Question-Answering Model for Schizophrenia Symptoms and Their Impact on Daily Life using Mental Health Forums Data
This work addresses the need for accurate medical QA systems in mental health, specifically for schizophrenia, though it is incremental as it applies existing models to a new dataset.
The paper tackled the problem of building a question-answering model for analyzing schizophrenia symptoms and their impact on daily life by creating a low-bias dataset from mental health forums and fine-tuning models like BioBERT, achieving an F1 score of 0.885 and outperforming the state-of-the-art in the mental disorders domain.
In recent years, there is strong emphasis on mining medical data using machine learning techniques. A common problem is to obtain a noiseless set of textual documents, with a relevant content for the research question, and developing a Question Answering (QA) model for a specific medical field. The purpose of this paper is to present a new methodology for building a medical dataset and obtain a QA model for analysis of symptoms and impact on daily life for a specific disease domain. The ``Mental Health'' forum was used, a forum dedicated to people suffering from schizophrenia and different mental disorders. Relevant posts of active users, who regularly participate, were extrapolated providing a new method of obtaining low-bias content and without privacy issues. Furthermore, it is shown how to pre-process the dataset to convert it into a QA dataset. The Bidirectional Encoder Representations from Transformers (BERT), DistilBERT, RoBERTa, and BioBERT models were fine-tuned and evaluated via F1-Score, Exact Match, Precision and Recall. Accurate empirical experiments demonstrated the effectiveness of the proposed method for obtaining an accurate dataset for QA model implementation. By fine-tuning the BioBERT QA model, we achieved an F1 score of 0.885, showing a considerable improvement and outperforming the state-of-the-art model for mental disorders domain.