Manitoba Centre for Health Policy, Department of Community Health Sciences, Rady Faculty of Health Sciences, University of Manitoba.
Department of Family Medicine, Rady Faculty of Health Sciences, University of Manitoba.
Int J Popul Data Sci. 2023 Dec 12;8(1):2153. doi: 10.23889/ijpds.v8i1.2153. eCollection 2023.
Using data in research often requires that the data first be de-identified, particularly in the case of health data, which often include Personal Identifiable Information (PII) and/or Personal Health Identifying Information (PHII). There are established procedures for de-identifying structured data, but de-identifying clinical notes, electronic health records, and other records that include free text data is more complex. Several different ways to achieve this are documented in the literature. This scoping review identifies categories of de-identification methods that can be used for free text data.
We adopted an established scoping review methodology to examine review articles published up to May 9, 2022, in Ovid MEDLINE; Ovid Embase; Scopus; the ACM Digital Library; IEEE Explore; and Compendex. Our research question was: What methods are used to de-identify free text data? Two independent reviewers conducted title and abstract screening and full-text article screening using the online review management tool Covidence.
The initial literature search retrieved 3,312 articles, most of which focused primarily on structured data. Eighteen publications describing methods of de-identification of free text data met the inclusion criteria for our review. The majority of the included articles focused on removing categories of personal health information identified by the Health Insurance Portability and Accountability Act (HIPAA). The de-identification methods they described combined rule-based methods or machine learning with other strategies such as deep learning.
Our review identifies and categorises de-identification methods for free text data as rule-based methods, machine learning, deep learning and a combination of these and other approaches. Most of the articles we found in our search refer to de-identification methods that target some or all categories of PHII. Our review also highlights how de-identification systems for free text data have evolved over time and points to hybrid approaches as the most promising approach for the future.
在研究中使用数据通常需要先对数据进行去识别，尤其是在健康数据的情况下，这些数据通常包括个人身份识别信息 (PII) 和/或个人健康识别信息 (PHII)。已经制定了用于去识别结构化数据的程序，但去识别临床记录、电子健康记录和其他包含自由文本数据的记录则更为复杂。文献中记录了几种不同的实现方法。本范围综述确定了可用于自由文本数据的去识别方法类别。
我们采用了既定的范围综述方法，以审查截至 2022 年 5 月 9 日在 Ovid MEDLINE；Ovid Embase；Scopus；ACM 数字图书馆；IEEE Explore 和 Compendex 上发表的综述文章。我们的研究问题是：用于去识别自由文本数据的方法有哪些？两名独立的审查员使用在线审查管理工具 Covidence 进行标题和摘要筛选以及全文文章筛选。
最初的文献搜索检索到 3312 篇文章，其中大多数主要集中在结构化数据上。有 18 篇描述自由文本数据去识别方法的出版物符合我们综述的纳入标准。大多数纳入的文章主要侧重于去除健康保险流通与责任法案 (HIPAA) 确定的个人健康信息类别。他们描述的去识别方法将基于规则的方法或机器学习与其他策略（如深度学习）相结合。
我们的综述确定并分类了自由文本数据的去识别方法，包括基于规则的方法、机器学习、深度学习以及这些方法和其他方法的组合。我们在搜索中找到的大多数文章都提到了针对某些或所有 PHII 类别的去识别方法。我们的综述还强调了自由文本数据去识别系统随着时间的推移是如何演变的，并指出混合方法是未来最有前途的方法。
