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The 30-days hospital readmission risk in diabetic patients: predictive modeling with machine learning classifiers
BACKGROUND AND OBJECTIVES: Diabetes mellitus is a major chronic disease that results in readmissions due to poor disease control. Here we established and compared machine learning (ML)-based readmission prediction methods to predict readmission risks of diabetic patients. METHODS: The dataset analyz...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323261/ https://www.ncbi.nlm.nih.gov/pubmed/34330267 http://dx.doi.org/10.1186/s12911-021-01423-y |
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author | Shang, Yujuan Jiang, Kui Wang, Lei Zhang, Zheqing Zhou, Siwei Liu, Yun Dong, Jiancheng Wu, Huiqun |
author_facet | Shang, Yujuan Jiang, Kui Wang, Lei Zhang, Zheqing Zhou, Siwei Liu, Yun Dong, Jiancheng Wu, Huiqun |
author_sort | Shang, Yujuan |
collection | PubMed |
description | BACKGROUND AND OBJECTIVES: Diabetes mellitus is a major chronic disease that results in readmissions due to poor disease control. Here we established and compared machine learning (ML)-based readmission prediction methods to predict readmission risks of diabetic patients. METHODS: The dataset analyzed in this study was acquired from the Health Facts Database, which includes over 100,000 records of diabetic patients from 1999 to 2008. The basic data distribution characteristics of this dataset were summarized and then analyzed. In this study, 30-days readmission was defined as a readmission period of less than 30 days. After data preprocessing and normalization, multiple risk factors in the dataset were examined for classifier training to predict the probability of readmission using ML models. Different ML classifiers such as random forest, Naive Bayes, and decision tree ensemble were adopted to improve the clinical efficiency of the classification. In this study, the Konstanz Information Miner platform was used to preprocess and model the data, and the performances of the different classifiers were compared. RESULTS: A total of 100,244 records were included in the model construction after the data preprocessing and normalization. A total of 23 attributes, including race, sex, age, admission type, admission location, length of stay, and drug use, were finally identified as modeling risk factors. Comparison of the performance indexes of the three algorithms revealed that the RF model had the best performance with a higher area under receiver operating characteristic curve (AUC) than the other two algorithms, suggesting that its use is more suitable for making readmission predictions. CONCLUSION: The factors influencing 30-days readmission predictions in diabetic patients, including number of inpatient admissions, age, diagnosis, number of emergencies, and sex, would help healthcare providers to identify patients who are at high risk of short-term readmission and reduce the probability of 30-days readmission. The RF algorithm with the highest AUC is more suitable for making 30-days readmission predictions and deserves further validation in clinical trials. |
format | Online Article Text |
id | pubmed-8323261 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-83232612021-07-30 The 30-days hospital readmission risk in diabetic patients: predictive modeling with machine learning classifiers Shang, Yujuan Jiang, Kui Wang, Lei Zhang, Zheqing Zhou, Siwei Liu, Yun Dong, Jiancheng Wu, Huiqun BMC Med Inform Decis Mak Research BACKGROUND AND OBJECTIVES: Diabetes mellitus is a major chronic disease that results in readmissions due to poor disease control. Here we established and compared machine learning (ML)-based readmission prediction methods to predict readmission risks of diabetic patients. METHODS: The dataset analyzed in this study was acquired from the Health Facts Database, which includes over 100,000 records of diabetic patients from 1999 to 2008. The basic data distribution characteristics of this dataset were summarized and then analyzed. In this study, 30-days readmission was defined as a readmission period of less than 30 days. After data preprocessing and normalization, multiple risk factors in the dataset were examined for classifier training to predict the probability of readmission using ML models. Different ML classifiers such as random forest, Naive Bayes, and decision tree ensemble were adopted to improve the clinical efficiency of the classification. In this study, the Konstanz Information Miner platform was used to preprocess and model the data, and the performances of the different classifiers were compared. RESULTS: A total of 100,244 records were included in the model construction after the data preprocessing and normalization. A total of 23 attributes, including race, sex, age, admission type, admission location, length of stay, and drug use, were finally identified as modeling risk factors. Comparison of the performance indexes of the three algorithms revealed that the RF model had the best performance with a higher area under receiver operating characteristic curve (AUC) than the other two algorithms, suggesting that its use is more suitable for making readmission predictions. CONCLUSION: The factors influencing 30-days readmission predictions in diabetic patients, including number of inpatient admissions, age, diagnosis, number of emergencies, and sex, would help healthcare providers to identify patients who are at high risk of short-term readmission and reduce the probability of 30-days readmission. The RF algorithm with the highest AUC is more suitable for making 30-days readmission predictions and deserves further validation in clinical trials. BioMed Central 2021-07-30 /pmc/articles/PMC8323261/ /pubmed/34330267 http://dx.doi.org/10.1186/s12911-021-01423-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 Shang, Yujuan Jiang, Kui Wang, Lei Zhang, Zheqing Zhou, Siwei Liu, Yun Dong, Jiancheng Wu, Huiqun The 30-days hospital readmission risk in diabetic patients: predictive modeling with machine learning classifiers |
title | The 30-days hospital readmission risk in diabetic patients: predictive modeling with machine learning classifiers |
title_full | The 30-days hospital readmission risk in diabetic patients: predictive modeling with machine learning classifiers |
title_fullStr | The 30-days hospital readmission risk in diabetic patients: predictive modeling with machine learning classifiers |
title_full_unstemmed | The 30-days hospital readmission risk in diabetic patients: predictive modeling with machine learning classifiers |
title_short | The 30-days hospital readmission risk in diabetic patients: predictive modeling with machine learning classifiers |
title_sort | 30-days hospital readmission risk in diabetic patients: predictive modeling with machine learning classifiers |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323261/ https://www.ncbi.nlm.nih.gov/pubmed/34330267 http://dx.doi.org/10.1186/s12911-021-01423-y |
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