• Ricardo A Dorr, Claudia Silberstein, Cristina Ibarra, and Roxana Toriano.
• Universidad de Buenos Aires, CONICET, Instituto de Fisiología y Biofísica Bernardo Houssay (IFIBIO Houssay), Laboratorio de Biomembranas, Buenos Aires, Argentina.
• Medicina (B Aires). 2022 Jan 1; 82 (4): 513-524.

AbstractHemolytic uremic syndrome (HUS) is characterized by thrombotic microangiopathy, hemolytic anemia, thrombocytopenia and acute renal failure. It can cause from permanent sequelae to death, mainly in children. In this work, using text mining (TM), we analyzed the explicit and implicit text of 16 192 original scientific articles on HUS indexed in the Europe PMC database. The objectives were to examine behaviors, track trends, and make predictions and cross-check data with other sources of information. For the analysis we used -among other computational tools- specially developed workflows (WF) in the KNIME platform. The TM on the words of the abstracts of the publications made it possible to: detect undescribed associations between events related to HUS; extract underlying information; make thematic clustering using unsupervised algorithms; make forecasting about the course of research associated with the topic. Both the approach and the WFs developed to perform Data Science on HUS can be applied to other biomedical topics and other scientific databases, making it possible to analyze relevant aspects in the field of human health to improve research, prevention and treatment of multiples diseases.

11 keywords...

hide…