🧠 Question-Answering Model for Schizophrenia Symptoms

A deep dive into the world of Schizophrenia through the lens of Machine Learning.

This repository is dedicated to understanding Schizophrenia symptoms and their impact on daily life, leveraging vast data from mental health forums and advanced ML models.

Arxiv Paper:

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

BibTeX formatted citation:

@misc{internò2023questionanswering,
      title={Question-Answering Model for Schizophrenia Symptoms and Their Impact on Daily Life using Mental Health Forums Data}, 
      author={Christian Internò and Eloisa Ambrosini},
      year={2023},
      eprint={2310.00448},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

BibTeX formatted citation:

@misc{internò2024automated,
      title={Automated Federated Learning via Informed Pruning}, 
      author={Christian Internò and Elena Raponi and Niki van Stein and Thomas Bäck and Markus Olhofer and Yaochu Jin and Barbara Hammer},
      year={2024},
      eprint={2405.10271},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

📖 Overview

In recent years, the emphasis on mining medical data using machine learning techniques has grown exponentially. This project presents a new methodology for building a medical dataset and designing a QA model to analyze symptoms and their daily life impact for a specific disease domain. The main focus is on Schizophrenia, a mental disorder that affects millions worldwide.

📊 Dataset

The dataset originates from the "Mental Health" forum, dedicated to individuals suffering from schizophrenia and other mental disorders. The corpus consists of:

• 415,602 posts
• Respective IDs
• Dates posted by different users

🛠 Tools & Techniques

• Fine-tuned Models: BERT, DistilBERT, RoBERTa, and BioBERT.
• Haystack: A framework for end-to-end QA systems.
• Fine-tuning: The models are rapidly fine-tuned for specific tasks with relatively fewer labels.

📈 Results

By fine-tuning the BioBERT QA model, we achieved:

• F1 Score: 0.885
• Notable improvement over state-of-the-art models in the mental disorders domain.

🤝 Contribution

We welcome contributions! Whether it's improving the model, enhancing the dataset, or providing feedback, feel free to make a pull request or open an issue.

📜 Citation

@misc{internò2023questionanswering,
      title={Question-Answering Model for Schizophrenia Symptoms and Their Impact on Daily Life using Mental Health Forums Data}, 
      author={Christian Internò and Eloisa Ambrosini},
      year={2023},
      eprint={2310.00448},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}