scenarios and therapeutic techniques.