Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical Records

Trienes Jan, Dolf Trienschnigg, Christin Seifert, Djoerd Hiemstra

Research output: Contribution to conferencePaperpeer-review

7 Citations (Scopus)
74 Downloads (Pure)

Abstract

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, deidentification 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.

Original languageEnglish
Pages3-11
Number of pages9
Publication statusPublished - 2020
EventACM Health Search and Data Mining Workshop, HSDM 2020 - Houston, United States
Duration: 3 Feb 20203 Feb 2020
https://sites.google.com/view/hsdm20

Workshop

WorkshopACM Health Search and Data Mining Workshop, HSDM 2020
Abbreviated titleHSDM 2020
Country/TerritoryUnited States
CityHouston
Period3/02/203/02/20
OtherHeld at the 13th ACM International WSDM Conference
Internet address

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