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--- |
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language: |
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- en |
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license: apache-2.0 |
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base_model: emilyalsentzer/Bio_ClinicalBERT |
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tags: |
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- medical |
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- clinical |
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- ssi |
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- classification |
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- surveillance |
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- multi-label |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: SSIBERT-multiclass |
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results: |
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- task: |
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type: text-classification |
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name: Multi-Label SSI Detection |
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dataset: |
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name: Synthetic UK NHS Clinical Notes (Multi-Label) |
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type: synthetic |
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split: test |
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metrics: |
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- name: F1 (Micro) |
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type: f1 |
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value: 1.0 |
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--- |
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# Model Card for Ch3DS/SSIBERT-multiclass |
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## Model Details |
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### Model Description |
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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: |
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1. **Purulence**: Presence of pus or purulent discharge. |
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2. **Redness**: Erythema, spreading redness, or inflammation. |
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3. **Fever**: Pyrexia, rigors, or elevated temperature. |
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4. **Antibiotics**: Prescription of antibiotics (treatment or prophylaxis). |
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5. **SSI**: Overall determination of Surgical Site Infection. |
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It is tailored to **UK NHS terminology**. |
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- **Developed by:** Daryn Sutton |
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- **Model type:** Multi-Label Text Classification (BERT) |
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- **Language(s) (NLP):** English |
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- **License:** Apache 2.0 |
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- **Finetuned from model:** [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) |
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- **Repository:** [https://huggingface.co/Ch3DS/SSIBERT-multiclass](https://huggingface.co/Ch3DS/SSIBERT-multiclass) |
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### Uses |
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#### Direct Use |
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This model extracts structured data from unstructured clinical notes, allowing for more granular surveillance. |
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- **Input**: Clinical note text. |
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- **Output**: Probabilities for `[Purulence, Redness, Fever, Antibiotics, SSI]`. |
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#### Out-of-Scope Use |
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- **Diagnosis**: This is a surveillance tool, not a diagnostic device. |
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- **Non-UK Contexts**: May perform poorly on non-NHS terminology. |
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## How to Get Started with the Model |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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model_name = "Ch3DS/SSIBERT-multiclass" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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text = "Day 3 post THR. Wound oozing pus. Patient pyrexial. Plan: Start Flucloxacillin." |
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inputs = tokenizer(text, return_tensors="pt") |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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probs = torch.sigmoid(logits) |
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labels = ["Purulence", "Redness", "Fever", "Antibiotics", "SSI"] |
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for i, label in enumerate(labels): |
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print(f"{label}: {probs[0][i]:.2%}") |
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``` |
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## Training Details |
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### Training Data |
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- **Source**: 5 million synthetic clinical notes. |
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- **Methodology**: Generated using templates based on UK NHS terminology and the PRAISE network's surveillance definitions. |
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- **Labels**: Multi-hot encoded. |
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### Training Procedure |
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- **Epochs**: 3 |
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- **Batch Size**: 64 |
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- **Hardware**: NVIDIA GeForce RTX 5070 Ti |
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## Evaluation |
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Evaluated on a held-out test set of synthetic data. Achieved near-perfect performance on the synthetic distribution. |
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## Model Card Contact |
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**Daryn Sutton** |
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