Graph approach improves patient data masking with better consistency
Consistent Relexicalization of Clinical Documents using Graph-Based Approach
Computation and LanguageArtificial Intelligence
Summary
Sensitive patient information in medical records needs to be hidden before sharing for research. However, simply swapping out names or dates can cause confusing errors across a patient's history. The authors developed a new method that uses graphs and language models to replace sensitive info while keeping relationships and timelines intact. This makes the transformed records more useful for studying health patterns without risking privacy. Their method greatly improves accuracy compared to previous techniques.
What this means in practice
- •For hospital data teams: Generate consistently masked clinical records for research while preserving patient timelines and relationships across visits.
- •For healthcare software developers: Build privacy-focused clinical data transformation tools that improve data utility by maintaining temporal and relational coherence.
Authors
Dipankar Das, Atri Mandal, Sandeep Singh, Tushar Shandhilya
Abstract
Relexicalization is a pivotal technique in clinical NLP, as it facilitates robust masking of sensitive information while synthesizing datasets that retain high-fidelity, real-world characteristics. However, preserving structural integrity, relational coherence, and temporal consistency during transformation remains a significant challenge. Existing approaches frequently rely on independent entity replacement, which results in clinical inconsistencies across longitudinal records. This reduces the value of such relexicalized datasets for downstream scientific analysis. To address these limitations, we introduce G-RELIC (Graph Based Contextual Relexicalization with Improved Consistency) which combines the power of LLMs with graphs. G-RELIC implements a graph-based mapping mechanism which optimizes for one-to-one correspondence between original and surrogate entities. It also introduces a deterministic temporal repositioning algorithm to preserve temporal consistency. Empirical evaluations on diverse, real-world clinical datasets validate that G-RELIC significantly outperforms state-of-the-art baselines. G-RELIC yields a 30.4 percentage point improvement in relational integrity (62.1% to 92.5%) and 45.9 percentage point improvement in temporal coherence (46% to 91.9%) without compromising on the recognized privacy benchmarks for clinical datasets. This maximizes the analytical utility of relexicalized datasets while minimizing re-identification risk.