Development of a medical big-data mining process using topic modeling

With the development of convergence information technology, all of the spaces and objects of human living have become digitized. In the health- and medical-service areas, IT supports Internet of things (IoT)-based medical services and health-care systems for patients. Medical facilities have been advanced on the basis of such IoT devices, and the digitized information on human behaviors and health makes the delivery of efficient and convenient health care possible. Under the given circumstances, health and medical care have been researched. For some of this research, the patient-health data were collected using IoT-based medical devices, and they served as a tool for medical diagnosis and treatment. This study proposes the development of a medical big-data mining process for which topic modeling is employed. The proposed method uses the big data that are offered by the open system of the health- and medical-services big data from the Health Insurance Review and Assessment Service, and their application follows the guidelines of the knowledge discovery in big-data process for data mining and topic modeling. For the medical data regarding the topic modeling, the public structured health- and medical-services big data, Open API, and patient datasets were used. For the document classification in the semantic situation of a topic, the Bag of Words technique and the latent Dirichlet allocation method were applied to find the document association for the development of the medical big-data mining process. In addition, this study conducted a performance evaluation of the topic-modeling accuracy based on the medical big-data mining process and the topic-modeling efficiency, and the effectiveness of the proposed method was examined.