Multimorbidity prediction using link prediction

Furqan Aziz, Victor Roth Cardoso, Laura Bravo-Merodio, Dominic Russ, Samantha Pendleton, John Williams, Animesh Acharjee, Georgios Gkoutos

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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.
Original languageEnglish
Article number16392
JournalScientific Reports
Volume11
Issue number1
DOIs
Publication statusPublished - 12 Aug 2021

Bibliographical note

Funding Information:
The authors acknowledge support from the NIHR Birmingham ECMC, NIHR Birmingham SRMRC, Nanocom-mons H2020-EU (731032) and the NIHR Birmingham Biomedical Research Centre and the MRC Heath Data Research UK (HDRUK/CFC/01), an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Medical Research Council or the Department of Health.

Publisher Copyright:
© 2021, The Author(s).

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