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Multiscale classification of heart failure phenotypes by unsupervised clustering of unstructured electronic medical record data

As a leading cause of death and morbidity, heart failure (HF) is responsible for a large portion of healthcare and disability costs worldwide. Current approaches to define specific HF subpopulations may fail to account for the diversity of etiologies, comorbidities, and factors driving disease progr...

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Autores principales: Nagamine, Tasha, Gillette, Brian, Pakhomov, Alexey, Kahoun, John, Mayer, Hannah, Burghaus, Rolf, Lippert, Jörg, Saxena, Mayur
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7721729/
https://www.ncbi.nlm.nih.gov/pubmed/33288774
http://dx.doi.org/10.1038/s41598-020-77286-6
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author Nagamine, Tasha
Gillette, Brian
Pakhomov, Alexey
Kahoun, John
Mayer, Hannah
Burghaus, Rolf
Lippert, Jörg
Saxena, Mayur
author_facet Nagamine, Tasha
Gillette, Brian
Pakhomov, Alexey
Kahoun, John
Mayer, Hannah
Burghaus, Rolf
Lippert, Jörg
Saxena, Mayur
author_sort Nagamine, Tasha
collection PubMed
description As a leading cause of death and morbidity, heart failure (HF) is responsible for a large portion of healthcare and disability costs worldwide. Current approaches to define specific HF subpopulations may fail to account for the diversity of etiologies, comorbidities, and factors driving disease progression, and therefore have limited value for clinical decision making and development of novel therapies. Here we present a novel and data-driven approach to understand and characterize the real-world manifestation of HF by clustering disease and symptom-related clinical concepts (complaints) captured from unstructured electronic health record clinical notes. We used natural language processing to construct vectorized representations of patient complaints followed by clustering to group HF patients by similarity of complaint vectors. We then identified complaints that were significantly enriched within each cluster using statistical testing. Breaking the HF population into groups of similar patients revealed a clinically interpretable hierarchy of subgroups characterized by similar HF manifestation. Importantly, our methodology revealed well-known etiologies, risk factors, and comorbid conditions of HF (including ischemic heart disease, aortic valve disease, atrial fibrillation, congenital heart disease, various cardiomyopathies, obesity, hypertension, diabetes, and chronic kidney disease) and yielded additional insights into the details of each HF subgroup’s clinical manifestation of HF. Our approach is entirely hypothesis free and can therefore be readily applied for discovery of novel insights in alternative diseases or patient populations.
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spelling pubmed-77217292020-12-08 Multiscale classification of heart failure phenotypes by unsupervised clustering of unstructured electronic medical record data Nagamine, Tasha Gillette, Brian Pakhomov, Alexey Kahoun, John Mayer, Hannah Burghaus, Rolf Lippert, Jörg Saxena, Mayur Sci Rep Article As a leading cause of death and morbidity, heart failure (HF) is responsible for a large portion of healthcare and disability costs worldwide. Current approaches to define specific HF subpopulations may fail to account for the diversity of etiologies, comorbidities, and factors driving disease progression, and therefore have limited value for clinical decision making and development of novel therapies. Here we present a novel and data-driven approach to understand and characterize the real-world manifestation of HF by clustering disease and symptom-related clinical concepts (complaints) captured from unstructured electronic health record clinical notes. We used natural language processing to construct vectorized representations of patient complaints followed by clustering to group HF patients by similarity of complaint vectors. We then identified complaints that were significantly enriched within each cluster using statistical testing. Breaking the HF population into groups of similar patients revealed a clinically interpretable hierarchy of subgroups characterized by similar HF manifestation. Importantly, our methodology revealed well-known etiologies, risk factors, and comorbid conditions of HF (including ischemic heart disease, aortic valve disease, atrial fibrillation, congenital heart disease, various cardiomyopathies, obesity, hypertension, diabetes, and chronic kidney disease) and yielded additional insights into the details of each HF subgroup’s clinical manifestation of HF. Our approach is entirely hypothesis free and can therefore be readily applied for discovery of novel insights in alternative diseases or patient populations. Nature Publishing Group UK 2020-12-07 /pmc/articles/PMC7721729/ /pubmed/33288774 http://dx.doi.org/10.1038/s41598-020-77286-6 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Nagamine, Tasha
Gillette, Brian
Pakhomov, Alexey
Kahoun, John
Mayer, Hannah
Burghaus, Rolf
Lippert, Jörg
Saxena, Mayur
Multiscale classification of heart failure phenotypes by unsupervised clustering of unstructured electronic medical record data
title Multiscale classification of heart failure phenotypes by unsupervised clustering of unstructured electronic medical record data
title_full Multiscale classification of heart failure phenotypes by unsupervised clustering of unstructured electronic medical record data
title_fullStr Multiscale classification of heart failure phenotypes by unsupervised clustering of unstructured electronic medical record data
title_full_unstemmed Multiscale classification of heart failure phenotypes by unsupervised clustering of unstructured electronic medical record data
title_short Multiscale classification of heart failure phenotypes by unsupervised clustering of unstructured electronic medical record data
title_sort multiscale classification of heart failure phenotypes by unsupervised clustering of unstructured electronic medical record data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7721729/
https://www.ncbi.nlm.nih.gov/pubmed/33288774
http://dx.doi.org/10.1038/s41598-020-77286-6
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