Kagashe Ireneus, Yan Zhijun, Suheryani Imran
School of Management and Economics, Beijing Institute of Technology, Beijing, China.
Sustainable Development Research Institute for Economy and Society of Beijing, Beijing, China.
J Med Internet Res. 2017 Sep 12;19(9):e315. doi: 10.2196/jmir.7393.
Uptake of medicinal drugs (preventive or treatment) is among the approaches used to control disease outbreaks, and therefore, it is of vital importance to be aware of the counts or frequencies of most commonly used drugs and trending topics about these drugs from consumers for successful implementation of control measures. Traditional survey methods would have accomplished this study, but they are too costly in terms of resources needed, and they are subject to social desirability bias for topics discovery. Hence, there is a need to use alternative efficient means such as Twitter data and machine learning (ML) techniques.
Using Twitter data, the aim of the study was to (1) provide a methodological extension for efficiently extracting widely consumed drugs during seasonal influenza and (2) extract topics from the tweets of these drugs and to infer how the insights provided by these topics can enhance seasonal influenza surveillance.
From tweets collected during the 2012-13 flu season, we first identified tweets with mentions of drugs and then constructed an ML classifier using dependency words as features. The classifier was used to extract tweets that evidenced consumption of drugs, out of which we identified the mostly consumed drugs. Finally, we extracted trending topics from each of these widely used drugs' tweets using latent Dirichlet allocation (LDA).
Our proposed classifier obtained an F score of 0.82, which significantly outperformed the two benchmark classifiers (ie, P<.001 with the lexicon-based and P=.048 with the 1-gram term frequency [TF]). The classifier extracted 40,428 tweets that evidenced consumption of drugs out of 50,828 tweets with mentions of drugs. The most widely consumed drugs were influenza virus vaccines that had around 76.95% (31,111/40,428) share of the total; other notable drugs were Theraflu, DayQuil, NyQuil, vitamins, acetaminophen, and oseltamivir. The topics of each of these drugs exhibited common themes or experiences from people who have consumed these drugs. Among these were the enabling and deterrent factors to influenza drugs uptake, which are keys to mitigating the severity of seasonal influenza outbreaks.
The study results showed the feasibility of using tweets of widely consumed drugs to enhance seasonal influenza surveillance in lieu of the traditional or conventional surveillance approaches. Public health officials and other stakeholders can benefit from the findings of this study, especially in enhancing strategies for mitigating the severity of seasonal influenza outbreaks. The proposed methods can be extended to the outbreaks of other diseases.
使用药物（预防性或治疗性）是控制疾病暴发的方法之一，因此，了解最常用药物的数量或频率以及消费者对这些药物的热门话题，对于成功实施控制措施至关重要。传统的调查方法本可以完成这项研究，但就所需资源而言成本过高，而且在发现话题方面容易受到社会期望偏差的影响。因此，需要使用替代的有效方法，如推特数据和机器学习（ML）技术。
利用推特数据，本研究的目的是（1）提供一种方法扩展，以有效提取季节性流感期间广泛使用的药物，（2）从这些药物的推文提取话题，并推断这些话题提供的见解如何加强季节性流感监测。
从2012 - 13流感季节收集的推文中，我们首先识别提及药物的推文，然后使用依存词作为特征构建一个ML分类器。该分类器用于提取证明有药物消费的推文，从中我们识别出最常使用的药物。最后，我们使用潜在狄利克雷分配（LDA）从这些广泛使用的药物的每条推文中提取热门话题。
我们提出的分类器获得了0.82的F分数，显著优于两个基准分类器（即，基于词典的分类器P <.001，单字项频率[TF]分类器P =.048）。该分类器从50,828条提及药物的推文中提取了40,428条证明有药物消费的推文。最广泛使用的药物是流感病毒疫苗，占总数的76.95%左右（31,111 / 40,428）；其他值得注意的药物有泰勒诺流感、日夜百服宁、奈奎尔、维生素、对乙酰氨基酚和奥司他韦。这些药物各自的话题展示了服用这些药物的人的共同主题或经历。其中包括流感药物使用的促进因素和阻碍因素，这些是减轻季节性流感暴发严重程度的关键。
研究结果表明，使用广泛使用药物的推文来加强季节性流感监测以替代传统监测方法是可行的。公共卫生官员和其他利益相关者可以从本研究的结果中受益，特别是在加强减轻季节性流感暴发严重程度的策略方面。所提出的方法可以扩展到其他疾病的暴发。
