Published in

Association for Computing Machinery, ACM Computing Surveys, 2(55), p. 1-40, 2023

DOI: 10.1145/3490234



Export citation

Search in Google Scholar

The Secondary Use of Electronic Health Records for Data Mining: Data Characteristics and Challenges

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

Full text: Unavailable

Green circle
Preprint: archiving allowed
Green circle
Postprint: archiving allowed
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO


The primary objective of implementing Electronic Health Records (EHRs) is to improve the management of patients’ health-related information. However, these records have also been extensively used for the secondary purpose of clinical research and to improve healthcare practice. EHRs provide a rich set of information that includes demographics, medical history, medications, laboratory test results, and diagnosis. Data mining and analytics techniques have extensively exploited EHR information to study patient cohorts for various clinical and research applications, such as phenotype extraction, precision medicine, intervention evaluation, disease prediction, detection, and progression. But the presence of diverse data types and associated characteristics poses many challenges to the use of EHR data. In this article, we provide an overview of information found in EHR systems and their characteristics that could be utilized for secondary applications. We first discuss the different types of data stored in EHRs, followed by the data transformations necessary for data analysis and mining. Later, we discuss the data quality issues and characteristics of the EHRs along with the relevant methods used to address them. Moreover, this survey also highlights the usage of various data types for different applications. Hence, this article can serve as a primer for researchers to understand the use of EHRs for data mining and analytics purposes.