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Applied Machine Learning for the Prediction of Growth of Abdominal Aortic Aneurysm in Humans
OBJECTIVE: Accurate prediction of abdominal aortic aneurysm (AAA) growth in an individual can allow personalised stratification of surveillance intervals and better inform the timing for surgery. The authors recently described the novel significant association between flow mediated dilatation (FMD)...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6033055/ https://www.ncbi.nlm.nih.gov/pubmed/29988820 http://dx.doi.org/10.1016/j.ejvssr.2018.03.004 |
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author | Lee, R. Jarchi, D. Perera, R. Jones, A. Cassimjee, I. Handa, A. Clifton, D.A. |
author_facet | Lee, R. Jarchi, D. Perera, R. Jones, A. Cassimjee, I. Handa, A. Clifton, D.A. |
author_sort | Lee, R. |
collection | PubMed |
description | OBJECTIVE: Accurate prediction of abdominal aortic aneurysm (AAA) growth in an individual can allow personalised stratification of surveillance intervals and better inform the timing for surgery. The authors recently described the novel significant association between flow mediated dilatation (FMD) and future AAA growth. The feasibility of predicting future AAA growth was explored in individual patients using a set of benchmark machine learning techniques. METHODS: The Oxford Abdominal Aortic Aneurysm Study (OxAAA) prospectively recruited AAA patients undergoing the routine NHS management pathway. In addition to the AAA diameter, FMD was systemically measured in these patients. A benchmark machine learning technique (non-linear Kernel support vector regression) was applied to predict future AAA growth in individual patients, using their baseline FMD and AAA diameter as input variables. RESULTS: Prospective growth data were recorded at 12 months (360 ± 49 days) in 94 patients. Of these, growth data were further recorded at 24 months (718 ± 81 days) in 79 patients. The average growth in AAA diameter was 3.4% at 12 months, and 2.8% per year at 24 months. The algorithm predicted the individual's AAA diameter to within 2 mm error in 85% and 71% of patients at 12 and 24 months. CONCLUSIONS: The data highlight the utility of FMD as a biomarker for AAA and the value of machine learning techniques for AAA research in the new era of precision medicine. |
format | Online Article Text |
id | pubmed-6033055 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-60330552018-07-09 Applied Machine Learning for the Prediction of Growth of Abdominal Aortic Aneurysm in Humans Lee, R. Jarchi, D. Perera, R. Jones, A. Cassimjee, I. Handa, A. Clifton, D.A. EJVES Short Rep Original Research OBJECTIVE: Accurate prediction of abdominal aortic aneurysm (AAA) growth in an individual can allow personalised stratification of surveillance intervals and better inform the timing for surgery. The authors recently described the novel significant association between flow mediated dilatation (FMD) and future AAA growth. The feasibility of predicting future AAA growth was explored in individual patients using a set of benchmark machine learning techniques. METHODS: The Oxford Abdominal Aortic Aneurysm Study (OxAAA) prospectively recruited AAA patients undergoing the routine NHS management pathway. In addition to the AAA diameter, FMD was systemically measured in these patients. A benchmark machine learning technique (non-linear Kernel support vector regression) was applied to predict future AAA growth in individual patients, using their baseline FMD and AAA diameter as input variables. RESULTS: Prospective growth data were recorded at 12 months (360 ± 49 days) in 94 patients. Of these, growth data were further recorded at 24 months (718 ± 81 days) in 79 patients. The average growth in AAA diameter was 3.4% at 12 months, and 2.8% per year at 24 months. The algorithm predicted the individual's AAA diameter to within 2 mm error in 85% and 71% of patients at 12 and 24 months. CONCLUSIONS: The data highlight the utility of FMD as a biomarker for AAA and the value of machine learning techniques for AAA research in the new era of precision medicine. Elsevier 2018-05-01 /pmc/articles/PMC6033055/ /pubmed/29988820 http://dx.doi.org/10.1016/j.ejvssr.2018.03.004 Text en © 2018 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Original Research Lee, R. Jarchi, D. Perera, R. Jones, A. Cassimjee, I. Handa, A. Clifton, D.A. Applied Machine Learning for the Prediction of Growth of Abdominal Aortic Aneurysm in Humans |
title | Applied Machine Learning for the Prediction of Growth of Abdominal Aortic Aneurysm in Humans |
title_full | Applied Machine Learning for the Prediction of Growth of Abdominal Aortic Aneurysm in Humans |
title_fullStr | Applied Machine Learning for the Prediction of Growth of Abdominal Aortic Aneurysm in Humans |
title_full_unstemmed | Applied Machine Learning for the Prediction of Growth of Abdominal Aortic Aneurysm in Humans |
title_short | Applied Machine Learning for the Prediction of Growth of Abdominal Aortic Aneurysm in Humans |
title_sort | applied machine learning for the prediction of growth of abdominal aortic aneurysm in humans |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6033055/ https://www.ncbi.nlm.nih.gov/pubmed/29988820 http://dx.doi.org/10.1016/j.ejvssr.2018.03.004 |
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