---
language:
  - en
license: apache-2.0
base_model: emilyalsentzer/Bio_ClinicalBERT
tags:
  - medical
  - clinical
  - ssi
  - classification
  - surveillance
  - multi-label
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: SSIBERT-multiclass
    results:
      - task:
          type: text-classification
          name: Multi-Label SSI Detection
        dataset:
          name: Synthetic UK NHS Clinical Notes (Multi-Label)
          type: synthetic
          split: test
        metrics:
          - name: F1 (Micro)
            type: f1
            value: 1.0
---

# Model Card for Ch3DS/SSIBERT-multiclass

## Model Details

### Model Description

This model is a fine-tuned version of [Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) designed for **multi-label classification** of postoperative clinical notes. Unlike the binary SSI model, this model identifies specific clinical indicators of infection:

1.  **Purulence**: Presence of pus or purulent discharge.
2.  **Redness**: Erythema, spreading redness, or inflammation.
3.  **Fever**: Pyrexia, rigors, or elevated temperature.
4.  **Antibiotics**: Prescription of antibiotics (treatment or prophylaxis).
5.  **SSI**: Overall determination of Surgical Site Infection.

It is tailored to **UK NHS terminology**.

- **Developed by:** Daryn Sutton
- **Model type:** Multi-Label Text Classification (BERT)
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model:** [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT)
- **Repository:** [https://huggingface.co/Ch3DS/SSIBERT-multiclass](https://huggingface.co/Ch3DS/SSIBERT-multiclass)

### Uses

#### Direct Use

This model extracts structured data from unstructured clinical notes, allowing for more granular surveillance.

- **Input**: Clinical note text.
- **Output**: Probabilities for `[Purulence, Redness, Fever, Antibiotics, SSI]`.

#### Out-of-Scope Use

- **Diagnosis**: This is a surveillance tool, not a diagnostic device.
- **Non-UK Contexts**: May perform poorly on non-NHS terminology.

## How to Get Started with the Model

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "Ch3DS/SSIBERT-multiclass"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "Day 3 post THR. Wound oozing pus. Patient pyrexial. Plan: Start Flucloxacillin."
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits
    probs = torch.sigmoid(logits)

labels = ["Purulence", "Redness", "Fever", "Antibiotics", "SSI"]
for i, label in enumerate(labels):
    print(f"{label}: {probs[0][i]:.2%}")
```

## Training Details

### Training Data

- **Source**: 5 million synthetic clinical notes.
- **Methodology**: Generated using templates based on UK NHS terminology and the PRAISE network's surveillance definitions.
- **Labels**: Multi-hot encoded.

### Training Procedure

- **Epochs**: 3
- **Batch Size**: 64
- **Hardware**: NVIDIA GeForce RTX 5070 Ti

## Evaluation

Evaluated on a held-out test set of synthetic data. Achieved near-perfect performance on the synthetic distribution.

## Model Card Contact

**Daryn Sutton**