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。

MeSH Suggester: 一个用于系统性评价布尔查询构建的 MeSH 术语建议库和系统

MeSH Suggester: A Library and System for MeSH Term Suggestion for Systematic Review Boolean Query Construction

High-quality medical systematic reviews require comprehensive literature
searches to ensure the recommendations and outcomes are sufficiently reliable.
Indeed, searching for relevant medical literature is a key phase in
constructing systematic reviews and often involves domain (medical researchers)
and search (information specialists) experts in developing the search queries.
Queries in this context are highly complex, based on Boolean logic, include
free-text terms and index terms from standardised terminologies (e.g., the
Medical Subject Headings (MeSH) thesaurus), and are difficult and
time-consuming to build. The use of MeSH terms, in particular, has been shown
to improve the quality of the search results. However, identifying the correct
MeSH terms to include in a query is difficult: information experts are often
unfamiliar with the MeSH database and unsure about the appropriateness of MeSH
terms for a query. Naturally, the full value of the MeSH terminology is often
not fully exploited. This article investigates methods to suggest MeSH terms
based on an initial Boolean query that includes only free-text terms. In this
context, we devise lexical and pre-trained language models based methods. These
methods promise to automatically identify highly effective MeSH terms for
inclusion in a systematic review query. Our study contributes an empirical
evaluation of several MeSH term suggestion methods. We further contribute an
extensive analysis of MeSH term suggestions for each method and how these
suggestions impact the effectiveness of Boolean queries.

本研究探讨基于布尔查询的方法，提出词汇和基于预训练的语言模型方法来建议 MeSH 词汇术语，以自动识别高效的 MeSH 术语，并对每种方法的 MeSH 术语建议进行了广泛的分析和经验评估，以提高系统性综述查询的有效性。

系统评价文献检索中自动推荐 MeSH 词汇以实现有效查询构建

Automated MeSH Term Suggestion for Effective Query Formulation in Systematic Reviews Literature Search

We propose a model for tagging unstructured texts with an arbitrary number of
terms drawn from a tree-structured vocabulary (i.e., an ontology). We treat
this as a special case of sequence-to-sequence learning in which the decoder
begins at the root node of an ontological tree and recursively elects to expand
child nodes as a function of the input text, the current node, and the latent
decoder state. In our experiments the proposed method outperforms
state-of-the-art approaches on the important task of automatically assigning
MeSH terms to biomedical abstracts.

本文提出了一种基于序列到序列学习的模型，用于将非结构化文本与本体树中的任意数量术语进行标记，并在自动分配 MeSH 术语给生物医学摘要的重要任务上，优于现有方法。