An hybrid Machine Learning method for the de-identification of Un-Structured Narrative Clinical Text in Multi-Center Chinese Electronic Medical Records Data
单位：[1]Peking University Medical Informatics Center, Beijing, China[2]National Medical Service Data Center, Beijing, China[3]Peking University Health Science Center, Beijing, China[4]Peking University School of Public Health, Beijing, China[5]Peking University Fifth Clinical Medical College, Beijing, China[6]China-Japan Friendship Hospital, Beijing, China
The premise of the full use of unstructured electronic medical records is to maintain the fully protection of a patient's information privacy. Presently, in prior of processing the electronic medical record date, identification and removing of relevant information which can be used to identify a patient is a research hotspot nowadays. There are very few methods in de identification of Chinese electronic medical records and their cross center performance is poor. Therefore, we develop a de-identification method which is a mixture of rule-based methods and machine learning methods. The method was tested on 700 electronic medical records from six hospitals. Five-fold cross test was used to evaluate the results of c5.0, Random Forest, SVM and XGBOOST. Leave-one-out test was used to evaluate CRF. And the F1 Measure of machine learning reached 91.18% in PHI_Names, 98.21% in PHI_MEDICALID, 95.74% in PHI_OTHERNFC, 97.14% in PHI_GEO, 89.19% in PHI_DATES, and 91.49% in PHI_TEL. And the F1 Measure of rule-based methods reached 93.00% in PHI_Names, 97.00% in PHI_MEDICALID, 97.00% in PHI_OTHERNFC, 97.00% in PHI_GEO, 96.00% in PHI_DATES, and 89.00% in PHI_TEL.
语种：
外文
WOS：
第一作者：
第一作者单位：[1]Peking University Medical Informatics Center, Beijing, China[2]National Medical Service Data Center, Beijing, China
通讯作者：
通讯机构：[1]Peking University Medical Informatics Center, Beijing, China[2]National Medical Service Data Center, Beijing, China
推荐引用方式(GB/T 7714)：
Jin Meng,Zhang Kai,Yang Yunhaonan,et al.An hybrid Machine Learning method for the de-identification of Un-Structured Narrative Clinical Text in Multi-Center Chinese Electronic Medical Records Data[J].2019 10TH IEEE INTERNATIONAL CONFERENCE on BIG KNOWLEDGE (ICBK 2019).2019,105-111.doi:10.1109/ICBK.2019.00023.
APA：
Jin, Meng,Zhang, Kai,Yang, Yunhaonan,Xie, Shuanglian,Song, Kai...&Bao, Xiaoyuan.(2019).An hybrid Machine Learning method for the de-identification of Un-Structured Narrative Clinical Text in Multi-Center Chinese Electronic Medical Records Data.2019 10TH IEEE INTERNATIONAL CONFERENCE on BIG KNOWLEDGE (ICBK 2019),,
MLA：
Jin, Meng,et al."An hybrid Machine Learning method for the de-identification of Un-Structured Narrative Clinical Text in Multi-Center Chinese Electronic Medical Records Data".2019 10TH IEEE INTERNATIONAL CONFERENCE on BIG KNOWLEDGE (ICBK 2019) .(2019):105-111
