MattStammers/SBERT_IBD

Model Card for BioClinicalBERT IBD

The model classifies documents as either IBD or Not IBD

Model Details

Model Description

As above. This is a model trained to detect IBD patients from clinical text

• Developed by: Matt Stammers
• Funded by: University Hospital Foundation NHS Trust
• Shared by: Matt Stammers - SETT Data and AI Clinical Lead
• Model type: BERT Transformer
• Language(s) (NLP): English
• License: cc-by-nc-4.0
• Finetuned from model: sentence-transformers/all-mpnet-base-v2

Model Sources

• Repository: https://huggingface.co/MattStammers/SBERT_IBD
• Paper: MedRxiv- MedRxiv Paper
• Demo: https://huggingface.co/spaces/MattStammers/IBD_Cohort_Identification

Uses

For document classification tasks to differentiate between documents likely to be diagnostic of IBD and those unlikely to be diagnostic of IBD.

Direct Use

This model can be used directly at Cohort Identification Demo

Downstream Use

Others can build on this model and improve it but only for non-commercial purposes.

Out-of-Scope Use

This model is less powerful (in terms of F1 Score) when making predictions at the patient level by 1-2%. It can be used for this purpose but with care.

Bias, Risks, and Limitations

This model does not have any major known biases. It only has some slight bias tendencies.

Recommendations

This model might work well in any population but has not yet been tested in this regard as such.

How to Get Started with the Model

Use the code below to get started with the model.

The model is best used with the transformers library.

Training Details

Training Data

The model was trained on fully pseudonymised clinical information at UHSFT which was carefully labelled by a consultant (attending) physician and evaluated against a randomly selected internal holdout set.

Training Procedure

See the paper for more information on the training procedure

Training Hyperparameters

• Training regime: fp32

Speeds, Sizes, Times

This model (part of a set of models) took 5.61 minutes to train

Evaluation

The model was internally validated against a holdout set

Testing Data, Factors & Metrics

Testing Data

The testing data cannot be revealed due to IG regulations and to remain compliant with GDPR, only the resulting model can be

Factors

IBD vs Not-IBD

Metrics

Full evaluation metrics are available in the paper with a summary below

Results

Model	Doc Coverage	Accuracy	Precision	Recall	Specificity	NPV	F1 Score	MCC
SBERT	768 (100.00%)	89.67% (CI: 86.64% - 92.08%)	88.81% (CI: 85.44% - 91.48%)	99.20% (CI: 97.68% - 99.73%)	56.48% (CI: 47.07% - 65.45%)	95.31% (CI: 87.10% - 98.39%)	93.72% (CI: 92.50% - 94.90%)	0.6844 (CI: 0.6120 - 0.7545)

Summary

Overall performance of the model is high with an F1 Score of >94% on our internal holdout set.

Environmental Impact

Training the model used 0.045kWh of energy emmitting 9.31 grams of CO2

• Hardware Type: L40S
• Hours used: 0.1
• Carbon Emitted: 0.009 Kg CO2

Citation

Stammers M, Gwiggner M, Nouraei R, Metcalf C, Batchelor J. From Rule-Based to DeepSeek R1: A Robust Comparative Evaluation of Fifty Years of Natural Language Processing (NLP) Models To Identify Inflammatory Bowel Disease Cohorts. medRxiv. 2025:2025-07. MedRxiv- MedRxiv Paper

Base Training Data for the Model

Training data

We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the data_config.json file.

Dataset	Paper	Number of training tuples
Reddit comments (2015-2018)	paper	726,484,430
S2ORC Citation pairs (Abstracts)	paper	116,288,806
WikiAnswers Duplicate question pairs	paper	77,427,422
PAQ (Question, Answer) pairs	paper	64,371,441
S2ORC Citation pairs (Titles)	paper	52,603,982
S2ORC (Title, Abstract)	paper	41,769,185
Stack Exchange (Title, Body) pairs	-	25,316,456
Stack Exchange (Title+Body, Answer) pairs	-	21,396,559
Stack Exchange (Title, Answer) pairs	-	21,396,559
MS MARCO triplets	paper	9,144,553
GOOAQ: Open Question Answering with Diverse Answer Types	paper	3,012,496
Yahoo Answers (Title, Answer)	paper	1,198,260
Code Search	-	1,151,414
COCO Image captions	paper	828,395
SPECTER citation triplets	paper	684,100
Yahoo Answers (Question, Answer)	paper	681,164
Yahoo Answers (Title, Question)	paper	659,896
SearchQA	paper	582,261
Eli5	paper	325,475
Flickr 30k	paper	317,695
Stack Exchange Duplicate questions (titles)		304,525
AllNLI (SNLI and MultiNLI	paper SNLI, paper MultiNLI	277,230
Stack Exchange Duplicate questions (bodies)		250,519
Stack Exchange Duplicate questions (titles+bodies)		250,460
Sentence Compression	paper	180,000
Wikihow	paper	128,542
Altlex	paper	112,696
Quora Question Triplets	-	103,663
Simple Wikipedia	paper	102,225
Natural Questions (NQ)	paper	100,231
SQuAD2.0	paper	87,599
TriviaQA	-	73,346
Total		1,170,060,424

