The number of missing people (i.e., people who get lost) greatly increases in recent years. It is a serious worldwide problem, and finding the missing people consumes a large amount of social resources. In tracking and finding these missing people, timely data gathering and analysis actually play an important role. With the development of social media, information about missing people can get propagated through the web very quickly, which provides a promising way to solve the problem. The information in online social media is usually of heterogeneous categories, involving both complex social interactions and textual data of diverse structures. Effective fusion of these different types of information for addressing the missing people identification problem can be a great challenge. Motivated by the multi-instance learning problem and existing social science theory of "homophily", in this paper, we propose a novel r-instance (RI) learning model.
Background The Adverse Event Reporting System is the primary surveillance database used by the Food and Drug Administration for identifying postmarketing drug safety problems. Methods We analyzed all reports of suspected adverse drug reactions submitted to the Food and Drug Administration from the inception of the Adverse Event Reporting System database in 1969 through December 2002. We documented drug withdrawals and restricted distribution programs based on safety concerns. Results During the 33-year period from 1969 when adverse drug event reporting was initiated through 2002, about 2.3 million case reports of adverse events for the cumulative number of approximately 6000 marketed drugs were entered in the database. Most reports were for female patients. During this period, numerous drug reactions have been identified and added to the product labeling as boxed warnings, warnings, precautions, contraindications, and adverse reactions. More than 75 drugs drug products have been removed from the market due to safety problems. In addition, 11 drugs have special requirements for prescriptions or have restricted distribution programs. Drugs withdrawn or restricted represent a small proportion (about 1 ) of marketed drugs. Conclusions The Food and Drug Administration’s Adverse Event Reporting System is the primary surveillance database used for the identification of safety problems of marketed drugs. Despite the limitations of underreporting, differential reporting, and uneven quality, submitted reports often allow the identification of serious adverse events that are added to the product labeling information. In rare instances, additional regulations, up to and including market removal, have been required. We encourage physicians, pharmacists, other health care professionals, and patients to continue to report serious suspected and known adverse drug reactions to manufacturers and the Food and Drug Administration. The textual entailment task - determining if a given text entails a given hypothesis - provides an abstraction of applied semantic inference. This paper describes first a general generative probabilistic setting for textual entailment. We then focus on the sub-task of recognizing whether the lexical concepts present in the hypothesis are entailed from the text. This problem is recast as one of text categorization in which the classes are the vocabulary words. We make novel use of Naive Bayes to model the problem in an entirely unsupervised fashion. Empirical tests suggest that the method is effective and compares favorably with state-of-the-art heuristic scoring approaches.