Ranking sets of morbidities using hypergraph centrality.

ABSTRACT: Multi-morbidity, the health state of having two or more concurrent chronic conditions, is becoming more common as populations age, but is poorly understood. Identifying and understanding commonly occurring sets of diseases is important to inform clinical decisions to improve patient services and outcomes. Network analysis has been previously used to investigate multi-morbidity, but a classic application only allows for information on binary sets of diseases to contribute to the graph. We propose the use of hypergraphs, which allows for the incorporation of data on people with any number of conditions, and also allows us to obtain a quantitative understanding of the centrality, a measure of how well connected items in the network are to each other, of both single diseases and sets of condit

SUBMITTER: Rafferty J 

PROVIDER: S-EPMC8524321 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature