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        <title>De-identifying Free Text of Japanese Dummy Electronic Health Records</title>
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    <name type="personal">
        <namePart type="given">Mizuki</namePart>
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        <namePart type="given">Yoshinobu</namePart>
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    <abstract>A new law was established in Japan to promote utilization of EHRs for research and developments, while de-identification is required to use EHRs. However, studies of automatic de-identification in the healthcare domain is not active for Japanese language, no de-identification tool available in practical performance for Japanese medical domains, as far as we know. Previous work shows that rule-based methods are still effective, while deep learning methods are reported to be better recently. In order to implement and evaluate a de-identification tool in a practical level, we implemented three methods, rule-based, CRF, and LSTM. We prepared three datasets of pseudo EHRs with de-identification tags manually annotated. These datasets are derived from shared task data to compare with previous work, and our new data to increase training data. Our result shows that our LSTM-based method is better and robust, which leads to our future work that plans to apply our system to actual de-identification tasks in hospitals.</abstract>
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%0 Conference Proceedings
%T De-identifying Free Text of Japanese Dummy Electronic Health Records
%A Kajiyama, Kohei
%A Horiguchi, Hiromasa
%A Okumura, Takashi
%A Morita, Mizuki
%A Kano, Yoshinobu
%Y Lavelli, Alberto
%Y Minard, Anne-Lyse
%Y Rinaldi, Fabio
%S Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F kajiyama-etal-2018-de
%X A new law was established in Japan to promote utilization of EHRs for research and developments, while de-identification is required to use EHRs. However, studies of automatic de-identification in the healthcare domain is not active for Japanese language, no de-identification tool available in practical performance for Japanese medical domains, as far as we know. Previous work shows that rule-based methods are still effective, while deep learning methods are reported to be better recently. In order to implement and evaluate a de-identification tool in a practical level, we implemented three methods, rule-based, CRF, and LSTM. We prepared three datasets of pseudo EHRs with de-identification tags manually annotated. These datasets are derived from shared task data to compare with previous work, and our new data to increase training data. Our result shows that our LSTM-based method is better and robust, which leads to our future work that plans to apply our system to actual de-identification tasks in hospitals.
%R 10.18653/v1/W18-5608
%U https://aclanthology.org/W18-5608
%U https://doi.org/10.18653/v1/W18-5608
%P 65-70

Markdown (Informal)

[De-identifying Free Text of Japanese Dummy Electronic Health Records](https://aclanthology.org/W18-5608) (Kajiyama et al., Louhi 2018)

• De-identifying Free Text of Japanese Dummy Electronic Health Records (Kajiyama et al., Louhi 2018)

ACL

• Kohei Kajiyama, Hiromasa Horiguchi, Takashi Okumura, Mizuki Morita, and Yoshinobu Kano. 2018. De-identifying Free Text of Japanese Dummy Electronic Health Records. In Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis, pages 65–70, Brussels, Belgium. Association for Computational Linguistics.