BERT Based Model for Robust Mental Health Analysis in Clinical Informatics

In the domain of healthcare informatics, precise analysis of patient language is essential for assessing mental health. Traditional methodologies often necessitate intricate feature engineering and are limited by computational constraints. This paper presents a BERT (Bidirectional Encoder Representations from Transformers) based model that transcends these limitations, providing a nuanced sentiment classification of patient speech. Employing the expansive Kaggle Mental Health Corpus, our model adeptly differentiates between nuanced linguistic indicators of mental health conditions. Comparative results illustrate the BERT model's superior performance, achieving a notable accuracy and F1-score of 0.92, a significant improvement over traditional machine learning counterparts. These findings underscore the model's potential as both a clinical diagnostic aid and a predictor of mental health trends, evidencing the transformative impact of natural language processing in healthcare.