面向社交网络的潜在药物不良反应发现

随着互联网的发展,社交网络中积累了大量的医疗健康领域的文本数据。该文利用基于信息熵的方法,从健康社交网络中的用药者评论数据中识别药物的潜在不良反应;同时,对于潜在药物不良反应,该文提出了基于Word2vec和Skip-gram模型的蛋白质关联紧密度函数,尽最大努力发现药物引起其“潜在”不良反应的证据链。实验证明,该方法用来寻求潜在药物不良反应证据链是有效的。

Potential Adverse Drug Reactions Discovery from Social Networks

With the development of the Internet, social networks have accumulated large amounts of text data about health care. This paper presents an information entropy based method to recognize potential adverse drug reactions from user comments in health related social networks. Meanwhile, to recognize the potential adverse drug reactions, this paper proposes a protein association function based on Word2vec and Skip-gram. Following this functions, this paper tries to detect the evidences between drugs and their potential adverse drug reactions. The results show that this method is promising in providing evidence chain for potential adverse drug reactions.