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OBJECTIVES To automatically evaluate the quality of health information on the Internet, we presents a method for detecting indicators for quality of health information. METHODS An automatic indicator detection tool (AIDT) was developed in the following steps: (1) 18 initial technical criteria were chosen; (2) multiple measurable indicators were defined(More)
Simple clustering methods such as hierarchical clustering and k-means are widely used for gene expression data analysis; but they are unable to deal with noise and high dimensionality associated with the microarray gene expression data. Consensus clustering appears to improve the robustness and quality of clustering results. Incorporating prior knowledge in(More)
Permission is granted to quote short excerpts and to reproduce figures and tables from this report, provided that the source of such material is fully acknowledged. Abstract. With the growing use of computers and the Internet, it has become difficult for organizations to locate and effectively manage sensitive personally identifiable information (PII). This(More)
Privacy compliance for free text documents is a challenge facing many organizations. Named entity recognition techniques and machine learning methods can be used to detect private information, such as personally identifiable information (PII) and personal health information (PHI) in free text documents. However, these methods cannot measure the level of(More)
For omni-directional imaging based cooperative robot vision system, panorama unrolling is an important problem. We present an eight direction symmetry reuse algorithm for this problem, principles of this algorithm are: 1) Treat the original image to be unrolled as a series of co-centric circles, and uniformly partition them into eight parts of symmetrical(More)
Consumers face barriers when seeking health information on the Internet. A Personalized Health Information Retrieval System (PHIRS) is proposed to recommend health information for consumers. The system consists of four modules: (1) User modeling module captures user inverted exclamation mark s preference and health interests; (2) Automatic quality filtering(More)