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A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction
BACKGROUND: Acute myocardial infarction (AMI) is a serious cardiovascular disease, followed by a high readmission rate within 30-days of discharge. Accurate prediction of AMI readmission is a crucial way to identify the high-risk group and optimize the distribution of medical resources. METHODS: In...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7734833/ https://www.ncbi.nlm.nih.gov/pubmed/33317534 http://dx.doi.org/10.1186/s12911-020-01358-w |
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author | Zhang, Zhen Qiu, Hang Li, Weihao Chen, Yucheng |
author_facet | Zhang, Zhen Qiu, Hang Li, Weihao Chen, Yucheng |
author_sort | Zhang, Zhen |
collection | PubMed |
description | BACKGROUND: Acute myocardial infarction (AMI) is a serious cardiovascular disease, followed by a high readmission rate within 30-days of discharge. Accurate prediction of AMI readmission is a crucial way to identify the high-risk group and optimize the distribution of medical resources. METHODS: In this study, we propose a stacking-based model to predict the risk of 30-day unplanned all-cause hospital readmissions for AMI patients based on clinical data. Firstly, we conducted an under-sampling method of neighborhood cleaning rule (NCR) to alleviate the class imbalance and then utilized a feature selection method of SelectFromModel (SFM) to select effective features. Secondly, we adopted a self-adaptive approach to select base classifiers from eight candidate models according to their performances in datasets. Finally, we constructed a three-layer stacking model in which layer 1 and layer 2 were base-layer and level 3 was meta-layer. The predictions of the base-layer were used to train the meta-layer in order to make the final forecast. RESULTS: The results show that the proposed model exhibits the highest AUC (0.720), which is higher than that of decision tree (0.681), support vector machine (0.707), random forest (0.701), extra trees (0.709), adaBoost (0.702), bootstrap aggregating (0.704), gradient boosting decision tree (0.710) and extreme gradient enhancement (0.713). CONCLUSION: It is evident that our model could effectively predict the risk of 30-day all cause hospital readmissions for AMI patients and provide decision support for the administration. |
format | Online Article Text |
id | pubmed-7734833 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-77348332020-12-15 A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction Zhang, Zhen Qiu, Hang Li, Weihao Chen, Yucheng BMC Med Inform Decis Mak Research Article BACKGROUND: Acute myocardial infarction (AMI) is a serious cardiovascular disease, followed by a high readmission rate within 30-days of discharge. Accurate prediction of AMI readmission is a crucial way to identify the high-risk group and optimize the distribution of medical resources. METHODS: In this study, we propose a stacking-based model to predict the risk of 30-day unplanned all-cause hospital readmissions for AMI patients based on clinical data. Firstly, we conducted an under-sampling method of neighborhood cleaning rule (NCR) to alleviate the class imbalance and then utilized a feature selection method of SelectFromModel (SFM) to select effective features. Secondly, we adopted a self-adaptive approach to select base classifiers from eight candidate models according to their performances in datasets. Finally, we constructed a three-layer stacking model in which layer 1 and layer 2 were base-layer and level 3 was meta-layer. The predictions of the base-layer were used to train the meta-layer in order to make the final forecast. RESULTS: The results show that the proposed model exhibits the highest AUC (0.720), which is higher than that of decision tree (0.681), support vector machine (0.707), random forest (0.701), extra trees (0.709), adaBoost (0.702), bootstrap aggregating (0.704), gradient boosting decision tree (0.710) and extreme gradient enhancement (0.713). CONCLUSION: It is evident that our model could effectively predict the risk of 30-day all cause hospital readmissions for AMI patients and provide decision support for the administration. BioMed Central 2020-12-14 /pmc/articles/PMC7734833/ /pubmed/33317534 http://dx.doi.org/10.1186/s12911-020-01358-w Text en © The Author(s) 2020 Open AccessThis 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/. 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 in a credit line to the data. |
spellingShingle | Research Article Zhang, Zhen Qiu, Hang Li, Weihao Chen, Yucheng A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction |
title | A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction |
title_full | A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction |
title_fullStr | A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction |
title_full_unstemmed | A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction |
title_short | A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction |
title_sort | stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7734833/ https://www.ncbi.nlm.nih.gov/pubmed/33317534 http://dx.doi.org/10.1186/s12911-020-01358-w |
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