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README.md

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-license: mit
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+---
+license: mit
+---
+## Clinical Decision Support Model 🩺📊
+Model Overview
+This Clinical Decision Support Model is designed to assist healthcare providers in making data-driven decisions based on patient information. The model leverages advanced natural language processing (NLP) capabilities using the BiomedBERT architecture, fine-tuned specifically on a synthetic dataset of heart disease-related patient data. It provides personalized recommendations for patients based on their clinical profile.
+
+## Model Use Case
+The primary use case for this model is Clinical Decision Support in the domain of Cardiovascular Health. It helps healthcare professionals by:
+
+Evaluating patient health data.
+Predicting clinical recommendations.
+Reducing decision-making time and improving the quality of care.
+
+## Inputs
+The model expects input in the following format:
+
+Age: <int>, Gender: <Male/Female>, Weight: <int>, Smoking Status: <Never/Former/Current>, Diabetes: <0/1>, Hypertension: <0/1>, Cholesterol: <int>, Heart Disease History: <0/1>, Symptoms: <string>, Risk Score: <float>
+
+## Output
+The model predicts a recommendation from one of the following categories:
+
+Maintain healthy lifestyle
+Immediate cardiologist consultation
+Start statins, monitor regularly
+Lifestyle changes, monitor
+No immediate action
+Increase statins, lifestyle changes
+Start ACE inhibitors, monitor
+
+## Example Input
+Age: 70, Gender: Female, Weight: 66, Smoking Status: Never, Diabetes: 0, Hypertension: 1, Cholesterol: 258, Heart Disease History: 1, Symptoms: Chest pain, Risk Score: 6.1
+
+## Example Output
+Recommendation: Start ACE inhibitors, monitor
+
+## Model Training
+
+Base Model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
+Dataset: A synthetic dataset of 5000 patient examples with details like age, gender, symptoms, risk score, etc.
+Fine-tuning Framework: Hugging Face Transformers.
+
+## How to Use
+
+from transformers import pipeline
+
+#Load the model
+model_path = "your_username/clinical_decision_support"
+classifier = pipeline("text-classification", model=model_path)
+
+#Example input
+input_text = "Age: 70, Gender: Female, Weight: 66, Smoking Status: Never, Diabetes: 0, Hypertension: 1, Cholesterol: 258, Heart Disease History: 1, Symptoms: Chest pain, Risk Score: 6.1"
+
+#Get prediction
+prediction = classifier(input_text)
+print(prediction)
+
+## Limitations
+
+The model is based on synthetic data and may not fully generalize to real-world scenarios.
+Recommendations are not a substitute for clinical expertise and should always be validated by a healthcare professional.
+
+## Future Improvements
+
+Train on a larger, real-world dataset to enhance model performance.
+Expand the scope to include recommendations for other medical domains.
+
+## Acknowledgments
+
+Model fine-tuned using the Hugging Face Transformers library.
+Base model provided by Microsoft: BiomedBERT.
+