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- Chomutare, Taridzo, et al.
(författare)
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De-Identifying Swedish EHR Text Using Public Resources in the General Domain
- 2020
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Ingår i: Digital Personalized Health and Medicine. - Amsterdam : IOS Press. - 9781643680828 - 9781643680835 ; , s. 148-152
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Konferensbidrag (refereegranskat)abstract
- Sensitive data is normally required to develop rule-based or train machine learning-based models for de-identifying electronic health record (EHR) clinical notes; and this presents important problems for patient privacy. In this study, we add non-sensitive public datasets to EHR training data; (i) scientific medical text and (ii) Wikipedia word vectors. The data, all in Swedish, is used to train a deep learning model using recurrent neural networks. Tests on pseudonymized Swedish EHR clinical notes showed improved precision and recall from 55.62% and 80.02% with the base EHR embedding layer, to 85.01% and 87.15% when Wikipedia word vectors are added. These results suggest that non-sensitive text from the general domain can be used to train robust models for de-identifying Swedish clinical text; and this could be useful in cases where the data is both sensitive and in low-resource languages.
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