A novel link prediction approach on clinical knowledge graphs utilizing graph structures

• Autorzy: Dörpinghaus Jens , Hübenthal Tobias , Faber Jennifer

Identyfikatory

DOI

10.15439/2022F36

• Języki publikacji: EN

Abstrakty

EN

This paper presents a novel approach towards link prediction in clinical knowledge graphs. They play a central role for linking data from different data sources and are widely used in big data integration, especially for connecting data from different domains. We present a knowledge graph initially build on data from a clinical trial on Spinocerebellar ataxia type 3 (SCA3), which is a rare autosomal dominant inherited disorder. The contributions of this paper are (1) to create a feasible data representation schema capable of handling clinical imaging data in a knowledge graph and to (2) convert the data efficiently into a knowledge graph. Due to the limited amount of patient-nodes usually common methods for link prediction and graph embeddings are problematic and thus we will (3) present a novel approach for link prediction utilizing graph structures and Conditional Random Fields. In addition, we present (4) an extensive evaluation underlining the importance of (a) data management and (b) further research on link prediction using graph structures.

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 30
• Strony: 43--52
• Opis fizyczny: Bibliogr. 24 poz.

Twórcy

autor

Dörpinghaus Jens

autor

Hübenthal Tobias

autor

Faber Jennifer