Harnessing Moderate-Sized Language Models for Reliable Patient Data Deidentification in Emergency Department Records: Algorithm Development, Validation, and Implementation Study
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View abstract on PubMed
Summary
This summary is machine-generated.Mistral 7B, an open-source language model, effectively deidentifies clinical texts on personal computers. This approach enhances data privacy for medical research without requiring extensive hardware, making pseudonymized clinical data more accessible.
Area Of Science
- Natural Language Processing
- Health Informatics
- Data Privacy
Background
- Digitization of healthcare via electronic health records (EHRs) enhances research but raises privacy concerns.
- Machine learning and large language models (LLMs) have advanced patient data deidentification.
- Advanced LLMs face deployment challenges in hospitals due to security and hardware requirements.
Purpose Of The Study
- To design, implement, and evaluate deidentification algorithms using fine-tuned, moderate-sized open-source language models.
- To ensure the suitability of these models for production inference tasks on personal computers.
- To balance privacy preservation with textual integrity in clinical notes.
Main Methods
- Utilized a dataset of over 425,000 clinical notes from Bordeaux University Hospital.
- Independently double-annotated 3000 notes for validation.
- Fine-tuned open-source models (Llama 2 7B, Mistral 7B, Mixtral 8x7B) using quantized low-rank adaptation.
- Evaluated PII-level (F1-score) and note-level (recall, BLEU) metrics.
Main Results
- Mistral 7B achieved the highest overall F1-score (0.9673) and note-level recall (0.9326).
- Mistral 7B's recall for name deidentification reached 0.9915.
- BLEU scores consistently exceeded 0.9864, indicating minimal text alteration.
Conclusions
- Generative NLP models, particularly Mistral 7B, demonstrate strong capabilities for efficient clinical text deidentification.
- Mistral 7B performs effectively on personal computers, addressing hardware limitations.
- This research facilitates broader access to pseudonymized clinical data for research and healthcare optimization.
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