Medical institutions are challenged to secure competitiveness among medical institutions due to the rapid spread of ICT convergence and to manage the exponential growth of data due to the emergence of big data in the medical industry and the Internet of Things(IoT). The big data paradigm in the medical industry means not merely tools and processes for processing and analyzing large data, but also a computerized change in the way people live, think and study. As medical data are recently released, the demand for the use of medical data is increasing. Therefore, a study on implementation of disease search system based on symptom query using public medical entities was conducted to help rational and efficient decision-making. Most patients with a specific disease have several symptoms at the same time, so it needs to take a comprehensive approach to these comorbidities. However, most major disease control systems are focused on diseases so that they are not able to reflect current mechanical changes that matter preventive care and self-care. Therefore, it is necessary to quickly and easily search various symptomatic diseases by establishing major disease search engines and utilizing the established disease data based on the public medical data of healthcare information accumulated over many years. According to the test results, relevant diseases are searched only by several symptoms and causes, unlike simple search by disease or a single symptom provided by existing public institutions. And even renamed diseases or diseases with similar symptoms were also searched. Therefore, this study aims to find out related diseases through the expansion of queries about symptoms, and to help doctors’ clinical decision-making by searching diseases associated with queries using the causal relationship between terms that appear in the context of medical terms such as disease names or symptoms.
