Interoperability of Data and Mined Knowledge in Clinical Decision Support Systems

Abstract

<p> The constantly changing and dynamic nature of medical knowledge has proven to be challenging for healthcare professionals. Due to reliance on human knowledge the practice of medicine in many cases is subject to errors that endanger patients' health and cause substantial financial loss to both public and governmental health sectors. Computer based clinical guidelines have been developed to help healthcare professionals in practicing medicine. Currently, the decision making steps within most guideline modeling languages are limited to the evaluation of basic logic expressions. On the other hand, data mining analyses aim at building descriptive or predictive mining models that contain valuable knowledge; and researchers in this field have been active to apply data mining techniques on health data. However, this type of knowledge can not be represented using the current guideline specification standards.</p> <p> In this thesis, we focus is on encoding, sharing and finally using the results obtained from a data mining study in the context of clinical care and in particular at the point of care. For this purpose, a knowledge management framework is proposed that addresses the issues of data and knowledge interoperability. Standards are adopted to represent both data and data mining results in an interoperable manner; and then the incorporation of data mining results into guideline-based Clinical Decision Support Systems is elaborated. A prototype tool has been developed as a part of this thesis that serves as the proof of concept which provides an environment for clinical guideline authoring and execution. Finally three real-world clinical case studies are presented.</p>