A machine learning based approach to identify protected health information in Chinese clinical text.
Background: With the increasing application of electronic health records (EHRs) in the world, protecting private information in clinical text has drawn extensive attention from healthcare providers to researchers. De-identification, the process of identifying and removing protected health informatio...
| Publicado en: | International Journal of Medical Informatics Vol. 116; pp. 24 - 33 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Aug2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=130046173&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130046173 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13865056 JR4 jtl: International Journal of Medical Informatics issn: 13865056 maglogo: N pubinfo: dt: Aug2018 vid: 116 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 130046173 130046173 NLM29887232 130046173 10.1016/j.ijmedinf.2018.05.010 NLM29887232 130046173 ppf: 24 ppct: 9 formats: tig: atl: A machine learning based approach to identify protected health information in Chinese clinical text. aug: au: Du, Liting Xia, Chenxi Deng, Zhaohua Lu, Gary Xia, Shuxu Ma, Jingdong affil: School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Hubei, China sug: subj: Data Security Privacy and Confidentiality China Software Algorithms Natural Language Processing Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies ab: Background: With the increasing application of electronic health records (EHRs) in the world, protecting private information in clinical text has drawn extensive attention from healthcare providers to researchers. De-identification, the process of identifying and removing protected health information (PHI) from clinical text, has been central to the discourse on medical privacy since 2006. While de-identification is becoming the global norm for handling medical records, there is a paucity of studies on its application on Chinese clinical text. Without efficient and effective privacy protection algorithms in place, the use of indispensable clinical information would be confined.Objectives: We aimed to (i) describe the current process for PHI in China, (ii) propose a machine learning based approach to identify PHI in Chinese clinical text, and (iii) validate the effectiveness of the machine learning algorithm for de-identification in Chinese clinical text.Methods: Based on 14,719 discharge summaries from regional health centers in Ya'an City, Sichuan province, China, we built a conditional random fields (CRF) model to identify PHI in clinical text, and then used the regular expressions to optimize the recognition results of the PHI categories with fewer samples.Results: We constructed a Chinese clinical text corpus with PHI tags through substantial manual annotation, wherein the descriptive statistics of PHI manifested its wide range and diverse categories. The evaluation showed with a high F-measure of 0.9878 that our CRF-based model had a good performance for identifying PHI in Chinese clinical text.Conclusion: The rapid adoption of EHR in the health sector has created an urgent need for tools that can parse patient specific information from Chinese clinical text. Our application of CRF algorithms for de-identification has shown the potential to meet this need by offering a highly accurate and flexible solution to analyzing Chinese clinical text. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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