A Python package to differentiate between tuberculous and non-tuberculous pleural effusion using ChatGPT-4 with biochemistry and blood cell analysis data.
Project description
This Python package is designed to quickly differentiate between tuberculous pleural effusion and non-tuberculous pleural effusion by leveraging the predictive power of ChatGPT-4. By inputting a set of key variables related to the patient's biochemistry and blood cell analysis, the model can provide an intelligent prediction. The required variables include:
- Pleural fluid biochemistry: ADA, total protein, albumin
- Blood cell analysis: lymphocyte count, neutrophil percentage, monocyte percentage, neutrophil count
- Patient's age
By using these inputs, the model makes a prediction through ChatGPT-4, assisting clinicians in promptly identifying the type of pleural effusion, thereby supporting more informed diagnostic and treatment decisions.
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