Towards reliable Spanish clinical text de-identification through comparative evaluation of language model approaches.
TL;DR
Fine-tuning-based approaches outperform models without task-specific adaptation and prompt-based strategies, and domain-specific fine-tuning remains the most effective strategy for real-world clinical text de-identification.
Abstract
Background
Unstructured clinical narratives in electronic health records contain essential information for healthcare delivery and research. However, the presence of personally identifiable information poses significant privacy risks, which limit the secondary use of data. Therefore, reliable automated de-identification is a prerequisite for the reuse of clinical texts. This study aims to evaluate different strategies for Spanish clinical text de-identification by assessing the performance of language models on synthetic and real-world datasets.
Methods
This study presents a comparative evaluation of de-identification approaches using two datasets in Spanish: MEDDOCAN, a publicly available synthetic corpus, and ObstEHR, real-world clinical narratives from an obstetrics department. Three de-identification strategies were assessed: (i) inference with a pre-trained task-specific model used as-is, (ii) prompt-based inference using locally deployed large language models (LLMs), and (iii) fine-tuned task-specific models. Model performance was evaluated using precision, recall, and F1 score on test sets. In addition, a text preservation metric was introduced to assess prompt-based LLMs, and the impact of training set size was analyzed using progressively larger subsets of annotated ObstEHR data.
Results
Models without fine-tuning and prompt-based LLMs showed limited performance, with macro-averaged F1 scores ranging from 0.14 to 0.5 on ObstEHR and from 0.19 to 0.5 on MEDDOCAN. Fine-tuned models achieved higher performance, reaching macro-averaged F1 scores of up to 0.956. Learning curve analyses showed consistently high precision and gradual improvements in recall as the amount of training data increased, with high performance achieved with moderate amounts of annotated data.
Conclusion
Fine-tuning-based approaches outperform models without task-specific adaptation and prompt-based strategies. Despite their flexibility in generative settings, LLM-based prompt strategies show limited reliability and information preservation in clinical de-identification. Prompt-based LLMs make slight textual modifications that cause token misalignment, leading to a subsequent decrease in evaluation metrics. Therefore, domain-specific fine-tuning remains the most effective strategy for real-world clinical text de-identification.