Learning and visualizing chronic latent representations using electronic health records

Background: Nowadays, patients with chronic diseases such as diabetes and hypertension have reached alarming numbers worldwide. These diseases increase the risk of developing acute complications and involve a substantial economic burden and demand for health resources. The widespread adoption of Ele...

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Autores: Chushig-Muzo, David, Soguero-Ruiz, Cristina, Miguel Bohoyo, Pablo, Mora-Jiménez, Inmaculada
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad Rey Juan Carlos
Repositorio:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
OAI Identifier:oai:burjcdigital.urjc.es:10115/28495
Acceso en línea:https://hdl.handle.net/10115/28495
Access Level:acceso abierto
Palabra clave:Denoising Autoencoder
Chronic diseases
Diabetes
Hypertension
Clustering
Patient representation
Synthetic patient
Health status trajectory
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spelling Learning and visualizing chronic latent representations using electronic health recordsChushig-Muzo, DavidSoguero-Ruiz, CristinaMiguel Bohoyo, PabloMora-Jiménez, InmaculadaDenoising AutoencoderChronic diseasesDiabetesHypertensionClusteringPatient representationSynthetic patientHealth status trajectoryBackground: Nowadays, patients with chronic diseases such as diabetes and hypertension have reached alarming numbers worldwide. These diseases increase the risk of developing acute complications and involve a substantial economic burden and demand for health resources. The widespread adoption of Electronic Health Records (EHRs) is opening great opportunities for supporting decision-making. Nevertheless, data extracted from EHRs are complex (heterogeneous, high-dimensional and usually noisy), hampering the knowledge extraction with conventional approaches. Methods: We propose the use of the Denoising Autoencoder (DAE), a Machine Learning (ML) technique allowing to transform high-dimensional data into latent representations (LRs), thus addressing the main challenges with clinical data. We explore in this work how the combination of LRs with a visualization method can be used to map the patient data in a two-dimensional space, gaining knowledge about the distribution of patients with diferent chronic conditions. Furthermore, this representation can be also used to characterize the patient’s health status evolution, which is of paramount importance in the clinical setting. Results: To obtain clinical LRs, we considered real-world data extracted from EHRs linked to the University Hospital of Fuenlabrada in Spain. Experimental results showed the great potential of DAEs to identify patients with clinical patterns linked to hyper‑ tension, diabetes and multimorbidity. The procedure allowed us to fnd patients with the same main chronic disease but diferent clinical characteristics. Thus, we identifed two kinds of diabetic patients with diferences in their drug therapy (insulin and non-insulin dependant), and also a group of women afected by hypertension and gestational diabetes. We also present a proof of concept for mapping the health status evolution of synthetic patients when considering the most signifcant diagnoses and drugs associated with chronic patients. Conclusion: Our results highlighted the value of ML techniques to extract clinical knowledge, supporting the identifcation of patients with certain chronic conditions. Furthermore, the patient’s health status progression on the two-dimensional space might be used as a tool for clinicians aiming to characterize health conditions and identify their more relevant clinical codes.BMC202420242022info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10115/28495reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlosinstname:Universidad Rey Juan CarlosInglésAtribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:burjcdigital.urjc.es:10115/284952026-06-24T12:48:17Z
dc.title.none.fl_str_mv Learning and visualizing chronic latent representations using electronic health records
title Learning and visualizing chronic latent representations using electronic health records
spellingShingle Learning and visualizing chronic latent representations using electronic health records
Chushig-Muzo, David
Denoising Autoencoder
Chronic diseases
Diabetes
Hypertension
Clustering
Patient representation
Synthetic patient
Health status trajectory
title_short Learning and visualizing chronic latent representations using electronic health records
title_full Learning and visualizing chronic latent representations using electronic health records
title_fullStr Learning and visualizing chronic latent representations using electronic health records
title_full_unstemmed Learning and visualizing chronic latent representations using electronic health records
title_sort Learning and visualizing chronic latent representations using electronic health records
dc.creator.none.fl_str_mv Chushig-Muzo, David
Soguero-Ruiz, Cristina
Miguel Bohoyo, Pablo
Mora-Jiménez, Inmaculada
author Chushig-Muzo, David
author_facet Chushig-Muzo, David
Soguero-Ruiz, Cristina
Miguel Bohoyo, Pablo
Mora-Jiménez, Inmaculada
author_role author
author2 Soguero-Ruiz, Cristina
Miguel Bohoyo, Pablo
Mora-Jiménez, Inmaculada
author2_role author
author
author
dc.subject.none.fl_str_mv Denoising Autoencoder
Chronic diseases
Diabetes
Hypertension
Clustering
Patient representation
Synthetic patient
Health status trajectory
topic Denoising Autoencoder
Chronic diseases
Diabetes
Hypertension
Clustering
Patient representation
Synthetic patient
Health status trajectory
description Background: Nowadays, patients with chronic diseases such as diabetes and hypertension have reached alarming numbers worldwide. These diseases increase the risk of developing acute complications and involve a substantial economic burden and demand for health resources. The widespread adoption of Electronic Health Records (EHRs) is opening great opportunities for supporting decision-making. Nevertheless, data extracted from EHRs are complex (heterogeneous, high-dimensional and usually noisy), hampering the knowledge extraction with conventional approaches. Methods: We propose the use of the Denoising Autoencoder (DAE), a Machine Learning (ML) technique allowing to transform high-dimensional data into latent representations (LRs), thus addressing the main challenges with clinical data. We explore in this work how the combination of LRs with a visualization method can be used to map the patient data in a two-dimensional space, gaining knowledge about the distribution of patients with diferent chronic conditions. Furthermore, this representation can be also used to characterize the patient’s health status evolution, which is of paramount importance in the clinical setting. Results: To obtain clinical LRs, we considered real-world data extracted from EHRs linked to the University Hospital of Fuenlabrada in Spain. Experimental results showed the great potential of DAEs to identify patients with clinical patterns linked to hyper‑ tension, diabetes and multimorbidity. The procedure allowed us to fnd patients with the same main chronic disease but diferent clinical characteristics. Thus, we identifed two kinds of diabetic patients with diferences in their drug therapy (insulin and non-insulin dependant), and also a group of women afected by hypertension and gestational diabetes. We also present a proof of concept for mapping the health status evolution of synthetic patients when considering the most signifcant diagnoses and drugs associated with chronic patients. Conclusion: Our results highlighted the value of ML techniques to extract clinical knowledge, supporting the identifcation of patients with certain chronic conditions. Furthermore, the patient’s health status progression on the two-dimensional space might be used as a tool for clinicians aiming to characterize health conditions and identify their more relevant clinical codes.
publishDate 2022
dc.date.none.fl_str_mv 2022
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10115/28495
url https://hdl.handle.net/10115/28495
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv BMC
publisher.none.fl_str_mv BMC
dc.source.none.fl_str_mv reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
instname:Universidad Rey Juan Carlos
instname_str Universidad Rey Juan Carlos
reponame_str BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
collection BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
repository.name.fl_str_mv
repository.mail.fl_str_mv
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