Exploring biomedical records through text mining-driven complex data visualisation

The recent events in health call for the prioritization of insightful and meaningful information retrieval from the fastly growing pool of biomedical knowledge. This information has its own challenges both in the data itself and in its appropriate representation, enhancing its usability by health professionals. In this paper we present a framework leveraging the MEDLINE dataset and its controlled vocabulary, the MeSH Headings, to annotate and explore health-related documents. The MEDijs system ingests and automatically annotates text documents, extending their legacy metadata with MeSH Headings. It then uses text mining algorithms that enable interactive data visualisations. These allow the user to the exploration of the enriched data made available by the MEDijs system. CCS CONCEPTS • Information systems; • Computing methodologies → Machine learning approaches; ACM Reference Format Joao Pita Costa, Luka Stopar, Luis Rei, Besher Massri, and Marko Grobelnik. 2018. Exploring biomedical records through text mining-driven complex data visualisation. In Proceedings of SEBILAN ’21: ACM International Workshop on Semantics-enabled Biomedical Literature Analytics (SEBILAN ’21). ACM, New York, NY, USA, 6 pages. https://doi.org/0