João Alveira


2024

pdf bib
Unlocking the Potential of Large Language Models for Clinical Text Anonymization: A Comparative Study
David Pissarra | Isabel Curioso | João Alveira | Duarte Pereira | Bruno Ribeiro | Tomás Souper | Vasco Gomes | André Carreiro | Vitor Rolla
Proceedings of the Fifth Workshop on Privacy in Natural Language Processing

Automated clinical text anonymization has the potential to unlock the widespread sharing of textual health data for secondary usage while assuring patient privacy. Despite the proposal of many complex and theoretically successful anonymization solutions in literature, these techniques remain flawed. As such, clinical institutions are still reluctant to apply them for open access to their data. Recent advances in developing Large Language Models (LLMs) pose a promising opportunity to further the field, given their capability to perform various tasks. This paper proposes six new evaluation metrics tailored to the challenges of generative anonymization with LLMs. Moreover, we present a comparative study of LLM-based methods, testing them against two baseline techniques. Our results establish LLM-based models as a reliable alternative to common approaches, paving the way toward trustworthy anonymization of clinical text.

pdf bib
Anonymization Through Substitution: Words vs Sentences
Vasco Alves | Vitor Rolla | João Alveira | David Pissarra | Duarte Pereira | Isabel Curioso | André Carreiro | Henrique Lopes Cardoso
Proceedings of the Fifth Workshop on Privacy in Natural Language Processing

Anonymization of clinical text is crucial to allow the sharing and disclosure of health records while safeguarding patient privacy. However, automated anonymization processes are still highly limited in healthcare practice, as these systems cannot assure the anonymization of all private information. This paper explores the application of a novel technique that guarantees the removal of all sensitive information through the usage of text embeddings obtained from a de-identified dataset, replacing every word or sentence of a clinical note. We analyze the performance of different embedding techniques and models by evaluating them using recently proposed evaluation metrics. The results demonstrate that sentence replacement is better at keeping relevant medical information untouched, while the word replacement strategy performs better in terms of anonymization sensitivity.