The electronic health record (EHR) represents a rich source of patient information,
increasingly being leveraged for cardiovascular research. Although its primary use remains …

Big data technologies have proliferated since the dawn of the cloud-computing era.
Traditional data storage, extraction, transformation, and analysis technologies have thus …

Objective The rapidly evolving COVID-19 pandemic has created a need for timely data from
the healthcare systems for research. To meet this need, several large new data consortia …

Background Most healthcare data sources store information within their own unique
schemas, making reliable and reproducible research challenging. Consequently …

Background During the COVID-19 pandemic, several methodologies were designed for
obtaining electronic health record (EHR)-derived datasets for research. These processes …

Many industries and academia have devoted a lot of effort and money to creating and/or
using good extract-transform-load (ETL) software suitable for their data analysis purposes …

Abstract Background and Objective Effective sharing and reuse of Electronic Health Records
(EHR) requires technological solutions which deal with different representations and …

In clinical science and practice, text data, such as clinical letters or procedure reports, is
stored in an unstructured way. This type of data is not a quantifiable resource for any kind of …

Data Warehouses (DW) are database implementations that support the storage and analysis
of historical data. The key components of DWs are known as Extraction, Transformation, and …

Big data analytics is playing a critical role in the healthcare industry in providing better
healthcare delivery to patients and in disease exploration research. New big data tools have …