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The TREC 2015 Clinical Decision Support track is composed of two subtasks, task A and task B. Similar to 2014 [1] , the participants need to answer 30 clinical questions from patient cases for each task. According to the three types of clinical question: diagnosis, test and treatment, these tasks are to retrieve relevant literatures for helping clinicians(More)
Named entity recognition of biomedical text is the shared task 1b of the 2015 CLEF eHealth evaluation lab, which focuses on making biomedical text easier to understand for patients and clinical workers. In this paper, we propose a novel method to recognize clinical entities based on conditional random fields (CRF). The biomedical texts are split into(More)
As understanding social networks requires complete and real-time collection of social network data, it is critical to develop efficient data gathering methods. In particular, smart deployment of monitoring points on these networks will greatly help the data acquisition process. In this paper, we propose a monitoring point deployment strategy on social(More)
BACKGROUND AND OBJECTIVE Electronic medical records (EMRs) contain an amount of medical knowledge which can be used for clinical decision support. We attempt to integrate this medical knowledge into a complex network, and then implement a diagnosis model based on this network. METHODS The dataset of our study contains 992 records which are uniformly(More)
Although cascading failures have occurred on many real-world networks, to our best knowledge, no one has clearly identified this issue on a social network. In this paper, we identify this potential issue on social networks, and develop a theoretical model to analyze related issues. Note that highly-influential “super” users play critical roles(More)
Based on a weighted knowledge graph to represent first-order knowledge and combining it with a probabilistic model, we propose a methodology for the creation of a medical knowledge network (MKN) in medical diagnosis. When a set of symptoms is activated for a specific patient, we can generate a ground medical knowledge network composed of symptom nodes and(More)
Retweet has become one of the most prominent feature on social networks and an important mean for secondary content promotion. Most existing investigations of retweet behaviors on social networks are conducted based on empirical studies or information diffusion models (such as stochastic process or cascading model). To the best of our knowledge, such a(More)
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