Extended kepler-based PACS-grid system for chronological clustering

Abstract

In health care, chronic disease is a long term sickness which requires lifetime treatment. Medical doctor needs to keep tracking patients’ status over time including periodically lab tests and medical examinations. Chronic disease requires periodical medical tests; therefore, the results from these tests need to be consolidated into single view. It is inadequate for medical doctor to review huge number of medical tests for a chronic patient. Medical doctors are not only reviewing medical tests, but they sometimes use Computer-Aided tools to analysis these tests. Over long period of time, patient will develop a huge database of medical records which makes the summarization of his/her condition difficult to present in a concise view. The objective of this thesis is to extend the workflow capability over PACS-Grid to accommodate the Timeline visualization tool for overseas chronic patients. This work has dual requirements to be considered which are related to Timeline and Grid computing. In this thesis, we defined the main requirement to be the extended Kepler for PACS-Grid, which will help in orchestrating the Timeline Visualization. On the other hand, we defined the other requirement to be the decision mechanism of displaying the most important reports based on Chronological Clustering Algorithm. The important benefit of this proposal is to optimize the view of chronic patients without harness the grid infrastructure with capacity storage. Using the extended Kepler, medical doctors could adjust the view of their patients’ profiles and re-launch the computation through the Extended Kepler as they diagnosis. This adjustment in the workflow Kepler would be performed fast with neither utilizes the grid resources storage capacity with neither temporary data nor stress PACS-System with multiple requests for the same data.