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Träfflista för sökning "WFRF:(Yigzaw Kassaye Yitbarek) "

Sökning: WFRF:(Yigzaw Kassaye Yitbarek)

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1.
  • Budrionis, Andrius, et al. (författare)
  • Negation detection in Norwegian medical text : Porting a Swedish NegEx to Norwegian. Work in progress
  • 2018
  • Konferensbidrag (refereegranskat)abstract
    • This paper presents an initial effort in developing a negation detection algorithm for Norwegian clinical text. An evaluated version of NegEx for Swedish was extended to support Norwegian clinical text, by translating the negation triggers and adding more negation rules as well as using a pre-processed Norwegian ICD-10 diagnosis code list to detect symptoms and diagnoses. Due to limited access to the Norwegian clinical text the Norwegian NegEx was tested on Norwegian medical scientific text. NegEx found 70 negated symptoms/diagnoses in the text combined of 170 publications in the medical domain. The results are not completely evaluated due to the lacking gold standard. Some challenging erroneous tokenizations of Norwegian words were found in addition to the need for improved preprocessing and matching techniques for the Norwegian ICD-10 code list. This work pointed out the weaknesses of the current implementation and provided insights for future work.
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2.
  • Chomutare, Taridzo, et al. (författare)
  • De-Identifying Swedish EHR Text Using Public Resources in the General Domain
  • 2020
  • Ingår i: Digital Personalized Health and Medicine. - Amsterdam : IOS Press. - 9781643680828 - 9781643680835 ; , s. 148-152
  • 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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  • Resultat 1-2 av 2
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konferensbidrag (2)
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refereegranskat (2)
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Dalianis, Hercules (2)
Budrionis, Andrius (2)
Yigzaw, Kassaye Yitb ... (2)
Makhlysheva, Alexand ... (2)
Chomutare, Taridzo (2)
Godtliebsen, Fred (1)
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Stockholms universitet (2)
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Engelska (2)
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Naturvetenskap (2)

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