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Medical resources are important and necessary in health care. Recently, the development of methods for improving the efficiency of medical resource utilization is an emerging problem. Despite evidence supporting the use of order sets in hospitals, only a small number of health information systems have successfully equipped physicians with analysis of complex order sequences from clinical pathway and clinical guideline. This paper presents a data-mining framework for transnational healthcare system to find alternative practices, including transfusion, pre-admission tests, and evaluation of liver diseases. However, individual countries vary with respect to geographical location, living habits, and culture, so disease risks and treatment methods also vary across countries. To realize the difference, a service-oriented architecture and cloud-computing technology are applied to analyze these medical data. The validity of the proposed system is demonstrated in including Taiwan and Mongolia, to ensure the feasibility of our approach.

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Summary The increasing numbers of large data sets generated by information technologies provide a great opportunity to better understand emerging topics in human society. Retrieving real‐world events from such data, particularly free‐text data, is a complicated task in Natural Language Processing and Location‐based Social Networks. In this work, we propose a new approach, which recognizes geo‐referenced high‐level events/activities mentioned in web sources adopting open gazetteers: OpenStreetMap and Google Maps. Our approach demonstrated on sampled news articles identifies events associated with the relevant topics using a latent Dirichlet allocation. This research is an essential step towards recommendation systems, urban planning, and monitoring.