DE-IDENTIFICATION OF ELECTRONIC HEALTH RECORDS USING MACHINE LEARNING ALGORITHMS

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

LANGARIZADEH MOSTAFA; OROOJI AZAM

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Health and Biomedical Informatics Year:2017 | Volume:4 | Issue:2 Page(s): 154-167

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

1,041

Download:

Cites:

Information Journal Paper

Title

DE-IDENTIFICATION OF ELECTRONIC HEALTH RECORDS USING MACHINE LEARNING ALGORITHMS

Author(s)

LANGARIZADEH MOSTAFA | OROOJI AZAM | Issue Writer Certificate

Keywords

CONFIDENTIALITYQ2

PRIVACYQ2

DE-IDENTIFICATIONQ2

MACHINE LEARNINGQ1

Abstract

Introduction: Electronic Health Record (EHR) contains valuable clinical information that can be useful for activities such as public health surveillance, quality improvement, and research. However, EHRs often contain identifiable health information that their presence limits the use of the records for sharing and secondary usages. DE-IDENTIFICATION is one of the common methods for protecting the CONFIDENTIALITY of patient information. This systematic review has focused on recently published studies on the usage of DE-IDENTIFICATION methods based on MACHINE LEARNING (ML) approaches for removing all identifiable information from electronic health records.Methods: A systematic review was performed in electronic databases like PubMed and ScienceDirect between 2006 and 2016. Studies were assessed for adherence to the CASP checklists and reviewed independently by two investigators. Finally, 12 articles were matched with inclusion criteria.Results: The selected studies have been discussed in terms of used methods and knowledge resources, types of identifiers detected, types of clinical documents, challenges and achieved results. The results showed that ML-based DE-IDENTIFICATION is a widely invoked approach to protect patient PRIVACY when disclosing clinical data for secondary purposes, such as research. Also, the combination of the ML algorithms and some techniques such as pattern matching and regular expression matching could decrease need to train data.Conclusion: There is a lot of identifiable information in medical records. This study showed MLbased DE-IDENTIFICATION methods can intensively reduce the disclosure risk of information.

Cites

No record.

References

No record.

Cite

APA: Copy

LANGARIZADEH, MOSTAFA, & OROOJI, AZAM. (2017). DE-IDENTIFICATION OF ELECTRONIC HEALTH RECORDS USING MACHINE LEARNING ALGORITHMS. JOURNAL OF HEALTH AND BIOMEDICAL INFORMATICS, 4(2 ), 154-167. SID. https://sid.ir/paper/258909/en

Vancouver: Copy

LANGARIZADEH MOSTAFA, OROOJI AZAM. DE-IDENTIFICATION OF ELECTRONIC HEALTH RECORDS USING MACHINE LEARNING ALGORITHMS. JOURNAL OF HEALTH AND BIOMEDICAL INFORMATICS[Internet]. 2017;4(2 ):154-167. Available from: https://sid.ir/paper/258909/en

IEEE: Copy

MOSTAFA LANGARIZADEH, and AZAM OROOJI, “DE-IDENTIFICATION OF ELECTRONIC HEALTH RECORDS USING MACHINE LEARNING ALGORITHMS,” JOURNAL OF HEALTH AND BIOMEDICAL INFORMATICS, vol. 4, no. 2 , pp. 154–167, 2017, [Online]. Available: https://sid.ir/paper/258909/en

Related Journal Papers