Electronic health records (EHR) contain a lot of valuable information about individual
patients and the whole population. Besides structured data, unstructured data in EHRs can …

Background The promise of digital health is principally dependent on the ability to
electronically capture data that can be analyzed to improve decision-making. However, the …

Objective We extended a 2013 literature review on electronic health record (EHR) data
quality assessment approaches and tools to determine recent improvements or changes in …

Background Clinical laboratory (lab) tests are used in clinical practice to diagnose, treat, and
monitor disease conditions. Test results are stored in electronic health records (EHRs), and …

Abstract Background: Electronic Clinical Narratives (ECNs) store valuable individual's health
information. However, there are few available open-source data. Besides, ECNs can be …

Recent advancement in EHR-based (Electronic Health Record) systems has resulted in
producing data at an unprecedented rate. The complex, growing, and high-dimensional data …

Background Multiple chronic diseases in patients are a major burden on the health service
system. Currently, diseases are mostly treated separately without paying sufficient attention …

This article presents a systematic literature review that expands and updates a previous
review on the application of machine learning to laboratory medicine. We used Scopus and …

Abstract Information systems such as Electronic Health Record (EHR) systems are
susceptible to data quality (DQ) issues. Given the growing importance of EHR data, there is …

Detecting anomalous sequences is an integral part of building and protecting modern large-
scale health information technology (HIT) systems. These HIT systems generate a large …