quadranttechnologies/Clinical_Decision_Support

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: , Gender: <Male/Female>, Weight: , Smoking Status: <Never/Former/Current>, Diabetes: <0/1>, Hypertension: <0/1>, Cholesterol: , Heart Disease History: <0/1>, Symptoms: , Risk Score:

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.