A Knowledge graph representation of baseline characteristics for the Dutch proton therapy research registry

TL;DR

This paper presents a knowledge graph that encodes baseline characteristics of Dutch proton therapy patients, facilitating data integration, flexibility, and research opportunities in cancer registries.

Contribution

It introduces a novel knowledge graph representation of patient data adhering to FAIR principles, enhancing data interoperability and adaptability for clinical research.

Findings

Created a FAIR-compliant knowledge graph of patient characteristics

Enabled linking of external data sources with the registry

Provided a flexible data structure adaptable to clinical needs

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.