Impact Analysis of De-Identification in Clinical Notes Classification

Abstract

Background: Clinical notes provide valuable data in telemonitoring systems for disease management. Such data must be converted into structured information to be effective in automated analysis. One way to achieve this is by classification (e.g. into categories). However, to conform with privacy regulations and concerns, text is usually de-identified. Objectives: This study investigated the effects of de-identification on classification. Methods: Two pseudonymisation and two classification algorithms were applied to clinical messages from a telehealth system. Divergence in classification compared to clear text classification was measured. Results: Overall, de-identification notably altered classification. The delicate classification algorithm was severely impacted, especially losses of sensitivity were noticeable. However, the simpler classification method was more robust and in combination with a more yielding pseudonymisation technique, had only a negligible impact on classification. Conclusion: The results indicate that de-identification can impact text classification and suggest, that considering de-identification during development of the classification methods could be beneficial.