Hypergraphs for Frailty Analysis Research Paper

Zoe Hancox, Samuel D. Relton, Andrew Clegg, Philip G. Conaghan, Dan Schofield

Abstract: Frailty and multimorbidity becomes more prevalent as the population continues to age. We employ directed hypergraphs to represent the complex interactions among multiple health conditions and capture the inter-dependencies within multimorbidity sets. We introduce the inclusion of ‘Mortality’ nodes into directed hypergraphs. Through the analysis of ResearchOne data, we aim to identify the most prevalent combinations of frailty conditions alongside their co-occurrence with mortality, providing valuable knowledge for healthcare professionals to improve patient care and develop targeted interventions. We demonstrate that hypergraphs enable us to determine the probability of acquiring another electronic frailty index (eFI) condition, understand condition connectivity and sequentiality, and identify the most influential hyperarcs. The findings from this study suggest that hypergraphs enable us to retain progression information compared to holistic views, facilitating the implementation of more effective healthcare strategies.