title: CRFs based de-identification of medical records.
abstract: 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.
authors: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
authors: Bin
authors: B
authors: He
authors: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. Electronic address: guanyi@hit.edu.cn.
authors: Yi
authors: Y
authors: Guan
authors: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
authors: Jianyi
authors: J
authors: Cheng
authors: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
authors: Keting
authors: K
authors: Cen
authors: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
authors: Wenlan
authors: W
authors: Hua
mesh_terms: D002681:China; D015331:Cohort Studies; D016494:Computer Security; D003219:Confidentiality; D003627:Data Interpretation, Statistical; D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records / Q000458:organization & administration*; D033262:Narration; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D018875:Vocabulary, Controlled
keywords: Conditional random fields; De-identification; Medical records; Protected health information