Basic Concepts and Principles of Data Mining in Clinical Practice.
10.4258/jksmi.2009.15.2.175
- Author:
Sun Mi LEE
1
;
Rae Woong PARK
Author Information
1. The Catholic University of Korea College of Nursing, Korea.
- Publication Type:Review
- Keywords:
Clinical Data Mining;
Machine Learning
- MeSH:
Machine Learning;
Data Mining;
Electronic Health Records;
Electronics;
Electrons;
Hospital Information Systems;
Humans;
Informatics;
Public Health;
Research Personnel
- From:Journal of Korean Society of Medical Informatics
2009;15(2):175-189
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
Recently, many hospitals have been adopting clinical data warehouses (CDW) as well as electronic medical records. These new hospital information systems are inevitably introducing very large amounts of clinical data that might be useful for further analysis. However, the electronic clinical data in the CDW are usually byproducts of clinical practice rather than the product of research. Therefore, they include inconsistent and sometimes erroneous information that might not have the specific context of the clinical situations. Data miners usually have various academic backgrounds such as electronics, informatics, statistics, biomedicine, and public health. If the complex situations surrounding the clinical data are not well understood, investigators performing data mining in clinical fields may have problems assessing the information they are confronted with. Here, we would like to introduce some basic concepts on the principles of data mining in clinical fields including legal and ethical considerations as well as technical concerns.
