Secondary use of clinical structured data takes an important place in healthcare research. It
was first described by Fayyad as “knowledge discovery in databases”. Feature extraction is …

Objective Despite at least 40 years of promising empirical performance, very few clinical
natural language processing (NLP) or information extraction systems currently contribute to …

Business and medical professionals rely on large data sets to identify trends or other
knowledge that can be gleaned from the collection of it. New technologies concentrate on …

The increasing availability of electronic health record (EHR) systems has created enormous
potential for translational research. However, it is difficult to know all the relevant codes …

Data mining, through its capacity to discover knowledge embedded in large databases to
improve organizational decision-making, has the potential to contribute to efficiencies and …

In order to better understand the knowledge acquisition process, we studied the changes
which a newly developed “preliminary” QMR disease profile undergoes during the expert …

Background Unstructured data from clinical epidemiological studies can be valuable and
easy to obtain. However, it requires further extraction and processing for data analysis …

Overview With the development and maintenance of large health data repositories of
structured and unstructured data, health organizations are increasingly using data analytics …

This study investigated the extent of use of data mining on electronic health records to
support evidence-based clinical decisions, reasons why only few healthcare institutions …

Abstract Knowledge discovery is a broad research field in which methods are developed to
support discovery of novel and potentially useful knowledge from clinical databases and …