Question-Answering Model for Schizophrenia Symptoms and Their Impact on Daily Life using Mental Health Forums Data

TL;DR

This paper introduces a novel methodology for creating a low-bias, privacy-preserving mental health forum dataset and develops a QA model for schizophrenia symptoms and daily life impact, achieving high accuracy with BioBERT.

Contribution

It presents a new approach to dataset collection from mental health forums and fine-tunes BERT-based models for effective medical question answering in schizophrenia.

Findings

BioBERT-based QA model achieved 0.885 F1 score.

Proposed dataset creation method reduces bias and privacy concerns.

Fine-tuned models outperform existing state-of-the-art in mental health QA.

Abstract

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.