Social Media Analytics for Smart Health

Abbasi, Ahmed; Adjeroh, Donald A.; Dredze, Mark; Paul, Michael J.; Zahedi, Fatemeh; Zhao, Huimin; Walia, Nitin; Jain, Hemant; Sanvanson, Patrick; Shaker, Reza; Huesch, Marco D.; Beal, Richard; Zheng, Weiwei; Abate, Marie A.; Ross, Arun

doi:10.1109/mis.2014.29

Cited by 47 publications

(32 citation statements)

References 21 publications

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“…The use of social media for health-related and other tasks is, however, not without drawbacks and difficulties. The drawbacks found when utilizing the user generated content of social media may include issues with the credibility, recency, uniqueness, frequency, and salience of the data [33]. Abbasi and Adjeroh [33] demonstrate the potential downside of each of these five points and the importance of selecting the right media channel for social media analytics.…”

Section: Introductionmentioning

confidence: 99%

Utilizing social media data for pharmacovigilance: A review

Sarker

Ginn

Nikfarjam

et al. 2015

Journal of Biomedical Informatics

426

338

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Objective Automatic monitoring of Adverse Drug Reactions (ADRs), defined as adverse patient outcomes caused by medications, is a challenging research problem that is currently receiving significant attention from the medical informatics community. In recent years, user-posted data on social media, primarily due to its sheer volume, has become a useful resource for ADR monitoring. Research using social media data has progressed using various data sources and techniques, making it difficult to compare distinct systems and their performances. In this paper, we perform a methodical review to characterize the different approaches to ADR detection/extraction from social media, and their applicability to pharmacovigilance. In addition, we present a potential systematic pathway to ADR monitoring from social media. Methods We identified studies, describing approaches for ADR detection from social media from the Medline, Embase, Scopus and Web of Science databases, and the Google Scholar search engine. Studies that met our inclusion criteria were those that attempted to utilize ADR information posted by users on any publicly available social media platform. We categorized the studies into various dimensions such as primary ADR detection approach, size of data, source(s), availability, evaluation criteria, and so on. Results Twenty-two studies met our inclusion criteria, with fifteen (68.2%) published within the last two years. The survey revealed a clear trend towards the usage of annotated data with eleven of the fifteen (73.3%) studies published in the last two years relying on expert annotations. However, publicly available annotated data is still scarce, and we found only six (27.3%) studies that made the annotations used publicly available, making system performance comparisons difficult. In terms of algorithms, supervised classification techniques to detect posts containing ADR mentions, and lexicon-based approaches for extraction of ADR mentions from texts have been the most popular. Conclusion Our review suggests that interest in the utilization of the vast amounts of available social media data for ADR monitoring is increasing with time. In terms of sources, both health-related and general social media data have been used for ADR detection— while health-related sources tend to contain higher proportions of relevant data, the volume of data from general social media websites is significantly higher. There is still very limited publicly available annotated data available, and, as indicated by the promising results obtained by recent supervised learning approaches, there is a strong need to make such data available to the research community.

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Section: Introductionmentioning

confidence: 99%

Utilizing social media data for pharmacovigilance: A review

Sarker

Ginn

Nikfarjam

et al. 2015

Journal of Biomedical Informatics

426

338

View full text Add to dashboard Cite

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“…; tokenization, stemming, stop words removal, vector space modeling, and similarity calculation [19], [20]. Fig.…”

Section: ) Motte (Methodist Hospital Text Teaser)mentioning

confidence: 99%

METEOR: An Enterprise Health Informatics Environment to Support Evidence-Based Medicine

Puppala

Chen

et al. 2015

IEEE Trans. Biomed. Eng.

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The twin pressures of cost containment in the healthcare market and new federal regulations and policies have led to the prioritization of the meaningful use of electronic health records in the United States. EDW and SIA layers on top of EDW are becoming an essential strategic tool to healthcare institutions and integrated delivery networks in order to support evidence-based medicine at the enterprise level.

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“…To describe the process, Adjeroh et al [121] employ the term signal fusion stating the diversity of social media sources, noise, data redundancy and correlation between sources as its major challenges. Abbasi et al [122] have proposed the CRUFS framework (an acronym denoting credibility, recency, uniqueness, frequency and salience) as a uniform foundation for critically assessing different data channels in social media analysis of adverse drug events.…”

Section: Analysis Of Social Datamentioning

confidence: 99%

“…Methods exist to reduce noise and make the data suitable for post-market safety surveillance. However, big data cannot be considered a substitute for traditional data collection and analysis, but rather functions as a supplement to existing methods [61,122]. Abbasi et al [122] stress the importance of developing an understanding of the strengths and limitations of the various social media channels and the capabilities of real-time analytics.…”

Section: Key Insights and Future Directionsmentioning

confidence: 99%

Harnessing social media data for pharmacovigilance: a review of current state of the art, challenges and future directions

Pappa

Stergioulas

2019

Int J Data Sci Anal

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The ever-increasing supply of information combined with the growing knowledge elicitation capabilities of key emerging technologies presents pharmacovigilance with enormous opportunities. Currently, safety monitoring is expanding its evidence base, moving beyond traditional approaches towards sophisticated methods that can identify possible safety signals from multiple information sources, both structured and unstructured. In this context, health information posted online by patients represents a potentially valuable, yet currently left largely unexploited source of post-market safety data that could supplement data from traditional sources of drug safety information. As the use of social media data for pharmacovigilance is still in its infancy, the present paper explores the state of the art in the application of social data to adverse drug reaction detection; provides a thorough review of existing work in the field, highlighting important research efforts and achievements; and finally, discusses the current challenges and promising avenues for future work. Following a literature review methodology, a critical appraisal was conducted of carefully selected work on the use of social data in post-market surveillance, as presented in the recent scientific literature. Out of a sample of more than 1300 articles, which was the result of the literature search, the final selection of articles was made based on their relevance to the applications of social networking sites (SNS) to pharmacovigilance, and a thorough review of this corpus was completed with a total of 100 articles reviewed. The main contributions of this review include the mapping and systematisation of the current knowledge in the field by drawing comparisons of different approaches, types of social data and of relevant sources currently used in the field, and by developing new classifications of social data sources and taxonomies for social data for use in pharmacovigilance, as well as the identification of key challenges and the extraction of new insights in terms of potential for practical applications and future research directions in the area of pharmacovigilance. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Social Media Analytics for Smart Health

Cited by 47 publications

References 21 publications

Utilizing social media data for pharmacovigilance: A review

Utilizing social media data for pharmacovigilance: A review

METEOR: An Enterprise Health Informatics Environment to Support Evidence-Based Medicine

Harnessing social media data for pharmacovigilance: a review of current state of the art, challenges and future directions

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