Vi Sualization of Cancer Comorbidity “Disease Graph”

With the growth of aging population and changes happening in lifestyle, more people are prone to get cancers. According to the National Health Service latest report, in 2012 the number of newly diagnosed cancer cases were nearly about one hundred thousand people. Since the with an increased aging population day-by-day, the number of cancer patients also continues to increase, and the annual payment for cancer accounts 20% funds of the total national health expenditure of universal health insurance system which is noticeably heavy burden in Taiwan. Cancer seems to become a chronic disease and the treatment planning should be advanced to benefit assessment and cannot be just rely on the past the concept of acute cancer therapies. With the advancement of technology and big data, it should be more dimensional perspective to observe and care chronic cancer patients with the identification of cancer precursor. By providing a more appropriate care would help to meet the chronic cancer patient’s demand for medical services. Big data visualization approach will provide researchers and clinicians the best solution at this stage to see the disease patterns and related comorbid factors associated with cancer. Therefore, the goal of this study is to visualize the top 10 cancer precursors in Taiwanese population, to learn more about the top ten cancer related impacts of comorbidity, multiple drug use with the progression of the disease affects, time series, causality, and then observe the process of comorbidity and disease with time. This will provide clinical decision support and reduce long-term health care resources consumption. The research project will also provide a long-term information about top ten cancers and help to render the multi-dimensional associated factors, will help to explore the complex disease evolution trends and the time factors through huge amount of visual information from the long-term health data. This research project will be beneficial for clinicians to answer their concerns, help in disease diagnosis and patient treatment decisions and plans. This jointly will enhance the quality of medical care; especially in terms of medical education, researchers can take advantage of multi-dimensional perspective to disease diversity, three-dimensional perspective, and visual evolution of different diseases with complex comorbidities; for policy-makers, it can help to develop the medical benefit plans, substantial environmental regulations, and also help to effectively monitor and enhance cancer prognosis.