Glossary

Term	Description
Accuracy	The percentage of results that were correct among all results from the system overall. Calc: ((TP+TN))/((TP+FP+TN+FN)). Very susceptible to class imbalance.
Precision (PPV)	Also called positive predictive value (PPV), it is the percentage of true positive results among all results that the system flagged as positive. Calc: TP/((TP+FP)). A measure of the trustworthiness of positive results.
Negative Predictive Value (NPV)	The percentage of results that were true negatives (TN) among all results that the system flagged as negative. Calc: TN/((TN+FN)). A measure of the trustworthiness of negative results.
Recall	Also called sensitivity. The percentage of results flagged positive among all results that should have been obtained. Calc: TP/((TP+FN)). 100% recall means there are no false negative results – useful in confidently screening out negative cases.
Specificity	The percentage of results that were flagged negative among all negative results. Calc: TN/((TN+FP)). 100% specificity means there are no false-positive results and rules in the disease.
F1-Score	In this case, the unweighted harmonic mean of PPV/precision and sensitivity/recall. Calc: (2 × (Precision × Recall))/((Precision + Recall)). More resistant to the effects of class imbalance.
Matthews’ Correlation Coefficient (MCC)	A statistical measure used to evaluate the quality of binary classifications. Unlike other metrics, MCC considers all four categories of a confusion matrix. Calc: ((TP×TN) − (FP×FN))/√((TP+FP)(TP+FN)(TN+FP)(TN+FN)). The results are more abstract but highly resistant to the effects of class imbalance, which is why they are included in this study.
Precision / Recall AUC	Represents the area under the Precision-Recall curve, which plots Precision against Recall at various threshold settings. It is more resistant to class imbalance than AUROC (area under the receiver operator curve). Both were used in testing, but the confusion matrix components form the primary outcomes for this study because they are more human-interpretable and harder to statistically manipulate.
Demographic Parity (DP)	Demographic Parity, also known as Statistical Parity, requires that the probability of a positive prediction is the same across different demographic groups. Calc: DP = P(Ŷ=1∣A=a) = P(Ŷ=1∣A=b), where Ŷ is the predicted outcome and A represents the protected attribute (e.g., race, gender). This figure is given as an absolute difference where positive values suggest the more privileged group gains and negative values the reverse. The rule of 10% is used here to look for significant biases.
Equal Opportunity (EO)	Equal Opportunity focuses on equalising the true positive rates across groups. Among those who truly belong to the positive class, the model should predict positive outcomes at equal rates across different groups. Calc: EO = P(Ŷ=1∣Y=1, A=a) = P(Ŷ=1∣Y=1, A=b), where Ŷ is the predicted outcome and Y is the actual outcome. If there is no bias, then the value will be equal for all groups. A higher value indicates a bias against the group considered more vulnerable. The rule of 10% is used here to look for significant biases.
Disparate Impact (DI)	Divides the protected group’s positive prediction rate by that of the most-favoured group. If the ratio is below 0.8 or above 1.25, disparate impact is considered present. Calc: DI = P(Ŷ=1∣A=unfavoured) / P(Ŷ=1∣A=favoured). Values outside the 0.8–1.25 range suggest bias.
Time / Energy / CO₂ Emissions	Measured in minutes and total energy consumption in kilowatt-hours (kWh), which can then be extrapolated to CO₂ emissions via a conversion factor set at 0.20705 Kg CO₂e per kWh for this study.

Model Card Authors

Matt Stammers - Computational Gastroenterologist

Model Card Contact

[email protected]

Legal

1. No guarantee is given of model performance in any production capacity whatsoever.
2. These models should be used in full accordance with the EU AI Act - Regulation 2024/1689.
3. These models are not CE marked medical devices and are suitable at this point only for research and development / experimentation at users own discretion.
4. They can be improved but any improvements should be published openly and shared openly with the community.
5. UHSFT and the author own the copyright and are choosing to share them freely under a CC BY-NC 4.0 Licence for the benefit of the wider research community but not for commercial organisations who are breaking copyright law and infringing upon NHS intellectual property if they try to sell/market these models for profit.