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

mihalca
/

SymptoAI