#phramacovigilance -探索深度学习技术，用于识别Twitter中提及的药物摄入
#phramacovigilance - Exploring Deep Learning Techniques for Identifying Mentions of Medication Intake from Twitter
摘要：
挖掘社交媒体消息中的健康和药物相关信息已经引起了药物警戒研究的极大兴趣。社交媒体网站（例如，Twitter），已被用于监测药物滥用，药物使用的不良反应和分析与药物有关的情绪表达。这些研究大多基于大量人口的汇总结果，而不是特定的个人。为了在个人层面或特定群组进行研究，有必要查明提及用户服用药物的帖子。为了实现这一目标，我们训练不同的深度神经网络分类模型上公开可用的注释数据集，并研究其性能识别提到的个人摄入药物的推文。我们还设计和训练了一个新的架构的堆叠集成的浅卷积神经网络（CNN）集成。我们使用随机搜索来调整模型的超参数，并在不同的深度神经网络架构中共享最佳学习模型的超参数所取值的细节。我们的系统产生了最先进的结果，微平均F分数为0.693。
英文摘要：
Mining social media messages for health and drug related information has received significant interest in pharmacovigilance research. Social media sites (e.g., Twitter), have been used for monitoring drug abuse, adverse reactions of drug usage and analyzing expression of sentiments related to drugs. Most of these studies are based on aggregated results from a large population rather than specific sets of individuals. In order to conduct studies at an individual level or specific cohorts, identifying posts mentioning intake of medicine by the user is necessary. Towards this objective, we train different deep neural network classification models on a publicly available annotated dataset and study their performances on identifying mentions of personal intake of medicine in tweets. We also design and train a new architecture of a stacked ensemble of shallow convolutional neural network (CNN) ensembles. We use random search for tuning the hyperparameters of the models and share the details of the values taken by the hyperparameters for the best learnt model in different deep neural network architectures. Our system produces state-of-the-art results, with a micro- averaged F-score of 0.693.
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