CRFs based de-identification of medical records.
CRFs based de-identification of medical records.
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DOI：
10.1016/j.jbi.2015.08.012
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发表时间：
2015-12
影响因子：
4.5
通讯作者：
Hua W
中科院分区：
文献类型：
--
作者：
He B;Guan Y;Cheng J;Cen K;Hua W
去身份化是2014年i2 b2/UTHealth挑战的共同任务。此任务的目的是从医疗记录中删除受保护的健康信息（PHI）。在本文中，我们提出了一种新的去标识符，WI-deId，基于条件随机场（CRF）。预处理模块采用正则表达式和现成的分词器对病历进行分词，提取三组特征用于训练去标识模型。实验结果表明，该系统对病历的去身份化是有效的，在i2 b2严格实体评价水平下，微F1达到0.9232。
De-identification is a shared task of the 2014 i2b2/UTHealth challenge. The purpose of this task is to remove protected health information (PHI) from medical records. In this paper, we propose a novel de-identifier, WI-deId, based on conditional random fields (CRFs). A preprocessing module, which tokenizes the medical records using regular expressions and an off-the-shelf tokenizer, is introduced, and three groups of features are extracted to train the de-identifier model. The experiment shows that our system is effective in the de-identification of medical records, achieving a micro-F1 of 0.9232 at the i2b2 strict entity evaluation level.