Multimorbidity prediction using link prediction

• Main Authors: Furqan Aziz, Victor Roth Cardoso, Laura Bravo-Merodio, Dominic Russ, Samantha C. Pendleton, John A. Williams, Animesh Acharjee, Georgios V. Gkoutos
• Format: Article
• Language: English
• Published: Nature Publishing Group 2021-08-01
• Series: Scientific Reports
• Online Access: https://doi.org/10.1038/s41598-021-95802-0

Abstract Multimorbidity, frequently associated with aging, can be operationally defined as the presence of two or more chronic conditions. Predicting the likelihood of a patient with multimorbidity to develop a further particular disease in the future is one of the key challenges in multimorbidity research. In this paper we are using a network-based approach to analyze multimorbidity data and develop methods for predicting diseases that a patient is likely to develop. The multimorbidity data is represented using a temporal bipartite network whose nodes represent patients and diseases and a link between these nodes indicates that the patient has been diagnosed with the disease. Disease prediction then is reduced to a problem of predicting those missing links in the network that are likely to appear in the future. We develop a novel link prediction method for static bipartite network and validate the performance of the method on benchmark datasets. By using a probabilistic framework, we then report on the development of a method for predicting future links in the network, where links are labelled with a time-stamp. We apply the proposed method to three different multimorbidity datasets and report its performance measured by different performance metrics including AUC, Precision, Recall, and F-Score.