CASE: Curricular Data Pre-training for Building Generative and Discriminative Assistive Psychology Expert Models

The limited availability of psychologists necessitates efficient identification of individuals requiring urgent mental healthcare. This study explores the use of Natural Language Processing (NLP) pipelines to analyze text data from online mental health forums used for consultations. By analyzing forum posts, these pipelines can flag users who may require immediate professional attention. A crucial challenge in this domain is data privacy and scarcity. To address this, we propose utilizing readily available curricular texts used in institutes specializing in mental health for pre-training the NLP pipelines. This helps us mimic the training process of a psychologist. Our work presents two models: a discriminative BERT-based model called CASE-BERT that flags potential mental health disorders based on forum text, and a generative model called CASE-Gemma that extracts key features for a preliminary diagnosis. CASE-BERT demonstrates superior performance compared to existing methods, achieving an f1 score of 0.91 for Depression and 0.88 for Anxiety, two of the most commonly reported mental health disorders. CASE-Gemma can achieve a BERT Score of 0.849 on generating diagnoses based on forum text. The effectiveness of CASE-Gemma is evaluated through both human evaluation and qualitative methods, with the collaboration of clinical psychologists who provide us with a set of annotated data for fine-tuning and evaluation. Our code is available at https://github.com/sarthakharne/CASE

Introducing SSBD+ Dataset with a Convolutional Pipeline for detecting Self-Stimulatory Behaviours in Children using raw videos

Conventionally, evaluation for the diagnosis of Autism spectrum disorder is done by a trained specialist through questionnaire-based formal assessments and by observation of behavioral cues under various settings to capture the early warning signs of autism. These evaluation techniques are highly subjective and their accuracy relies on the experience of the specialist. In this regard, machine learning-based methods for automated capturing of early signs of autism from the recorded videos of the children is a promising alternative. In this paper, the authors propose a novel pipelined deep learning architecture to detect certain self-stimulatory behaviors that help in the diagnosis of autism spectrum disorder (ASD). The authors also supplement their tool with an augmented version of the Self Stimulatory Behavior Dataset (SSBD) and also propose a new label in SSBD Action detection: no-class. The deep learning model with the new dataset is made freely available for easy adoption to the researchers and developers community. An overall accuracy of around 81% was achieved from the proposed pipeline model that is targeted for real-time and hands-free automated diagnosis. All of the source code, data, licenses of use, and other relevant material is made freely available in https://github.com/sarl-iiitb/