Mizuki Morita


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De-identifying Free Text of Japanese Dummy Electronic Health Records
Kohei Kajiyama | Hiromasa Horiguchi | Takashi Okumura | Mizuki Morita | Yoshinobu Kano
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis

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.


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MedNLPDoc: Japanese Shared Task for Clinical NLP
Eiji Aramaki | Yoshinobu Kano | Tomoko Ohkuma | Mizuki Morita
Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)

Due to the recent replacements of physical documents with electronic medical records (EMR), the importance of information processing in medical fields has been increased. We have been organizing the MedNLP task series in NTCIR-10 and 11. These workshops were the first shared tasks which attempt to evaluate technologies that retrieve important information from medical reports written in Japanese. In this report, we describe the NTCIR-12 MedNLPDoc task which is designed for more advanced and practical use for the medical fields. This task is considered as a multi-labeling task to a patient record. This report presents results of the shared task, discusses and illustrates remained issues in the medical natural language processing field.


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Word in a Dictionary is used by Numerous Users
Eiji Aramaki | Sachiko Maskawa | Mai Miyabe | Mizuki Morita | Sachi Yasuda
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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The First Workshop on Natural Language Processing for Medical and Healthcare Fields
Eiji Aramaki | Mizuki Morita
The First Workshop on Natural Language Processing for Medical and Healthcare Fields


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Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter
Eiji Aramaki | Sachiko Maskawa | Mizuki Morita
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing