Developing and validating a machine learning-based model for predicting in-hospital mortality among ICU-admitted heart failure patients: A study utilizing the MIMIC-III database
Background Although the assessment of in-hospital mortality risk among heart failure patients in the intensive care unit (ICU) is crucial for clinical decision-making, there is currently a lack of comprehensive models accurately predicting their prognosis. Machine learning techniques offer a powerfu...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-04-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076251335705 |
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| author | De Su Jie Zheng Yue-kai Shao Jun-ya Liu Xin-xin Liu Kun Yu Bang-hai Feng Hong Mei Song Qin |
| author_facet | De Su Jie Zheng Yue-kai Shao Jun-ya Liu Xin-xin Liu Kun Yu Bang-hai Feng Hong Mei Song Qin |
| author_sort | De Su |
| collection | DOAJ |
| description | Background Although the assessment of in-hospital mortality risk among heart failure patients in the intensive care unit (ICU) is crucial for clinical decision-making, there is currently a lack of comprehensive models accurately predicting their prognosis. Machine learning techniques offer a powerful means to identify potential risk factors and predict outcomes within multivariable clinical data. Methods This study, based on the MIMIC-III database, extracted demographic characteristics, vital signs, laboratory test values, and comorbidity information of heart failure patients using structured query language. LASSO regression was employed for feature selection, and various machine learning algorithms were utilized to train models, including logistic regression (LR), random forest (RF), and gradient boosting (GB), among others. An ensemble learning model based on a soft voting mechanism was constructed. Model performance was evaluated using accuracy, recall, precision, F1 score, and AUC values through cross-validation and on an independent test set. Results In five-fold cross-validation, the soft voting ensemble learning model demonstrated the best overall performance, with accuracy and AUC values both at 0.86. Additionally, RF and GB models also performed well, with RF achieving an accuracy of 0.79 and an AUC of 0.79 on the independent test set, while the GB model achieved an accuracy of 0.77 and an AUC of 0.79. In contrast, other models such as LR, SVM, and KNN exhibited poorer performance in terms of accuracy and AUC values, indicating the significant advantage of ensemble methods in handling complex clinical prediction tasks. Conclusion This study demonstrates the potential of machine learning models, particularly ensemble learning models based on soft voting mechanisms, in predicting in-hospital mortality risk among heart failure patients in the ICU. The overall performance of the ensemble learning model confirms its effectiveness as an adjunct clinical decision-making tool. Future research should further optimize the models and validate them in a broader patient population to enhance their practical utility and accuracy in real clinical settings. |
| format | Article |
| id | doaj-art-03a5be17bde54bdab95f90057cceffb4 |
| institution | DOAJ |
| issn | 2055-2076 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj-art-03a5be17bde54bdab95f90057cceffb42025-08-20T03:14:19ZengSAGE PublishingDigital Health2055-20762025-04-011110.1177/20552076251335705Developing and validating a machine learning-based model for predicting in-hospital mortality among ICU-admitted heart failure patients: A study utilizing the MIMIC-III databaseDe Su0Jie Zheng1Yue-kai Shao2Jun-ya Liu3Xin-xin Liu4Kun Yu5Bang-hai Feng6Hong Mei7Song Qin8 Department of Critical Care Medicine, , Zunyi, Guizhou, P.R. China Department of Critical Care Medicine, , Zunyi, Guizhou, P.R. China Department of Critical Care Medicine, , Zunyi, Guizhou, P.R. China Department of Critical Care Medicine, , Zunyi, Guizhou, P.R. China Department of Critical Care Medicine, , Zunyi, Guizhou, P.R. China Department of Critical Care Medicine, , Zunyi, Guizhou, P.R. China Department of Critical Care Medicine, Zunyi Hospital of Traditional Chinese Medicine, Zunyi, Guizhou, P.R. China Department of Critical Care Medicine, , Zunyi, Guizhou, P.R. China Department of Critical Care Medicine, , Zunyi, Guizhou, P.R. ChinaBackground Although the assessment of in-hospital mortality risk among heart failure patients in the intensive care unit (ICU) is crucial for clinical decision-making, there is currently a lack of comprehensive models accurately predicting their prognosis. Machine learning techniques offer a powerful means to identify potential risk factors and predict outcomes within multivariable clinical data. Methods This study, based on the MIMIC-III database, extracted demographic characteristics, vital signs, laboratory test values, and comorbidity information of heart failure patients using structured query language. LASSO regression was employed for feature selection, and various machine learning algorithms were utilized to train models, including logistic regression (LR), random forest (RF), and gradient boosting (GB), among others. An ensemble learning model based on a soft voting mechanism was constructed. Model performance was evaluated using accuracy, recall, precision, F1 score, and AUC values through cross-validation and on an independent test set. Results In five-fold cross-validation, the soft voting ensemble learning model demonstrated the best overall performance, with accuracy and AUC values both at 0.86. Additionally, RF and GB models also performed well, with RF achieving an accuracy of 0.79 and an AUC of 0.79 on the independent test set, while the GB model achieved an accuracy of 0.77 and an AUC of 0.79. In contrast, other models such as LR, SVM, and KNN exhibited poorer performance in terms of accuracy and AUC values, indicating the significant advantage of ensemble methods in handling complex clinical prediction tasks. Conclusion This study demonstrates the potential of machine learning models, particularly ensemble learning models based on soft voting mechanisms, in predicting in-hospital mortality risk among heart failure patients in the ICU. The overall performance of the ensemble learning model confirms its effectiveness as an adjunct clinical decision-making tool. Future research should further optimize the models and validate them in a broader patient population to enhance their practical utility and accuracy in real clinical settings.https://doi.org/10.1177/20552076251335705 |
| spellingShingle | De Su Jie Zheng Yue-kai Shao Jun-ya Liu Xin-xin Liu Kun Yu Bang-hai Feng Hong Mei Song Qin Developing and validating a machine learning-based model for predicting in-hospital mortality among ICU-admitted heart failure patients: A study utilizing the MIMIC-III database Digital Health |
| title | Developing and validating a machine learning-based model for predicting in-hospital mortality among ICU-admitted heart failure patients: A study utilizing the MIMIC-III database |
| title_full | Developing and validating a machine learning-based model for predicting in-hospital mortality among ICU-admitted heart failure patients: A study utilizing the MIMIC-III database |
| title_fullStr | Developing and validating a machine learning-based model for predicting in-hospital mortality among ICU-admitted heart failure patients: A study utilizing the MIMIC-III database |
| title_full_unstemmed | Developing and validating a machine learning-based model for predicting in-hospital mortality among ICU-admitted heart failure patients: A study utilizing the MIMIC-III database |
| title_short | Developing and validating a machine learning-based model for predicting in-hospital mortality among ICU-admitted heart failure patients: A study utilizing the MIMIC-III database |
| title_sort | developing and validating a machine learning based model for predicting in hospital mortality among icu admitted heart failure patients a study utilizing the mimic iii database |
| url | https://doi.org/10.1177/20552076251335705 |
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