Text Mining of Biomedical Articles Using the Konstanz Information Miner (KNIME) Platform: Hemolytic Uremic Syndrome as a Case Study

Abstract

Objectives: Automated systems for information extraction are becoming very useful due to the enormous scale of the existing literature and the increasing number of scientific articles published worldwide in the field of medicine. We aimed to develop an accessible method using the open-source platform KNIME to perform text mining (TM) on indexed publications. Material from scientific publications in the field of life sciences was obtained and integrated by mining information on hemolytic uremic syndrome (HUS) as a case study. Methods: Text retrieved from Europe PubMed Central (PMC) was processed using specific KNIME nodes. The results were presented in the form of tables or graphical representations. Data could also be compared with those from other sources. Results: By applying TM to the scientific literature on HUS as a case study, and by selecting various fields from scientific articles, it was possible to obtain a list of individual authors of publications, build bags of words and study their frequency and temporal use, discriminate topics (HUS vs. atypical HUS) in an unsupervised manner, and cross-reference information with a list of FDA-approved drugs. Conclusions: Following the instructions in the tutorial, researchers without programming skills can successfully perform TM on the indexed scientific literature. This methodology, using KNIME, could become a useful tool for performing statistics, analyzing behaviors, following trends, and making forecast related to medical issues. The advantages of TM using KNIME include enabling the integration of scientific information, helping to carry out reviews, and optimizing the management of resources dedicated to basic and clinical research.