mihalca/SymptoAI

Model Card for Disease Symptom Recognition Model

Model Details

Model Description

This model is a fine-tuned BERT-based architecture designed to recognize and classify symptoms of diseases. It has been trained on a curated dataset containing labeled descriptions of various disease symptoms and converted to ONNX for efficient inference.

• Developed by: Mihi
• Funded by: Self-funded
• Shared by: Mihi
• Model type: NLP Classification
• Language(s): English
• License: MIT
• Finetuned from model: BERT base uncased

Model Sources

• Repository: [GitHub Repository Link] (replace with actual link)
• Demo: [Demo Link] (replace with actual link)

Uses

Direct Use

This model can be used directly for symptom classification in applications like:

• Symptom checkers for healthcare applications
• Medical chatbots for triage
• Data analysis in public health studies

Downstream Use

The model may be fine-tuned further or integrated into larger healthcare solutions involving disease diagnosis or prediction.

Out-of-Scope Use

• The model is not designed for diagnosing diseases.
• It should not be used as a substitute for professional medical advice.

Bias, Risks, and Limitations

• The model's performance is limited to the scope and quality of the training data. It may not perform well on symptoms outside its training domain.
• Potential biases in the training data can lead to inaccurate predictions for underrepresented diseases or symptoms.

Recommendations

• Ensure proper pre-screening of the output by medical professionals before clinical application.
• Perform further fine-tuning or retraining if applied in domains outside the original dataset.

How to Get Started with the Model

Install the required dependencies:

pip install transformers onnxruntime