KQA: A Knowledge Quality Assessment Model for Clinical Decision Support Systems.

Informatics researchers have developed many methods for using computers to utilize knowledge in decision making in the form of clinical decision support systems (CDSSs). These systems can enhance human decision making in the healthcare domain. The knowledge acquisition bottleneck is one of the well-known issues in developing knowledge-based systems such as CDSS.

Issues Associated With the Use of Semantic Web Technology in Knowledge Acquisition for Clinical Decision Support Systems: Systematic Review of the Literature.

Background: Knowledge-based clinical decision support system (KB-CDSS) can be used to help practitioners make diagnostic decisions. KB-CDSS may use clinical knowledge obtained from a wide variety of sources to make decisions. However, knowledge acquisition is one of the well-known bottlenecks in KB-CDSSs, partly because of the enormous growth in health-related knowledge available and the difficulty in assessing the quality of this knowledge as well as identifying the "best" knowledge to use.

A review of subsequence time series clustering.

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition.