Deep Learning Approaches Outperform Conventional Strategies in De-Identification of German Medical Reports.

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ID: 49836
2019
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Abstract
One of the major obstacles for research on German medical reports is the lack of de-identified medical corpora. Previous de-identification tasks focused on non-German medical texts, which raised the demand for an in-depth evaluation of de-identification methods on German medical texts. Because of remarkable advancements in natural language processing using supervised machine learning methods on limited training data, we evaluated them for the first time on German medical reports using our annotated data set consisting of 113 medical reports from the cardiology domain. We applied state-of-the-art deep learning methods using pre-trained models as input to a bidirectional LSTM network and well-established conditional random fields for de-identification of German medical reports. We performed an extensive evaluation for de-identification and multiclass named entity recognition. Using rule based and out of domain machine learning methods as a baseline, the conditional random field improved F2-score from 70 to 93% for de-identification, the neural approach reached 96% in F2-score while keeping balanced precision and recall rates. These results show, that state-of-the-art machine learning methods can play a crucial role in de-identification of German medical reports.
Reference Key
richterpechanski2019deepstudies Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Richter-Pechanski, Phillip;Amr, Ali;Katus, Hugo A;Dieterich, Christoph;
Journal Studies in health technology and informatics
Year 2019
DOI
10.3233/SHTI190813
URL
Keywords
Deep learning Machine learning lstm de-identification elmo german medical admission notes personal health information

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