Rabby Gollam, Berka Petr
Department of Information and Knowledge Engineering, Prague University of Economics and Business, Prague, Czech Republic.
J Intell Inf Syst. 2023;60(2):571-591. doi: 10.1007/s10844-022-00768-8. Epub 2022 Nov 29.
In most biomedical research paper corpus, document classification is a crucial task. Even due to the global epidemic, it is a crucial task for researchers across a variety of fields to figure out the relevant scientific research papers accurately and quickly from a flood of biomedical research papers. It can also assist learners or researchers in assigning a research paper to an appropriate category and also help to find the relevant research paper within a very short time. A biomedical document classifier needs to be designed differently to go beyond a "general" text classifier because it's not dependent only on the text itself (i.e. on titles and abstracts) but can also utilize other information like entities extracted using some medical taxonomies or bibliometric data. The main objective of this research was to find out the type of information or features and representation method creates influence the biomedical document classification task. For this reason, we run several experiments on conventional text classification methods with different kinds of features extracted from the titles, abstracts, and bibliometric data. These procedures include data cleaning, feature engineering, and multi-class classification. Eleven different variants of input data tables were created and analyzed using ten machine learning algorithms. We also evaluate the data efficiency and interpretability of these models as essential features of any biomedical research paper classification system for handling specifically the COVID-19 related health crisis. Our major findings are that TF-IDF representations outperform the entity extraction methods and the abstract itself provides sufficient information for correct classification. Out of the used machine learning algorithms, the best performance over various forms of document representation was achieved by Random Forest and Neural Network (BERT). Our results lead to a concrete guideline for practitioners on biomedical document classification.
在大多数生物医学研究论文语料库中，文档分类是一项至关重要的任务。即使是由于全球疫情，对于各个领域的研究人员来说，从大量生物医学研究论文中准确快速地找出相关科研论文也是一项至关重要的任务。它还可以帮助学习者或研究人员将一篇研究论文归入适当的类别，并有助于在很短的时间内找到相关的研究论文。生物医学文档分类器需要进行不同的设计，以超越“一般”的文本分类器，因为它不仅依赖于文本本身（即标题和摘要），还可以利用其他信息，如使用一些医学分类法提取的实体或文献计量数据。本研究的主要目的是找出信息或特征的类型以及表示方法对生物医学文档分类任务的影响。因此，我们对从标题、摘要和文献计量数据中提取的不同类型特征的传统文本分类方法进行了多次实验。这些步骤包括数据清理、特征工程和多类分类。使用十种机器学习算法创建并分析了十一种不同变体的输入数据表。我们还将这些模型的数据效率和可解释性评估为任何生物医学研究论文分类系统处理特别是与COVID-19相关的健康危机的基本特征。我们的主要发现是，TF-IDF表示优于实体提取方法，并且摘要本身为正确分类提供了足够的信息。在所使用的机器学习算法中，随机森林和神经网络（BERT）在各种形式的文档表示上表现最佳。我们的结果为生物医学文档分类的从业者提供了具体的指导方针。
