Boolean query construction is often critical for medical systematic review literature search. To create an effective Boolean query, systematic review researchers typically spend weeks coming up with effective query terms and combinations. One challenge to creating an effective systematic review Boolean query is the selection of effective MeSH Terms to include in the query. In our previous work, we created neural MeSH term suggestion methods and compared them to state-of-the-art MeSH term suggestion methods. We found neural MeSH term suggestion methods to be highly effective. In this demonstration, we build upon our previous work by creating (1) a Web-based MeSH term suggestion prototype system that allows users to obtain suggestions from a number of underlying methods and (2) a Python library that implements ours and others' MeSH term suggestion methods and that is aimed at researchers who want to further investigate, create or deploy such type of methods. We describe the architecture of the web-based system and how to use it for the MeSH term suggestion task. For the Python library, we describe how the library can be used for advancing further research and experimentation, and we validate the results of the methods contained in the library on standard datasets. Our web-based prototype system is available at http://ielab-mesh-suggest.uqcloud.net, while our Python library is at https://github.com/ielab/meshsuggestlib.

翻译：Boolean 查询结构往往对医学系统审查文献搜索至关重要。 为创建有效的 Boolean 查询, 系统的审查研究人员通常花几周时间, 提出有效的查询术语和组合。 要创建有效的系统审查 Boolean 查询, 一项挑战是如何选择有效的MesH 术语, 以便在查询中包含有效的MesH 术语。 在我们先前的工作中, 我们创建了神经MesH 术语建议方法, 并将其与最先进的MesH 术语建议方法进行比较。 我们发现神经MeSH 术语建议方法非常有效。 在这个演示中, 我们以先前的工作为基础, 创建了:(1) 基于网络的MesH 术语建议原型系统, 使用户能够从一些基本方法中获取建议。 (2) Python 图书馆, 执行我们和其他人的MesH 术语建议方法, 目的是让想要进一步调查、 创建或部署这类方法的研究人员使用。 我们描述了基于网络的系统的结构, 以及如何将它用于Mesgiblemental Me- listriabb/bbs 样版的图书馆, 我们描述如何利用图书馆来推进研究和实验, 我们的标准系统, 正在验证我们的标准数据库中的数据系统。