Electronic Health Records (EHR) have brought valuable improvements to hospital practices by integrating patient information. In fact, the understanding of this data can prevent mistakes that may put patients' lives at risk. Nonetheless, to the best of our knowledge, there are no previous studies addressing the automatic detection of outlier prescriptions, regarding dosage and frequency. In this paper, we propose an unsupervised method, called Density-Distance-Centrality (DDC), to detect potential outlier prescriptions. A dataset with 563 thousand prescribed medications was used to assess our proposed approach against different state-of-the-art techniques for outlier detection. In the experiments, our approach achieves better results in the task of overdose and underdose detection in medical prescriptions, compared to other methods applied to this problem. Additionally, most of the false positive instances detected by our algorithm were potential prescriptions errors.
Abstract. Teachers have increasingly employed different methods to enrich the learning of a subject in class, drive other assignments, and meet curriculum standards. One of such methods is the use of movies as an alternative educational experience to support class discussions. In this sense, websites such as TeachWithMovies 1 , arise as a valuable support to the creation of lesson plans. In this website, each movie is described as a lesson plan targeting the learning of a subject. However, the creation of such lesson plan or even a simple educational description of the movie can demand much work and time, since the text describing the teaching plan must consider educational aspects of the movie. In this work, we propose BEATnIk (Biased Educational Automatic Text summarIzation), which is an unsupervised algorithm to automatically generate movies' summaries. Such algorithm favors educational aspects from the text to generate a biased educational summary. The experiments conducted show that our approach statistically outperforms a baseline in precision, recall, and f-score.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.