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A Machine Learning Approach for Studying the Comorbidities of Complex Diagnoses
The study of diagnostic associations entails a large number of methodological problems regarding the application of machine learning algorithms, collinearity and wide variability being some of the most prominent ones. To overcome these, we propose and tested the usage of uniform manifold approximati...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960661/ https://www.ncbi.nlm.nih.gov/pubmed/31766665 http://dx.doi.org/10.3390/bs9120122 |
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author | Sánchez-Rico, Marina Alvarado, Jesús M. |
author_facet | Sánchez-Rico, Marina Alvarado, Jesús M. |
author_sort | Sánchez-Rico, Marina |
collection | PubMed |
description | The study of diagnostic associations entails a large number of methodological problems regarding the application of machine learning algorithms, collinearity and wide variability being some of the most prominent ones. To overcome these, we propose and tested the usage of uniform manifold approximation and projection (UMAP), a very recent, popular dimensionality reduction technique. We showed its effectiveness by using it on a large Spanish clinical database of patients diagnosed with depression, to whom we applied UMAP before grouping them using a hierarchical agglomerative cluster analysis. By extensively studying its behavior and results, validating them with purely unsupervised metrics, we show that they are consistent with well-known relationships, which validates the applicability of UMAP to advance the study of comorbidities. |
format | Online Article Text |
id | pubmed-6960661 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-69606612020-01-23 A Machine Learning Approach for Studying the Comorbidities of Complex Diagnoses Sánchez-Rico, Marina Alvarado, Jesús M. Behav Sci (Basel) Article The study of diagnostic associations entails a large number of methodological problems regarding the application of machine learning algorithms, collinearity and wide variability being some of the most prominent ones. To overcome these, we propose and tested the usage of uniform manifold approximation and projection (UMAP), a very recent, popular dimensionality reduction technique. We showed its effectiveness by using it on a large Spanish clinical database of patients diagnosed with depression, to whom we applied UMAP before grouping them using a hierarchical agglomerative cluster analysis. By extensively studying its behavior and results, validating them with purely unsupervised metrics, we show that they are consistent with well-known relationships, which validates the applicability of UMAP to advance the study of comorbidities. MDPI 2019-11-22 /pmc/articles/PMC6960661/ /pubmed/31766665 http://dx.doi.org/10.3390/bs9120122 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sánchez-Rico, Marina Alvarado, Jesús M. A Machine Learning Approach for Studying the Comorbidities of Complex Diagnoses |
title | A Machine Learning Approach for Studying the Comorbidities of Complex Diagnoses |
title_full | A Machine Learning Approach for Studying the Comorbidities of Complex Diagnoses |
title_fullStr | A Machine Learning Approach for Studying the Comorbidities of Complex Diagnoses |
title_full_unstemmed | A Machine Learning Approach for Studying the Comorbidities of Complex Diagnoses |
title_short | A Machine Learning Approach for Studying the Comorbidities of Complex Diagnoses |
title_sort | machine learning approach for studying the comorbidities of complex diagnoses |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960661/ https://www.ncbi.nlm.nih.gov/pubmed/31766665 http://dx.doi.org/10.3390/bs9120122 |
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