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Multi-class classification of COVID-19 documents using machine learning algorithms.

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This summary is machine-generated.

TF-IDF representations and abstracts are effective for biomedical document classification. Random Forest and BERT neural networks offer the best performance, guiding practitioners in this crucial task.

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Machine Learning

Background:

  • Accurate and rapid identification of relevant scientific research papers is critical in biomedical research, especially during global health crises.
  • Traditional text classifiers are insufficient for biomedical documents, necessitating the integration of diverse data sources like entities and bibliometric data.
  • Developing effective biomedical document classification systems aids researchers and learners in categorizing and retrieving scientific literature efficiently.

Purpose of the Study:

  • To investigate the influence of different information types and feature representation methods on biomedical document classification.
  • To compare the effectiveness of various features, including text, entities, and bibliometric data, for classifying biomedical research papers.
  • To provide practical guidelines for optimizing biomedical document classification systems.

Main Methods:

  • Experiments were conducted using conventional text classification methods with features extracted from titles, abstracts, and bibliometric data.
  • Data preprocessing involved cleaning, feature engineering, and multi-class classification across eleven input data variants.
  • Ten machine learning algorithms were employed to analyze the performance, data efficiency, and interpretability of different models.

Main Results:

  • TF-IDF (Term Frequency-Inverse Document Frequency) representations demonstrated superior performance compared to entity extraction methods.
  • The abstract content alone proved sufficient for accurate document classification.
  • Random Forest and Neural Network (BERT) algorithms achieved the highest performance across various document representations.

Conclusions:

  • Biomedical document classification can be effectively achieved using TF-IDF features derived from abstracts, outperforming entity-based approaches.
  • Random Forest and BERT models provide robust and accurate classification, offering practical solutions for managing biomedical literature.
  • The study offers concrete guidelines for practitioners to enhance biomedical document classification, particularly relevant for managing research during health crises like COVID-19.