Quantifying the Severity of Adverse Drug Reactions Using Social Media: Network Analysis.
Quantifying the Severity of Adverse Drug Reactions Using Social Media: Network Analysis.
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DOI：
10.2196/27714
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发表时间：
2021-10-21
影响因子：
7.4
通讯作者：
Altman RB
中科院分区：
文献类型：
--
作者：
Lavertu A;Hamamsy T;Altman RB
药物不良反应（ADR）每年影响美国数十万人的健康，相关费用高达数千亿美元。ADR严重程度的监测和分析受到当前严重程度分类的定性和分类系统的限制。以往的努力对一部分发展成果评估作出了定量估计，但由于与这些努力有关的时间和费用，范围有限。本研究的目的是增加有定量严重程度估计值的ADR数量，同时提高这些严重程度估计值的质量。我们提出了一种半监督的方法，估计ADR的严重程度，使用社会媒体词嵌入构建一个词汇网络的ADR和执行标签传播。我们使用该方法估计了28，113例ADR的严重程度，代表了《国际医学用语词典》中的12，198个独特ADR概念。我们的Reddit不良事件严重程度（SAEDR）评分与现实世界的结果具有良好的相关性。SAEDR评分与美国食品药品监督管理局不良事件报告系统中的ADR病例结局的斯皮尔曼相关系数分别为0.595、0.633和-0.748。我们研究了定义初始种子项集的不同方法，并评估了它们对严重性估计的影响。我们分析了ADR的严重程度分布，这些分布基于其在加框的警告药物标签部分中的出现，以及与性别特异性相关的ADR。我们发现，在上市后发现的ADR的严重程度明显高于临床试验期间发现的ADR（P <0.001）。我们为968种药物创建了定量药物风险特征（DRIP）评分，这些药物与食品和药物管理局不良事件报告系统中导致死亡的药物的相关性为斯皮尔曼0.377，其中给定的药物是主要嫌疑人。我们的SAEDR和DRIP评分与它们所代表的实体的真实结果具有良好的相关性，并已证明在药物警戒研究中的实用性。我们公开了12，198起ADR的SAEDR评分和968种药物的DRIP评分，以便对药物警戒数据进行更定量的分析。
Adverse drug reactions (ADRs) affect the health of hundreds of thousands of individuals annually in the United States, with associated costs of hundreds of billions of dollars. The monitoring and analysis of the severity of ADRs is limited by the current qualitative and categorical systems of severity classification. Previous efforts have generated quantitative estimates for a subset of ADRs but were limited in scope because of the time and costs associated with the efforts. The aim of this study is to increase the number of ADRs for which there are quantitative severity estimates while improving the quality of these severity estimates. We present a semisupervised approach that estimates ADR severity by using social media word embeddings to construct a lexical network of ADRs and perform label propagation. We used this method to estimate the severity of 28,113 ADRs, representing 12,198 unique ADR concepts from the Medical Dictionary for Regulatory Activities. Our Severity of Adverse Events Derived from Reddit (SAEDR) scores have good correlations with real-world outcomes. The SAEDR scores had Spearman correlations of 0.595, 0.633, and −0.748 for death, serious outcome, and no outcome, respectively, with ADR case outcomes in the Food and Drug Administration Adverse Event Reporting System. We investigated different methods for defining initial seed term sets and evaluated their impact on the severity estimates. We analyzed severity distributions for ADRs based on their appearance in boxed warning drug label sections, as well as for ADRs with sex-specific associations. We found that ADRs discovered in the postmarketing period had significantly greater severity than those discovered during the clinical trial (P<.001). We created quantitative drug-risk profile (DRIP) scores for 968 drugs that had a Spearman correlation of 0.377 with drugs ranked by the Food and Drug Administration Adverse Event Reporting System cases resulting in death, where the given drug was the primary suspect. Our SAEDR and DRIP scores are well correlated with the real-world outcomes of the entities they represent and have demonstrated utility in pharmacovigilance research. We make the SAEDR scores for 12,198 ADRs and the DRIP scores for 968 drugs publicly available to enable more quantitative analysis of pharmacovigilance data.
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影响因子：
17.1
作者：
Tatonetti NP;Ye PP;Daneshjou R;Altman RB
通讯作者：
Altman RB
DOI：
10.1145/2939672.2939754
发表时间：
2016-08
期刊：
KDD : proceedings. International Conference on Knowledge Discovery & Data Mining
影响因子：
--
作者：
Grover A;Leskovec J
通讯作者：
Leskovec J
影响因子：
4.5
作者：
Sarker, Abeed;Ginn, Rachel;Nikfarjam, Azadeh;O'Connor, Karen;Smith, Karen;Jayaraman, Swetha;Upadhaya, Tejaswi;Gonzalez, Graciela
通讯作者：
Gonzalez, Graciela
DOI：
10.18653/v1/d16-1057
发表时间：
2016-11
期刊：
Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing
影响因子：
--
作者：
Hamilton WL;Clark K;Leskovec J;Jurafsky D
通讯作者：
Jurafsky D
影响因子：
4.2
作者：
Aronson, JK;Ferner, RE
通讯作者：
Ferner, RE