An ETL Approach to the Generation of Educational Resources From Digital Radiology Medical Collections

Abstract

This chapter describes an ETL (extract, transform, load) approach for the generation of instructional resources from pre-existing collections of clinical data, in particular in the field of radiology. The approach advocates, on the one hand, the extraction of information from such sources and its representation in a unified and machine-processable format. On the other hand, the extracted information can be transformed to fit different instructional needs. This transformation process can involve both automatic transformations and transformations carried out by experts using specialized editors. Finally, the information resulting from the transformation process can be exported in standard formats in order to load it to learning management platforms (e.g., Moodle or Canvas). The chapter illustrates, with the help of a radiology clinical case collection, how this approach can be supported by a flexible digital collection management tool called Clavy.