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MedMatch

  • In the ever-evolving landscape of healthcare, the integration of advanced technologies has become imperative to enhance accessibility and efficiency.
  • Our proposed methodology addresses this need through a two-fold approach, combining a fine-tuned LLaMA2 model using the QLoRa technique with a comprehensive database of doctors.
  • By leveraging publicly available datasets, encompassing biomedical, patient-physician dialogues, and mental health conversations, we aim to empower users with a conversational assistant capable of delivering real-time and accurate health assistance.

How to run

  • Set up the django environment
  • navigate to the directory containing the manage.py file
  • run the command "python manage.py runserver"
  • for model inference, run the notebook inference_enhancement.ipynb

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An LLM assisted pre-diagnostic tool

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