Utilizing social media data for pharmacovigilance: A review.
Utilizing social media data for pharmacovigilance: A review.
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DOI：
10.1016/j.jbi.2015.02.004
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发表时间：
2015-04
影响因子：
4.5
通讯作者：
Gonzalez, Graciela
中科院分区：
文献类型：
--
作者：
Sarker, Abeed;Ginn, Rachel;Nikfarjam, Azadeh;O'Connor, Karen;Smith, Karen;Jayaraman, Swetha;Upadhaya, Tejaswi;Gonzalez, Graciela
药物不良反应(ADRs)的自动监测是一个极具挑战性的研究问题，目前正受到医学信息界的高度关注。近年来，用户在社交媒体上发布的数据，主要是由于其数量巨大，已成为监测ADR的有用资源。使用社交媒体数据的研究使用了各种数据源和技术，这使得比较不同的系统及其性能变得困难。在这篇文章中，我们进行了系统的回顾，以表征不同的方法，从社交媒体检测/提取ADR，以及它们在药物警戒中的适用性。此外，我们提出了一种潜在的从社交媒体监测ADR的系统途径。我们确定了一些研究，描述了从Medline、Embase、Scope和Web of Science数据库以及谷歌学者搜索引擎的社交媒体中检测ADR的方法。符合我们纳入标准的研究是那些试图利用用户在任何公开可用的社交媒体平台上发布的ADR信息的研究。我们将这些研究分为多个维度，如主要的不良反应检测方法、数据量、来源(S)、可获得性、评价标准等。22项研究符合我们的纳入标准，其中15项(68.2%)在过去两年内发表。这项调查揭示了使用注释数据的明显趋势，在过去两年发表的15项研究中，有11项(73.3%)依赖于专家注释。然而，公开可用的注释数据仍然很少，我们发现只有6项(27.3%)研究将使用的注释公开，这使得系统性能比较困难。在算法方面，最流行的是用于检测包含ADR提及的帖子的监督分类技术，以及用于从文本中提取ADR提及的基于词典的方法。我们的审查表明，人们对利用大量可用社交媒体数据进行ADR监测的兴趣正在随着时间的推移而增加。在来源方面，与健康相关的数据和一般社交媒体数据都被用于ADR检测--尽管与健康相关的来源往往包含更高比例的相关数据，但来自一般社交媒体网站的数据量明显更高。现有的公开可用的注释数据仍然非常有限，而且，正如最近的监督学习方法所获得的有希望的结果所表明的那样，迫切需要向研究界提供这种数据。
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. 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. 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. 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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影响因子：
4.5
作者：
Benton, Adrian;Ungar, Lyle;Hill, Shawndra;Hennessy, Sean;Mao, Jun;Chung, Annie;Leonard, Charles E.;Holmes, John H.
通讯作者：
Holmes, John H.
影响因子：
5.7
作者：
Ahmad, SR
通讯作者：
Ahmad, SR
影响因子：
7.4
作者：
Hanson CL;Burton SH;Giraud-Carrier C;West JH;Barnes MD;Hansen B
通讯作者：
Hansen B
影响因子：
--
作者：
Corley CD;Cook DJ;Mikler AR;Singh KP
通讯作者：
Singh KP
影响因子：
4.2
作者：
Freifeld, Clark C.;Brownstein, John S.;Menone, Christopher M.;Bao, Wenjie;Filice, Ross;Kass-Hout, Taha;Dasgupta, Nabarun
通讯作者：
Dasgupta, Nabarun