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Predicting onset of complications from diabetes: a graph based approach

Diabetes is a significant health concern with more than 30 million Americans living with diabetes. Onset of diabetes increases the risk for various complications, including kidney disease, myocardial infractions, heart failure, stroke, retinopathy, and liver disease. In this paper, we study and pred...

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Detalles Bibliográficos
Autores principales: Thomas, Pamela Bilo, Robertson, Daniel H., Chawla, Nitesh V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6245137/
https://www.ncbi.nlm.nih.gov/pubmed/30581983
http://dx.doi.org/10.1007/s41109-018-0106-z
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author Thomas, Pamela Bilo
Robertson, Daniel H.
Chawla, Nitesh V.
author_facet Thomas, Pamela Bilo
Robertson, Daniel H.
Chawla, Nitesh V.
author_sort Thomas, Pamela Bilo
collection PubMed
description Diabetes is a significant health concern with more than 30 million Americans living with diabetes. Onset of diabetes increases the risk for various complications, including kidney disease, myocardial infractions, heart failure, stroke, retinopathy, and liver disease. In this paper, we study and predict the onset of these complications using a network-based approach by identifying fast and slow progressors. That is, given a patient’s diagnosis of diabetes, we predict the likelihood of developing one or more of the possible complications, and which patients will develop complications quickly. This combination of "if a complication will be developed” with ”how fast it will be developed” can aid the physician in developing better diabetes management program for a given patient.
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spelling pubmed-62451372018-12-20 Predicting onset of complications from diabetes: a graph based approach Thomas, Pamela Bilo Robertson, Daniel H. Chawla, Nitesh V. Appl Netw Sci Research Diabetes is a significant health concern with more than 30 million Americans living with diabetes. Onset of diabetes increases the risk for various complications, including kidney disease, myocardial infractions, heart failure, stroke, retinopathy, and liver disease. In this paper, we study and predict the onset of these complications using a network-based approach by identifying fast and slow progressors. That is, given a patient’s diagnosis of diabetes, we predict the likelihood of developing one or more of the possible complications, and which patients will develop complications quickly. This combination of "if a complication will be developed” with ”how fast it will be developed” can aid the physician in developing better diabetes management program for a given patient. Springer International Publishing 2018-11-15 2018 /pmc/articles/PMC6245137/ /pubmed/30581983 http://dx.doi.org/10.1007/s41109-018-0106-z Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Thomas, Pamela Bilo
Robertson, Daniel H.
Chawla, Nitesh V.
Predicting onset of complications from diabetes: a graph based approach
title Predicting onset of complications from diabetes: a graph based approach
title_full Predicting onset of complications from diabetes: a graph based approach
title_fullStr Predicting onset of complications from diabetes: a graph based approach
title_full_unstemmed Predicting onset of complications from diabetes: a graph based approach
title_short Predicting onset of complications from diabetes: a graph based approach
title_sort predicting onset of complications from diabetes: a graph based approach
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6245137/
https://www.ncbi.nlm.nih.gov/pubmed/30581983
http://dx.doi.org/10.1007/s41109-018-0106-z
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