Semantic Knowledge Base Construction from Radiology Reports

The tremendous quantity of data stored daily in healthcare institutions demands the development of new methods to summarize and reuse available information in clinical practice. In order to leverage modern healthcare information systems, new strategies must be developed that address challenges such as extraction of relevant information, data redundancy, and the lack of associations within the data. This article proposes a pipeline to overcome these challenges in the context of medical imaging reports, by automatically extracting and linking information, and summarizing natural language reports into an ontology model. Using data from the Physionet MIMIC II database, we created a semantic knowledge base with more than 6.5 millions of triples obtained from a collection of 16,000 radiology reports.