Text and Structural Data Mining of Influenza Mentions in Web and Social Media

Corley, Courtney D.; Cook, Diane J.; Mikler, Armin R.; Singh, Karan P.

doi:10.3390/ijerph7020596

Cited by 210 publications

(145 citation statements)

References 17 publications

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“…Courtney, Diane, Armin & Karan (2010) argue that the web and social media (WSM) is just like a pool of data, which offers us important disease surveillance resource [3]. In traditional method, a patient with influenza-like illness (ILI) will be checked by a physician, so it takes time to conduct a diagnostic test or take blood samples which could provide a certain diagnosis.…”

Section: Text Mining In Website and Social Mediamentioning

confidence: 99%

A Survey of Current Work in Medical Text Mining---Data Source Perspective

Li¹,

Anmeng²,

Wang³

2017

IJBSE

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This paper discusses the application of text mining in medical field at home and abroad based on overview and analysis of current literature data. Foreign researchers have specific text mining tools, and they use it in the search engine data and electronic medical record. In addition, it is also used to predict side effects between drugs. In China, text mining based on medical literature data occupies a large part. On the one hand, they can monitor the self disclosure of health information. On the other hand, they explore whether online information can help individuals get out of the disease. With the development of information technology, text mining will become more and more widely applied in the medical field in the future.

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Section: Text Mining In Website and Social Mediamentioning

confidence: 99%

A Survey of Current Work in Medical Text Mining---Data Source Perspective

Li¹,

Anmeng²,

Wang³

2017

IJBSE

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“…Another field of application is the text analysis of web and social media for detecting influenza-like illnesses [70].…”

Section: Research Track 3 Gdm Graph-based Data Miningmentioning

confidence: 99%

Machine Learning for Health Informatics

Holzinger¹

2016

Lecture Notes in Computer Science

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Abstract. Machine Learning (ML) studies algorithms which can learn from data to gain knowledge from experience and to make decisions and predictions. Health Informatics (HI) studies the effective use of probabilistic information for decision making. The combination of both has greatest potential to rise quality, efficacy and efficiency of treatment and care. Health systems worldwide are confronted with "big data" in high dimensions, where the inclusion of a human is impossible and automatic ML (aML) show impressive results. However, sometimes we are confronted with complex data, "little data", or rare events, where aMLapproaches suffer of insufficient training samples. Here interactive ML (iML) may be of help, particularly with a doctor-in-the-loop, e.g. in subspace clustering, k-Anonymization, protein folding and protein design. However, successful application of ML for HI needs an integrated approach, fostering a concerted effort of four areas: (1) data science, (2) algorithms (with focus on networks and topology (structure), and entropy (time), (3) data visualization, and last but not least (4) privacy, data protection, safety & security.

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“…Enterprises and Government Agencies Though in early stages, social networking technologies and mobile social applications are gaining wide adoption in enterprises and government agencies to engage with business partners, customers, prospects and the general public [6], [8], [9], [11], [13], [22], [23], [25], [28]- [30], [36], [38], [40], [41], [43], [46], [47], [49], [52], [53], [59], [69], [73], [82], [83], [92]. Gartner presents a full list of potential business applications of social software in its hype cycle report, such as social customer relationship management, social commerce, social shopping, social learning, social network payment, social business process management and social government management [40].…”

Section: Applications Of Social Networking Technologies Inmentioning

confidence: 99%

“…R.W. Newkirk et al qualitatively analyze the potential capability of social media in food safety and food terrorism surveillance systems, such as outbreak detection of foodborne diseases and food contamination incidents [11], [13], [52]. Regardless of prior works described above, a lot of researches remain to be done on how to efficiently and effectively fit social networking technologies into the context of improving transparency and visibility of supply chains, and tackle the challenges presented in part A of section II.…”

Section: Applications Of Social Networking Technologies Inmentioning

confidence: 99%

A Framework for Developing Social Networks Enabling Systems to Enhance the Transparency and Visibility of Cross-border Food Supply Chains

Xu¹,

Zhao²,

Lu³

et al. 2014

GSTF J Comput

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Abstract-Demands on safe, high-quality and healthy food are unprecedentedly increasing in both emerging economies and advanced countries. Food safety incidents and scandals, frequently breaking out around the world, are significantly reducing the confidence of consumers in food safety and quality. It is even more difficult to achieve the full transparency, visibility and traceability of dynamic cross-border and global food supply chains, which is critical to ensure food safety and quality, due to: the personalization and globalization of consumer demands; the diversities of food products, raw materials and ingredients; the loose relationships between food business partners; the technology and resource barriers in aligning business processes, coordinating and sharing information; and the changing government supervision mechanisms and standards. Social networking technologies and social software are promising enablers to facilitate end consumers, food companies and government agencies to participate in open discussion, comments and feedback on the quality and safety of cross-border foods. They are able to connect, interact, communicate and collaborate with each other in loose, open, effective and flexible ways for enhancing the transparency and visibility of cross-border and global food supply chains through collective wisdom and intelligence. In this paper a framework is proposed for developing social networks enabling food traceability systems, which not only leverages enterprise Manuscript received November 8, 2013. This work was supported in part by the Guangdong Science and Technology Innovation Fund administered by Guangdong Provincial Department of Science and Technology of China under the Programme of Guangdong-Hong Kong Breakthrough Project in Key Areas (The project reference number: 2011A011303004), and by Shenzhen Strategic Emerging Industry Development Fund (The project reference number: ZDSY20120615142741201).Frank J. Xu was with the E-Business Technology Institute, The University of Hong Kong. He is now with the Department of Civil Engineering, The University of Hong Kong. He is also a member of Shenzhen Institute of Research and Innovation and Shenzhen Key Lab in Carbon Emission Enabling Technologies, The University of Hong Kong (corresponding author, phone: 852-2241-5791; fax: 852-2559-5337; e-mail: frankxu@ hku.hk) Victor P. Zhao is with the Emerging Technologies Institute (formerly as E-Business Technology Institute), The University of Hong Kong (e-mail: vzhao@eti.hku.hk). He is also a member of Shenzhen Institute of Research and Innovation and Shenzhen Key Lab in Carbon Emission Enabling Technologies, The University of Hong Kong. He is currently a PhD candidate of Shanghai University of Finance and Economics, China.Lu Shan is with Guangzhou Wanglaoji Pharmaceutical Company Limited, China (e-mail: shanlu@wlj.com.cn).Chuxiong Huang is with Guangzhou Wanglaoji Pharmaceutical Company Limited, China (e-mail: huangcx@wlj.com.cn) applications, public service platforms for food quality, safety and trace...

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Text and Structural Data Mining of Influenza Mentions in Web and Social Media

Cited by 210 publications

References 17 publications

A Survey of Current Work in Medical Text Mining---Data Source Perspective

A Survey of Current Work in Medical Text Mining---Data Source Perspective

Machine Learning for Health Informatics

A Framework for Developing Social Networks Enabling Systems to Enhance the Transparency and Visibility of Cross-border Food Supply Chains

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