By Xiaolong Zheng, Daniel Zeng, Hsinchun Chen, Yong Zhang, Chunxiao Xing, Daniel B. Neill

ISBN-10: 3319084151

ISBN-13: 9783319084152

ISBN-10: 331908416X

ISBN-13: 9783319084169

This publication constitutes the refereed lawsuits of the overseas convention for shrewdpermanent healthiness, ICSH 2014, held in Beijing, China, in July 2014. The 21 papers offered including four prolonged abstracts have been conscientiously reviewed and chosen from various submissions. The papers are equipped in topical sections on details sharing, integrating and extraction; healthiness info research and administration; medical and scientific info mining; and medical perform and scientific monitoring.

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Extra info for Smart Health: International Conference, ICSH 2014, Beijing, China, July 10-11, 2014. Proceedings

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This work was supported in part by the following grants: The National Natural Science Foundation of China, Grant No. 71103180, 91124001, 71025001, and 91024030, by the Early Career Development Award of SKLMCCS and by the Ministry of Health under Grant No. 2012ZX10004801. : The regulatory challenge of electronic cigarettes. : Adolescent Males’ Awareness of and Willingness to Try Electronic Cigarettes. J. Adolesc. : Public health challenges of electronic cigarettes in South Korea. J. Prev. Med. : E-Cigarettes: Promise or Peril?

217–226 (2011) 5. : Towards internet-age pharmacovigilance: extracting adverse drug reactions from user posts to health-related social networks. In: Proceedings of the 2010 Workshop on Biomedical Natural Language Processing, pp. 117– 125. Association for Computational Linguistics (2010) 6. : Pattern mining for extraction of mentions of Adverse Drug Reaction from user comments. In: Proceeding of 2011 AMIA Annual Symposium, pp. 1019–1026 (2011) 7. : Social media mining for drug safety signal detection.

However, the overall DBN performance was better than SVM with higher F1 score and accuracy due to the DBN classification capability with complex medical Web articles. 05. The difference of the performance between the DBN and SVM was significant, thus the hypothesis “DBN is better than SVM“ was supported. 5 Conclusion In this study, we developed a topic detection framework to generate a primary category as the diabetes topic for each article collected from the Chinese health websites. This framework included four components: data collection, data preprocessing, training DBN model, and topic classification.

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Smart Health: International Conference, ICSH 2014, Beijing, China, July 10-11, 2014. Proceedings by Xiaolong Zheng, Daniel Zeng, Hsinchun Chen, Yong Zhang, Chunxiao Xing, Daniel B. Neill


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