论文标题
Drugs4Covid：基于科学出版物的药物驱动知识开发
Drugs4Covid: Drug-driven Knowledge Exploitation based on Scientific Publications
论文作者
论文摘要
由于需求增加，在没有足够的药物治疗的情况下，已经使用了废弃的药物，或者可用的药物剂量被医院药剂师修改。在现有的科学文献中可以找到可以帮助做出这些决定的一些证据。但是，以有效的方式利用大量文件并不容易，因为在文本中可能不会显式相关，并且可以在不同的品牌名称中提及。 Drugs4Covid结合了单词嵌入技术和语义Web技术，以实现面向药物的大型医学文献探索。根据ATC分类和网格类别鉴定药物和疾病。已从Cord-19语料库处理了超过60k的文章和2M段落，并提供了Covid-19，SARS和其他相关冠状病毒的信息。已经创建了一个开放的药物目录，并通过药物浏览器，关键字引导的文本资源管理器和知识图公开获得结果。
In the absence of sufficient medication for COVID patients due to the increased demand, disused drugs have been employed or the doses of those available were modified by hospital pharmacists. Some evidences for the use of alternative drugs can be found in the existing scientific literature that could assist in such decisions. However, exploiting large corpus of documents in an efficient manner is not easy, since drugs may not appear explicitly related in the texts and could be mentioned under different brand names. Drugs4Covid combines word embedding techniques and semantic web technologies to enable a drug-oriented exploration of large medical literature. Drugs and diseases are identified according to the ATC classification and MeSH categories respectively. More than 60K articles and 2M paragraphs have been processed from the CORD-19 corpus with information of COVID-19, SARS, and other related coronaviruses. An open catalogue of drugs has been created and results are publicly available through a drug browser, a keyword-guided text explorer, and a knowledge graph.
