Multimorbidity prediction using link prediction

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 r...

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Autores principales:	Furqan Aziz, Victor Roth Cardoso, Laura Bravo-Merodio, Dominic Russ, Samantha C. Pendleton, John A. Williams, Animesh Acharjee, Georgios V. Gkoutos
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3345541b904c4ba69daa4518344daf8f
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spelling	oai:doaj.org-article:3345541b904c4ba69daa4518344daf8f2021-12-02T15:08:10ZMultimorbidity prediction using link prediction10.1038/s41598-021-95802-02045-2322https://doaj.org/article/3345541b904c4ba69daa4518344daf8f2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95802-0https://doaj.org/toc/2045-2322Abstract 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.Furqan AzizVictor Roth CardosoLaura Bravo-MerodioDominic RussSamantha C. PendletonJohn A. WilliamsAnimesh AcharjeeGeorgios V. GkoutosNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Furqan Aziz Victor Roth Cardoso Laura Bravo-Merodio Dominic Russ Samantha C. Pendleton John A. Williams Animesh Acharjee Georgios V. Gkoutos Multimorbidity prediction using link prediction
description	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.
format	article
author	Furqan Aziz Victor Roth Cardoso Laura Bravo-Merodio Dominic Russ Samantha C. Pendleton John A. Williams Animesh Acharjee Georgios V. Gkoutos
author_facet	Furqan Aziz Victor Roth Cardoso Laura Bravo-Merodio Dominic Russ Samantha C. Pendleton John A. Williams Animesh Acharjee Georgios V. Gkoutos
author_sort	Furqan Aziz
title	Multimorbidity prediction using link prediction
title_short	Multimorbidity prediction using link prediction
title_full	Multimorbidity prediction using link prediction
title_fullStr	Multimorbidity prediction using link prediction
title_full_unstemmed	Multimorbidity prediction using link prediction
title_sort	multimorbidity prediction using link prediction
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3345541b904c4ba69daa4518344daf8f
work_keys_str_mv	AT furqanaziz multimorbiditypredictionusinglinkprediction AT victorrothcardoso multimorbiditypredictionusinglinkprediction AT laurabravomerodio multimorbiditypredictionusinglinkprediction AT dominicruss multimorbiditypredictionusinglinkprediction AT samanthacpendleton multimorbiditypredictionusinglinkprediction AT johnawilliams multimorbiditypredictionusinglinkprediction AT animeshacharjee multimorbiditypredictionusinglinkprediction AT georgiosvgkoutos multimorbiditypredictionusinglinkprediction
_version_	1718388242953273344

Multimorbidity prediction using link prediction

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