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Multimorbidity prediction using link prediction.
Aziz, Furqan; Cardoso, Victor Roth; Bravo-Merodio, Laura; Russ, Dominic; Pendleton, Samantha C; Williams, John A; Acharjee, Animesh; Gkoutos, Georgios V.
Afiliación
  • Aziz F; Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, B15 2TT, UK. f.aziz@bham.ac.uk.
  • Cardoso VR; Institute of Translational Medicine, University of Birmingham, Birmingham, B15 2TT, UK. f.aziz@bham.ac.uk.
  • Bravo-Merodio L; Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, B15 2TT, UK.
  • Russ D; Institute of Translational Medicine, University of Birmingham, Birmingham, B15 2TT, UK.
  • Pendleton SC; Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, B15 2TT, UK.
  • Williams JA; Institute of Translational Medicine, University of Birmingham, Birmingham, B15 2TT, UK.
  • Acharjee A; Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, B15 2TT, UK.
  • Gkoutos GV; Institute of Translational Medicine, University of Birmingham, Birmingham, B15 2TT, UK.
Sci Rep ; 11(1): 16392, 2021 08 12.
Article en En | MEDLINE | ID: mdl-34385524
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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedad Crónica / Multimorbilidad Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Sci Rep Año: 2021 Tipo del documento: Article País de afiliación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedad Crónica / Multimorbilidad Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Sci Rep Año: 2021 Tipo del documento: Article País de afiliación: Reino Unido
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