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We are working on automated symptom detection technique from Natural language using one of the popular LLM, BERT.

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ahmedfahad04/AI-Symptom-to-Disease-Mapping

 
 

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How to run Backend model

  1. Run backend/models/identify-symptoms-using-huggingface-transformer.ipynb, it'll create a fine-tuned-model folder that will contain the model details

  2. Then simply run the backend uvicorn main:app --reload and navigate to Sweeger UI

  3. Test the get_symptoms api to extract symptoms from natural language.

Resources

Doctors Image: https://drive.google.com/drive/folders/19drVDiuWjgQXLOfSosXugfibPXGQ0yVH?usp=drive_link

TODO:

Currently we are using Huggingface Transformer to extract symptoms from text which is supervised learning method.

Next if possible we can try to do some semi-supervised learning for achieveing better accuracy

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We are working on automated symptom detection technique from Natural language using one of the popular LLM, BERT.

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