This model is a fine-tuned version of cambridgeltl/SapBERT-from-PubMedBERT-fulltext on the DDXPlus dataset (50,000 samples) for medical diagnosis tasks.
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
The model was trained on DDXPlus dataset (50,000 samples) containing:
Patient cases with comprehensive medical information
Differential diagnosis annotations
49 distinct medical conditions
Evidence-based symptom-condition relationships
Performance
Final Metrics
Test Precision: 0.8159
Test Recall: 0.7948
Test F1 Score: 0.7420
Training Evolution
Best Validation Loss: 1.4600
Stopped at Epoch 4 (Early stopping triggered)
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Intended Use
This model is designed for:
Medical diagnosis support
Symptom analysis
Disease classification
Differential diagnosis generation
Out-of-Scope Use
The model should NOT be used for:
Direct medical diagnosis without professional oversight
Critical healthcare decisions without human validation
Clinical applications without proper testing and validation
Training Details
Training Procedure
Optimizer: AdamW with weight decay (0.01)
Learning Rate: 1e-5
Loss Function: Combined loss (0.8 × Focal Loss + 0.2 × KL Divergence)
Batch Size: 32
Gradient Clipping: 1.0
Early Stopping: Patience of 3 epochs
Training Strategy: Cross-validation with 5 folds
Model Architecture
Base Model: cambridgeltl/SapBERT-from-PubMedBERT-fulltext
Hidden Size: 768
Attention Heads: 12
Dropout Rate: 0.5
Added classification layers for diagnostic tasks
Layer normalization and dropout for regularization
Example Usage
from transformers import AutoTokenizer, AutoModel
# Load model and tokenizer
model_name = "acharya-jyu/sapbert-pubmedbert-ddxplus-10k"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)
# Example input structure
input_data = {
'age': 45, # Patient age
'sex': 'M', # Patient sex: 'M' or 'F'
'initial_evidence': 'E_91', # Initial evidence code (e.g., E_91 for fever)
'evidences': [
'E_91', # Fever
'E_77', # Cough
'E_89' # Fatigue
]
}
# Process demographic data and evidence codes
outputs = model(**input_data)
# Outputs will include:
# - Main diagnosis prediction
# - Differential diagnosis probabilities
# - Confidence scores
Note: Evidence codes (E_XX) correspond to specific symptoms and conditions defined in the release_evidences.json file. The model expects these standardized codes rather than raw text input.
Citation
bibtex
1 @misc{acharya2024sapbert,
2 title={SapBERT-PubMedBERT Fine-tuned on DDXPlus Dataset},
3 author={Acharya, Aashish},
4 year={2024},
5 publisher={Hugging Face Model Hub}
6}
7
