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
this http URL, while our Python library is at
this https URL.

通过创建基于神经元概念的 MeSH（医学主题头词库）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，我们发现神经元概念的 MeSH 术语建议方法非常有效。我们进一步开发了基于 Web 和 Python 的 MeSH 术语建议原型系统，它允许用户从多种潜在的方法中获得建议，并帮助研究人员进一步开展研究和实验。该网络原型系统可通过 http 链接进行访问，而 Python 库则可以通过 https 链接获得。