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A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data

BACKGROUND: Heart failure is one of the leading causes of hospitalization in the United States. Advances in big data solutions allow for storage, management, and mining of large volumes of structured and semi-structured data, such as complex healthcare data. Applying these advances to complex health...

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Autores principales: Golas, Sara Bersche, Shibahara, Takuma, Agboola, Stephen, Otaki, Hiroko, Sato, Jumpei, Nakae, Tatsuya, Hisamitsu, Toru, Kojima, Go, Felsted, Jennifer, Kakarmath, Sujay, Kvedar, Joseph, Jethwani, Kamal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6013959/
https://www.ncbi.nlm.nih.gov/pubmed/29929496
http://dx.doi.org/10.1186/s12911-018-0620-z
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author Golas, Sara Bersche
Shibahara, Takuma
Agboola, Stephen
Otaki, Hiroko
Sato, Jumpei
Nakae, Tatsuya
Hisamitsu, Toru
Kojima, Go
Felsted, Jennifer
Kakarmath, Sujay
Kvedar, Joseph
Jethwani, Kamal
author_facet Golas, Sara Bersche
Shibahara, Takuma
Agboola, Stephen
Otaki, Hiroko
Sato, Jumpei
Nakae, Tatsuya
Hisamitsu, Toru
Kojima, Go
Felsted, Jennifer
Kakarmath, Sujay
Kvedar, Joseph
Jethwani, Kamal
author_sort Golas, Sara Bersche
collection PubMed
description BACKGROUND: Heart failure is one of the leading causes of hospitalization in the United States. Advances in big data solutions allow for storage, management, and mining of large volumes of structured and semi-structured data, such as complex healthcare data. Applying these advances to complex healthcare data has led to the development of risk prediction models to help identify patients who would benefit most from disease management programs in an effort to reduce readmissions and healthcare cost, but the results of these efforts have been varied. The primary aim of this study was to develop a 30-day readmission risk prediction model for heart failure patients discharged from a hospital admission. METHODS: We used longitudinal electronic medical record data of heart failure patients admitted within a large healthcare system. Feature vectors included structured demographic, utilization, and clinical data, as well as selected extracts of un-structured data from clinician-authored notes. The risk prediction model was developed using deep unified networks (DUNs), a new mesh-like network structure of deep learning designed to avoid over-fitting. The model was validated with 10-fold cross-validation and results compared to models based on logistic regression, gradient boosting, and maxout networks. Overall model performance was assessed using concordance statistic. We also selected a discrimination threshold based on maximum projected cost saving to the Partners Healthcare system. RESULTS: Data from 11,510 patients with 27,334 admissions and 6369 30-day readmissions were used to train the model. After data processing, the final model included 3512 variables. The DUNs model had the best performance after 10-fold cross-validation. AUCs for prediction models were 0.664 ± 0.015, 0.650 ± 0.011, 0.695 ± 0.016 and 0.705 ± 0.015 for logistic regression, gradient boosting, maxout networks, and DUNs respectively. The DUNs model had an accuracy of 76.4% at the classification threshold that corresponded with maximum cost saving to the hospital. CONCLUSIONS: Deep learning techniques performed better than other traditional techniques in developing this EMR-based prediction model for 30-day readmissions in heart failure patients. Such models can be used to identify heart failure patients with impending hospitalization, enabling care teams to target interventions at their most high-risk patients and improving overall clinical outcomes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12911-018-0620-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-60139592018-07-05 A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data Golas, Sara Bersche Shibahara, Takuma Agboola, Stephen Otaki, Hiroko Sato, Jumpei Nakae, Tatsuya Hisamitsu, Toru Kojima, Go Felsted, Jennifer Kakarmath, Sujay Kvedar, Joseph Jethwani, Kamal BMC Med Inform Decis Mak Research Article BACKGROUND: Heart failure is one of the leading causes of hospitalization in the United States. Advances in big data solutions allow for storage, management, and mining of large volumes of structured and semi-structured data, such as complex healthcare data. Applying these advances to complex healthcare data has led to the development of risk prediction models to help identify patients who would benefit most from disease management programs in an effort to reduce readmissions and healthcare cost, but the results of these efforts have been varied. The primary aim of this study was to develop a 30-day readmission risk prediction model for heart failure patients discharged from a hospital admission. METHODS: We used longitudinal electronic medical record data of heart failure patients admitted within a large healthcare system. Feature vectors included structured demographic, utilization, and clinical data, as well as selected extracts of un-structured data from clinician-authored notes. The risk prediction model was developed using deep unified networks (DUNs), a new mesh-like network structure of deep learning designed to avoid over-fitting. The model was validated with 10-fold cross-validation and results compared to models based on logistic regression, gradient boosting, and maxout networks. Overall model performance was assessed using concordance statistic. We also selected a discrimination threshold based on maximum projected cost saving to the Partners Healthcare system. RESULTS: Data from 11,510 patients with 27,334 admissions and 6369 30-day readmissions were used to train the model. After data processing, the final model included 3512 variables. The DUNs model had the best performance after 10-fold cross-validation. AUCs for prediction models were 0.664 ± 0.015, 0.650 ± 0.011, 0.695 ± 0.016 and 0.705 ± 0.015 for logistic regression, gradient boosting, maxout networks, and DUNs respectively. The DUNs model had an accuracy of 76.4% at the classification threshold that corresponded with maximum cost saving to the hospital. CONCLUSIONS: Deep learning techniques performed better than other traditional techniques in developing this EMR-based prediction model for 30-day readmissions in heart failure patients. Such models can be used to identify heart failure patients with impending hospitalization, enabling care teams to target interventions at their most high-risk patients and improving overall clinical outcomes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12911-018-0620-z) contains supplementary material, which is available to authorized users. BioMed Central 2018-06-22 /pmc/articles/PMC6013959/ /pubmed/29929496 http://dx.doi.org/10.1186/s12911-018-0620-z Text en © The Author(s). 2018 Open AccessThis 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. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Golas, Sara Bersche
Shibahara, Takuma
Agboola, Stephen
Otaki, Hiroko
Sato, Jumpei
Nakae, Tatsuya
Hisamitsu, Toru
Kojima, Go
Felsted, Jennifer
Kakarmath, Sujay
Kvedar, Joseph
Jethwani, Kamal
A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data
title A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data
title_full A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data
title_fullStr A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data
title_full_unstemmed A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data
title_short A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data
title_sort machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6013959/
https://www.ncbi.nlm.nih.gov/pubmed/29929496
http://dx.doi.org/10.1186/s12911-018-0620-z
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