Exploring Complex Mental Health Symptoms via Classifying Social Media Data with Explainable LLMs
This work addresses mental health analysis for researchers and clinicians by providing an incremental method to predict and explain future conditions from social media data.
The authors tackled the problem of analyzing complex mental health symptoms by developing a pipeline that uses explainable LLMs to classify social media text data, predict future mental health concerns, and provide explanations for these predictions, reporting initial results on predicting ADHD concerns from anxiety disorder reports.
We propose a pipeline for gaining insights into complex diseases by training LLMs on challenging social media text data classification tasks, obtaining explanations for the classification outputs, and performing qualitative and quantitative analysis on the explanations. We report initial results on predicting, explaining, and systematizing the explanations of predicted reports on mental health concerns in people reporting Lyme disease concerns. We report initial results on predicting future ADHD concerns for people reporting anxiety disorder concerns, and demonstrate preliminary results on visualizing the explanations for predicting that a person with anxiety concerns will in the future have ADHD concerns.