Towards an Ontology Representing Characteristics of Inflammatory Bowel Disease.

Inflammatory bowel disease (IBD) is a chronic disease characterized by numerous, hard to predict periods of relapse and remission. "Digital twin" approaches, leveraging personalized predictive models, would significantly enhance therapeutic decision-making and cost-effectiveness. However, the associated computational and statistical methods require high quality data from a large population of patients. Such a comprehensive repository is very challenging to build, though, and none is available for IBD. To overcome this, a promising approach is to employ a knowledge graph, which is built from the available data and would help predicting IBD episodes and delivering more relevant personalized therapy at the lowest cost. In this research, we present a knowledge graph developed on the basis of patient records which are collected from one of the largest German gastroentologic outpatient clinic. First, we designed IBD ontology that encompasses the vocabulary, specifications and characteristics associated by physicians with IBD patients, such as disease classification schemas (e.g., Montreal Classification of IBD), status of the disease activity, and medications. Next, we defined the mappings between ontology entities and database variables. Physicians and project members participating in the Fraunhofer MED2ICIN project, validated the ontology and the knowledge graph. Furthermore, the knowledge graph has been validated against the competency questions compiled by physicians.