Research Results from University of Manitoba Update Knowledge of Machine Learning (De-identification of Free Text Data containing Personal Health Information: A Scoping Review of Reviews): Machine Learning

2023 DEC 26 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Investigators publish new report on artificial intelligence. According to news originating from the University of Manitoba by NewsRx editors, the research stated, “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.”

The news reporters obtained a quote from the research from University of Manitoba: “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.”

According to the news editors, the research concluded: “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.”

For more information on this research see: De-identification of Free Text Data containing Personal Health Information: A Scoping Review of Reviews. International Journal of Population Data Science, 2023,8(1). (International Journal of Population Data Science - https://ijpds.org). The publisher for International Journal of Population Data Science is Swansea University.

A free version of this journal article is available at https://doi.org/10.23889/ijpds.v8i1.2153.

Our news journalists report that more information may be obtained by contacting Bekelu Negash, Manitoba Center for Health Policy, Department of Community Health Sciences, Rady Faculty of Health Sciences, University of Manitoba. Additional authors for this research include Alan Katz, Christine J. Neilson, Moniruzzaman Moni, Marcello Nesca, Alexander Singer, Jennifer E. Enns.

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