KETO: a knowledge editing tool for encoding condition-action guidelines into clinical DSSs

Clinical practice guidelines are expected to promote more consistent, effective, and efficient medical practices, especially if implemented in clinical Decision Support Systems (DSSs). One prerequisite for the broad acceptance of clinical DSSs and their efficient application to medical settings is the guarantee of a high level of upgradability and maintainability. In this respect, this paper proposes KETO (Knowledge Editing TOol), a user-friendly tool to guide and assist the editing and formalization of condition-action clinical recommendations into a hybrid Knowledge Base (KB), made of if-then rules built on the top of ontological vocabularies, to be then used in a clinical DSS. The tool aims at: i) synergistically combining multiple knowledge representation techniques for building efficient DSSs able to deal with different clinical problems; ii) reducing the complexity of the formalization process, by enabling the creation and automatic encoding into machine executable languages of hybrid KBs that could be functional in the context of clinical DSSs.