Question-Answering Model for Schizophrenia Symptoms and Their Impact on
  Daily Life using Mental Health Forums Data
Christian Internò Eloisa Ambrosini
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.