TY - JOUR AU - Kajiyama, Kohei AU - Horiguchi, Hiromasa AU - Okumura, Takashi AU - Morita, Mizuki AU - Kano, Yoshinobu PY - 2020 DA - 2020/09/21 TI - De-identifying free text of Japanese electronic health records JO - Journal of Biomedical Semantics SP - 11 VL - 11 IS - 1 AB - Recently, more electronic data sources are becoming available in the healthcare domain. Electronic health records (EHRs), with their vast amounts of potentially available data, can greatly improve healthcare. Although EHR de-identification is necessary to protect personal information, automatic de-identification of Japanese language EHRs has not been studied sufficiently. This study was conducted to raise de-identification performance for Japanese EHRs through classic machine learning, deep learning, and rule-based methods, depending on the dataset. SN - 2041-1480 UR - https://doi.org/10.1186/s13326-020-00227-9 DO - 10.1186/s13326-020-00227-9 ID - Kajiyama2020 ER -