Automated deidentification of clinical text data is crucial due to the high cost of manual deidentification, which has been a barrier to sharing clinical text and the advancement of clinical natural language processing. However, creating effective automated deidentification tools faces several challenges, including issues in reproducibility due to differences in text processing, evaluation methods, and a lack of consistency across clinical domains and institutions. To address these challenges, we propose the pyclipse framework, a unified and configurable evaluation procedure to streamline the comparison of deidentification algorithms. Pyclipse serves as a single interface for running open-source deidentification algorithms on local clinical data, allowing for context-specific evaluation. To demonstrate the utility of pyclipse, we compare six deidentification algorithms across four public and two private clinical text datasets. We find that algorithm performance consistently falls short of the results reported in the original papers, even when evaluated on the same benchmark dataset. These discrepancies highlight the complexity of accurately assessing and comparing deidentification algorithms, emphasizing the need for a reproducible, adjustable, and extensible framework like pyclipse. Our framework lays the foundation for a unified approach to evaluate and improve deidentification tools, ultimately enhancing patient protection in clinical natural language processing.

翻译：暂无翻译