Tracing and cataloguing knowledge in an e-health cardiology environment

Abstract

In an e-health cardiology environment, the current knowledge engineering systems can support two knowledge processes; the knowledge tracing, and the knowledge cataloguing. We have developed an n-tier system capable of supporting these processes by enabling human collaboration in each phase along with, a prototype scalable knowledge engineering tactic. A knowledge graph is used as a dynamic information structure. Biosignal data (values of HR, QRS, and ST variables) from 86 patients were used; two general practitioners defined and updated the patients' clinical management protocols; and feedback was inserted retrospectively. Several calibration tests were also performed. The system succeeded in formulating three knowledge catalogues per patient, namely, the "patient in life", the "patient in time", and the "patient in action". For each patient the clinically accepted normal limits of each variable were predicted with an accuracy of approximately 95%. The patients' risk-levels were identified accurately, and in turn, the errors were reduced. The data and the expert-oriented feedback were also time-stamped correctly and synchronized under a common time-framework. Knowledge processes optimization necessitates human collaboration and scalable knowledge engineering tactics. Experts should be responsible for resenting or rejecting a process if it downgrades the provided healthcare quality.

Argumentative reasoning and taxonomic analysis for the identification of medical errors

“…Another area where medical errors have been investigated is in e-health cardiology (Gortzis and Nikiforidis, 2008). In e-health cardiology two kinds of knowledge processes are supported (Gortzis and Nikiforidis, 2008):…”

“…Knowledge tracing: This demonstrates how a system executes and produces knowledge. Based on these knowledge processes, Gortzis and Nikiforidis (2008) proposed a system, for minimising errors in healthcare systems (e.g. e-health cardiology environment).…”

“…Their approach is quite similar to teleexpertise since it allows collaboration between medical professionals. The authors of Gortzis and Nikiforidis (2008) achieved their goal by considering knowledge tracing process and knowledge cataloguing process. With regards to the knowledge tracing process that shows how the system executes and produces knowledge, they demonstrated how the collected data are accepted or rejected.…”

“…However, this first result has to be validated by an expert. Each node of the underlying knowledge graph (Gortzis and Nikiforidis, 2008) computes the incoming data, or a request to determine the right path which leads to the corresponding disease as shown in Fig. 2.…”

Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

“…Several knowledge acquisition techniques have been proposed for capturing and documenting clinical knowledge. Graphical tools and methods used for knowledge acquisition include a Unified Modeling Language (UML) based knowledge acquisition method [Garde et al, 2004], graphical knowledge acquisition tools that support the specification of temporal patterns [Chakravarty and Shahar, 2001;Shahar et al, 1999], clinical guidelines [de Clercq et al, 1999;de Clercq et al, 2000;de Clercq et al, 2001], graph-based knowledge engineering techniques [Gortzis and Nikiforidis, 2008], and knowledge-base driven user interface for capturing clinician knowledge during practice [Chambrin et al, 1995].…”