5 research outputs found
Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical Records
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
Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical Records
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.Comment: Proceedings of the 1st ACM WSDM Health Search and Data Mining
Workshop (HSDM2020), 202