Leveraging Topic Modelling to Analyze Biomedical Research Trends from the PubMed Database Using LDA Method

Abstract

Biomedical research has become an essential entity in human life. However, finding trends related to research topics in the health sector contained in the repository is a challenging matter. In this study, we implemented topic modelling to analyze biomedical research trends using the LDA method. Topic modelling was carried out using data from 7000 articles from PubMed, which were processed with text processing such as lowercase, punctuation removal, tokenization, stop-word removal, and lemmatization. For topic modelling, the LDA with corpus conditions varied to 75% and 100% for validation. Alpha and beta parameters are also set with variations between 0.01, 0.31, 0.61, 0.91, symmetry, and asymmetry when the number of the corpus is changed. When the number of the corpus is 75%, the optimal number of topics is 7, with a coherence value of 0.52. Whereas when the number of the corpus is 100%, the optimal number of topics is 10 with a coherence value of 0.51. In addition, based on the results of article topic modelling, several topics are trending, including disease diagnosis, patient care, and genetic or cell research. Based on the classification of biomedical topics into seven categories, the optimal accuracy, precision, and recall values using the Random Forest algorithm were obtained, namely 85.57%, 87.36%, and 87.58%. The results of this study suggest that topic modelling using the LDA can be used to identify trends in biomedical research with high accuracy. This information can help stakeholders make informed decisions about the direction of future research.