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acharya-jyu/sapbert-pubmedbert-ddxplus-10k

sourceHugging Facemitupdated 2y agoView on Hugging Face
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Model Details

Model Description

This model is a fine-tuned version of cambridgeltl/SapBERT-from-PubMedBERT-fulltext on the DDXPlus dataset (10,000 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.

  • —Developed by: Aashish Acharya
  • —Model type: sapBERT-BioMedBERT
  • —Language(s): English
  • —License: MIT
  • —Finetuned from model: cambridgeltl/SapBERT-from-PubMedBERT-fulltext

Model Sources

Training Dataset

The model was trained on DDXPlus dataset (10,000 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: 0.9619
  • —Test Recall: 0.9610
  • —Test F1 Score: 0.9592

Training Evolution

  • —Best Validation F1: 0.9728 (Epoch 4)
  • —Final Validation Loss: 0.6352

<img src="https://cdn-uploads.huggingface.co/production/uploads/662757230601587f0be9781b/7GK4e9jy4vKz9gSXU-dbh.png" width="400" alt="image"> <img src="https://cdn-uploads.huggingface.co/production/uploads/662757230601587f0be9781b/5b_O5oX0BISljP1kwdtTN.png" width="400" alt="image">

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

<pre> from transformers import AutoTokenizer, AutoModel

Load model and tokenizer

modelname = "acharya-jyu/sapbert-pubmedbert-ddxplus-10k" tokenizer = AutoTokenizer.frompretrained(modelname) model = AutoModel.frompretrained(model_name)

Example input structure

inputdata = { 'age': 45, # Patient age 'sex': 'M', # Patient sex: 'M' or 'F' 'initialevidence': 'E91', # Initial evidence code (e.g., E91 for fever) 'evidences': [ 'E91', # Fever 'E77', # 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

</pre> <b>Note: Evidence codes (EXX) correspond to specific symptoms and conditions defined in the releaseevidences.json file. The model expects these standardized codes rather than raw text input.</b>

Citation

bibtex
  @misc{acharya2024sapbert,
  title={SapBERT-PubMedBERT Fine-tuned on DDXPlus Dataset},
  author={Acharya, Aashish},
  year={2024},
  publisher={Hugging Face Model Hub}
}

Model Card Contact

Aashish Acharya