ABSTRACT
An electronic health record (EHR) is a computerized copy of a patient’s paper chart, which includes information like the patient’s demographics, diagnosis, prescriptions, and laboratory test results, among other things. The increasing role of EHRs, as an extensively used health information source, creates openings and challenges for researchers, health care employees and medical industries across the world, enabling them to gather and analyse EHR data for improving patient care. In this chapter, we provide an overview of the applicability of EHRs in mining comorbidity patterns in diabetic patients. Specifically, we discuss the different types of data stored in EHRs, followed by the challenges faced by the researchers to use EHR data. We, also discuss the different clinical datasets and techniques along with their advantages and disadvantages for mining comorbidity patterns in diabetic patients. This chapter will serve as a primer for researchers to assimilate the use of EHRs in gaining insights into comorbidity patterns in diabetic patients.
