Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical Records
This addresses the need for effective de-identification tools in non-English medical contexts, though it is incremental as it applies existing methods to new data.
The study tackled the problem of de-identifying Dutch medical records by comparing rule-based, feature-based, and deep neural methods, finding that a neural approach performed strongly across languages and domains with limited training data, while an existing rule-based method failed to generalize.
Unstructured information in electronic health records provide an invaluable resource for medical research. To protect the confidentiality of patients and to conform to privacy regulations, de-identification methods automatically remove personally identifying information from these medical records. However, due to the unavailability of labeled data, most existing research is constrained to English medical text and little is known about the generalizability of de-identification methods across languages and domains. In this study, we construct a varied dataset consisting of the medical records of 1260 patients by sampling data from 9 institutes and three domains of Dutch healthcare. We test the generalizability of three de-identification methods across languages and domains. Our experiments show that an existing rule-based method specifically developed for the Dutch language fails to generalize to this new data. Furthermore, a state-of-the-art neural architecture performs strongly across languages and domains, even with limited training data. Compared to feature-based and rule-based methods the neural method requires significantly less configuration effort and domain-knowledge. We make all code and pre-trained de-identification models available to the research community, allowing practitioners to apply them to their datasets and to enable future benchmarks.