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.

本文提出了一种新的方法，通过对关注精神分裂症和不同心理障碍的人群在 “心理健康” 论坛上发表的帖子进行概括，获得了一个低偏差、无隐私问题的用于问答模型实施的精确数据集，并利用各种模型的细调评估，展示了该方法获取准确数据集的有效性，BioBERT 模型的细调获得了 0.885 的 F1 得分，在心理障碍领域表现优于现有模型。