Pteredactyl Python Module, Gradio Webapp, API

• Authors: Matt Stammers🧪, Cai Davis🥼 and Michael George🩺

• Version 1. 29/06/2024

Clinical patient identifiable information (cPII) presents a significant challenge in natural language processing (NLP) that has yet to be fully resolved but significant progress is being made [1,2].

This is why we created Pteredactyl - a python module to help with redaction of clinical free text.

Tool Usage Instructions

When the input text is entered, the tool redacts the cPII from the entered text using NLP with labelled masking tokens and then assesses the models results. You can test the text against different models by selecting from the dropdown.

Python Module

The source code for the python module can be accessed here: Pteredactyl

We welcome contributors to work on this further and develop it.

Gradio Web App

The source code for the web app and API can be found here: Pteredactyl_Gradio_Web_App

This is so that other NHS and healthcare sites can deploy this on premesis or in the cloud as a standalone python module is not as useful as a webapp/api to most NHS/Healthcare users.

This webapp is already available online as a gradio app on Huggingface: Huggingface Gradio App. It is also available as source or as a Docker Image: Docker Image. All are MIT licensed.

Docker Deployment

Please note if deploying the docker image the port bindings are to 7860. The image can also be deployed from source using the following command:

docker build -t pteredactyl:latest .
docker run -d -p 7860:7860 --name pteredactyl-app pteredactyl:latest

Or can be deployed directly from Docker Hub

Background

We introduce a concentrated validation battery to the open-source OHDSI community, a Python module for cPII redaction on free text, and a web application that compares various models against the battery. This setup, which allows simultaneous production API deployment using Gradio, is remarkably efficient and can operate on a single CPU core.

Methods:

We evaluated three open-source models from Huggingface: Stanford Base De-Identifier, Deberta PII, and Nikhilrk De-Identify using our Clinical_PII_Redaction_Test dataset. The text was tokenised, and all entities such as [PERSON], [ID], and [LOCATION] were tagged in the gold standard. Each model redacted cPII from clinical texts, and outputs were compared to the gold standard template to calculate the confusion matrix, accuracy, precision, recall, and F1 score.

Results

The full results of the tool are given below in Table 1 below.

Metric	Stanford Base De-Identifier	Deberta PII	Nikhilrk De-Identify
Accuracy	0.98	0.85	0.68
Precision	0.91	0.93	0.28
Recall	0.94	0.16	0.49
F1 Score	0.93	0.28	0.36

Table 1: Summary of Model Performance Metrics

Strengths

• The test benchmark Clinical_PII_Redaction_Test intentionally exploits commonly observed weaknesses in NLP cPII token masking systems such as clinician/patient/diagnosis name similarity and commonly observed ID/username and location/postcode issues.

• The Stanford De-Identifier Base Model[1] is 99% accurate on our test set of radiology reports and achieves an F1 score of 93% on our challenging open-source benchmark. The others models are really to demonstrate the potential of Pteredactyl to deploy any transfomer model.

• We have submitted the code to OHDSI as an abstract and aim strongly to incorporate this into a wider open-source effort to solve intractable clinical informatics problems.

Limitations

• The tool was not designed initially to redact clinic letters as it was developed primarily on radiology reports in the US. We have made some augmentations to cover elements like postcodes using checksums but these might not always work. The same is true of NHS numbers as illustrated above.

• It may overly aggressively redact text because it was built as a research tool where precision is prized > recall. However, in our experience this is uncommon enough that it is still very useful.

• This is very much a research tool and should not be relied upon as a catch-all in any production-type capacity. The app makes the limitations very transparently obvious via the attached confusion matrix.

Conclusion

The validation cohort introduced in this study proves to be a highly effective tool for discriminating the performance of open-source cPII redaction models. Intentionally exploiting common weaknesses in cNLP token masking systems offers a more rigorous cPII benchmark than many larger datasets provide.

We invite the open-source community to collaborate to improve the present results and enhance the robustness of cPII redaction methods by building on the work we have begun here.

Logo

References:

1. Chambon PJ, Wu C, Steinkamp JM, Adleberg J, Cook TS, Langlotz CP. Automated deidentification of radiology reports combining transformer and “hide in plain sight” rule-based methods. J Am Med Inform Assoc. 2023 Feb 1;30(2):318–28.
2. Kotevski DP, Smee RI, Field M, Nemes YN, Broadley K, Vajdic CM. Evaluation of an automated Presidio anonymisation model for unstructured radiation oncology electronic medical records in an Australian setting. Int J Med Inf. 2022 Dec 1;168:104880.