A knowledge graph representation of baseline characteristics for the Dutch proton therapy research registry. (November 2021) - Sloep, Matthijs; Kalendralis, Petros; Choudhury, Ananya; Seyben, Lerau; Snel, Jasper; George, Nibin Moni; Veening, Martijn; Langendijk, Johannes A.; Dekker, Andre; van Soest, Johan; Fijten, Rianne
Highlights: Real world data enables model based patient selection and continuous validation. Multisource data selection requires an astute collection and stewardship strategy. Innovations need a flexible data model to facilitate collection between centres. The FAIR principles and knowledge graphs tackle specific data model requirements. Abstract: Cancer registries collect multisource data and provide valuable information that can lead to unique research opportunities. In the Netherlands, a registry and model-based approach (MBA) are used for the selection of patients that are eligible for proton therapy. We collected baseline characteristics including demographic, clinical, tumour and treatment information. These data were transformed into a machine readable format using the FAIR (Findable, Accessible, Interoperable, Reusable) data principles and resulted in a knowledge graph with baseline characteristics of proton therapy patients. With this approach, we enable the possibility of linking external data sources and optimal flexibility to easily adapt the data structure of the existing knowledge graph to the needs of the clinic.