An efficient approach towards the generation and analysis of interoperable clinical data in a knowledge graph

Dörpinghaus, Jens; Weil, Vera; Schaaf, Sebastian; Hubenthal, Tobias

doi:10.15439/2021f29

Cited by 1 publication

(1 citation statement)

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“…Their research is based on biomedical knowledge graphs as described in [8] containing mainly scientific documents and extracted data from PubMed. In [18] this approach was generalised to achieve interoperability with clinical data.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

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

Dörpinghaus¹,

Hübenthal²,

Faber³

2022

Annals of Computer Science and Information Systems

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This paper presents a novel approach towards link prediction in clinical knowledge graphs. They play a central role in 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 built 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 patientnodes usually common methods for link prediction and graph embeddings are problematic and thus we will (3) present a novel approach for link prediction utilising 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.

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Section: Related Work and Backgroundmentioning

confidence: 99%