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--- |
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license: mit |
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base_model: |
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- microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext |
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--- |
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## Clinical Decision Support Model 🩺📊 |
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Model Overview |
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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. |
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## Model Use Case |
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The primary use case for this model is Clinical Decision Support in the domain of Cardiovascular Health. It helps healthcare professionals by: |
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Evaluating patient health data. |
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Predicting clinical recommendations. |
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Reducing decision-making time and improving the quality of care. |
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## Inputs |
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The model expects input in the following format: |
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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> |
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## Output |
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The model predicts a recommendation from one of the following categories: |
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Maintain healthy lifestyle |
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Immediate cardiologist consultation |
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Start statins, monitor regularly |
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Lifestyle changes, monitor |
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No immediate action |
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Increase statins, lifestyle changes |
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Start ACE inhibitors, monitor |
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## Example Input |
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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 |
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## Example Output |
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Recommendation: Start ACE inhibitors, monitor |
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## Model Training |
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Base Model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext |
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Dataset: A synthetic dataset of 5000 patient examples with details like age, gender, symptoms, risk score, etc. |
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Fine-tuning Framework: Hugging Face Transformers. |
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## How to Use |
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from transformers import pipeline |
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#Load the model |
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model_path = "your_username/clinical_decision_support" |
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classifier = pipeline("text-classification", model=model_path) |
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#Example input |
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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" |
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#Get prediction |
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prediction = classifier(input_text) |
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print(prediction) |
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## Limitations |
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The model is based on synthetic data and may not fully generalize to real-world scenarios. |
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Recommendations are not a substitute for clinical expertise and should always be validated by a healthcare professional. |
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## Future Improvements |
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Train on a larger, real-world dataset to enhance model performance. |
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Expand the scope to include recommendations for other medical domains. |
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## Acknowledgments |
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Model fine-tuned using the Hugging Face Transformers library. |
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Base model provided by Microsoft: BiomedBERT. |
