This model is a fine-tuned version of Bio_ClinicalBERT designed for the surveillance of Surgical Site Infections (SSI) in postoperative clinical notes. It is specifically tailored to UK NHS terminology, covering specialties such as Orthopaedics, General Surgery (GI), and Obstetrics (C-sections).

Developed by: Daryn Sutton
Model type: Text Classification (BERT)
Language(s) (NLP): English
License: Apache 2.0
Finetuned from model: emilyalsentzer/Bio_ClinicalBERT
Repository: https://huggingface.co/Ch3DS/clinicalSSIBERT

Uses

Direct Use

This model is intended for use in clinical natural language processing (NLP) pipelines to automatically flag postoperative notes that indicate a potential Surgical Site Infection. It classifies notes into:

0 (Routine): Normal healing, no signs of infection.
1 (Infection): Signs of SSI (e.g., purulent discharge, erythema, antibiotic escalation).

It is particularly effective for notes containing UK-specific medical abbreviations and terminology (e.g., "Lap. Chole.", "THR", "Co-amoxiclav", "SHO review").

Out-of-Scope Use

Diagnosis: This model is a surveillance tool and should not be used to make clinical diagnoses without human verification.
Non-UK Contexts: Performance may vary on clinical notes from other healthcare systems with different terminology or documentation styles.

Bias, Risks, and Limitations

Synthetic Data: The model was trained on a large synthetic dataset. While designed to be realistic, it may not capture the full "messiness" or ambiguity of real-world clinical data.
False Negatives: There is a risk of missing subtle infections that do not use standard keywords.
Bias: The synthetic data generation process may have introduced biases based on the templates used.

Recommendations

Users should validate the model on their own local clinical data before deploying it for active surveillance. It is recommended to use this model as a "first pass" filter to prioritize cases for manual review by Infection Prevention and Control (IPC) teams.

How to Get Started with the Model

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "Ch3DS/clinicalSSIBERT"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "Day 5 post THR. Wound red and oozing pus. Patient pyrexial. Plan: Start Flucloxacillin."
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits
    predicted_class_id = logits.argmax().item()

labels = ["Routine", "Infection"]
print(f"Prediction: {labels[predicted_class_id]}")

Training Details

Training Data

The model was trained on 5 million synthetic clinical notes generated to mimic UK NHS postoperative records. The data covers:

Procedures: Total Hip/Knee Replacement, C-Section, Cholecystectomy, Hernia Repair, etc.
Terminology: UK-specific staff titles (Reg, SHO, FY1), antibiotics (Co-amoxiclav, Teicoplanin), and wound descriptions.
Balance: Approximately 5% infection rate.

Training Procedure

Training Hyperparameters

Epochs: 3
Batch Size: 64 (per device) with Gradient Accumulation of 4
Learning Rate: 2e-5
Precision: Mixed Precision (FP16)
Optimizer: AdamW

Hardware

GPU: NVIDIA GeForce RTX 5070 Ti

Evaluation

Testing Data, Factors & Metrics

The model was evaluated on a held-out test set of 100,000 synthetic records.

Results

Metric	Value
Accuracy	100%
Precision	1.0
Recall	1.0
F1-Score	1.0

Note: The perfect scores reflect the synthetic nature of the test data, which follows the same distribution as the training data. Real-world performance is expected to be lower and requires further validation.

Environmental Impact

Hardware Type: NVIDIA GeForce RTX 5070 Ti
Hours used: ~2 hours
Carbon Emitted: Negligible (local training)

Model Card Contact

Daryn Sutton
Email: darynsutton@hotmail.com
GitHub: Ch3w3y