Motivation

In the age of big data, the amount of scientific information available online dwarfs the ability of current tools to support researchers in locating and securing access to the necessary materials. Well-structured open data and the smart systems that make the appropriate use of it are invaluable and can help health researchers and professionals to find the appropriate information by, e.g., configuring the monitoring of information or refining a specific query on a disease.

Methods

We present an automated text classifier approach based on the MEDLINE/MeSH thesaurus, trained on the manual annotation of more than 26 million expert-annotated scientific abstracts. The classifier was developed tailor-fit to the public health and health research domain experts, in the light of their specific challenges and needs. We have applied the proposed methodology on three specific health domains: the Coronavirus, Mental Health and Diabetes, considering the pertinence of the first, and the known relations with the other two health topics.

Results

A classifier is trained on the MEDLINE dataset that can automatically annotate text, such as scientific articles, news articles or medical reports with relevant concepts from the MeSH thesaurus.

Conclusions

The proposed text classifier shows promising results in the evaluation of health-related news. The application of the developed classifier enables the exploration of news and extraction of health-related insights, based on the MeSH thesaurus, through a similar workflow as in the usage of PubMed, with which most health researchers are familiar.

中文翻译：

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NewsMeSH：一种新的分类器，旨在用MeSH标题注释健康新闻

动机

在大数据时代，在线可用的科学信息数量使现有工具支持研究人员查找和确保对必要资料的访问的能力相形见war。结构良好的开放数据和合理利用它们的智能系统非常宝贵，可以通过例如配置信息监控或完善疾病特定查询来帮助健康研究人员和专业人员找到合适的信息。

方法

我们提出了一种基于MEDLINE / MeSH词库的自动文本分类器方法，该方法经过了对超过2600万条带有专家注释的科学摘要的手工注释的培训。根据公共卫生和健康研究领域的专家的特定挑战和需求，量身定制了该分类器。考虑到第一个方面的相关性以及与其他两个健康主题的已知关系，我们已将所建议的方法应用于三个特定的健康领域：冠状病毒，心理健康和糖尿病。

结果

在MEDLINE数据集上训练分类器，该分类器可以使用MeSH词库中的相关概念自动注释文本，例如科学文章，新闻文章或医学报告。

结论

拟议的文本分类器在健康相关新闻的评估中显示出令人鼓舞的结果。开发的分类器的应用通过与大多数医学研究人员熟悉的PubMed用法类似的工作流程，基于MeSH词库，能够探索新闻并提取与健康相关的见解。