Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease
Background The objective of this study was to develop and validate a machine learning (ML) model for predict in-hospital mortality among critically ill patients with congestive heart failure (CHF) combined with chronic kidney disease (CKD).Methods After employing least absolute shrinkage and selecti...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Renal Failure |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2024.2315298 |
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| author | Xunliang Li Zhijuan Wang Wenman Zhao Rui Shi Yuyu Zhu Haifeng Pan Deguang Wang |
| author_facet | Xunliang Li Zhijuan Wang Wenman Zhao Rui Shi Yuyu Zhu Haifeng Pan Deguang Wang |
| author_sort | Xunliang Li |
| collection | DOAJ |
| description | Background The objective of this study was to develop and validate a machine learning (ML) model for predict in-hospital mortality among critically ill patients with congestive heart failure (CHF) combined with chronic kidney disease (CKD).Methods After employing least absolute shrinkage and selection operator regression for feature selection, six distinct methodologies were employed in the construction of the model. The selection of the optimal model was based on the area under the curve (AUC). Furthermore, the interpretation of the chosen model was facilitated through the utilization of SHapley Additive exPlanation (SHAP) values and the Local Interpretable Model-Agnostic Explanations (LIME) algorithm.Results This study collected data and enrolled 5041 patients on CHF combined with CKD from 2008 to 2019, utilizing the Medical Information Mart for Intensive Care Unit. After selection, 22 of the 47 variables collected post-intensive care unit admission were identified as mortality-associated and subsequently utilized in the development of ML models. Among the six models generated, the eXtreme Gradient Boosting (XGBoost) model demonstrated the highest AUC at 0.837. Notably, the SHAP values highlighted the sequential organ failure assessment score, age, simplified acute physiology score II, and urine output as the four most influential variables in the XGBoost model. In addition, the LIME algorithm explains the individualized predictions.Conclusions In conclusion, our study accomplished the successful development and validation of ML models for predicting in-hospital mortality in critically ill patients with CHF combined with CKD. Notably, the XGBoost model emerged as the most efficacious among all the ML models employed. |
| format | Article |
| id | doaj-art-a5fc5cd40a5a4ba4be571c2dbcab9916 |
| institution | Kabale University |
| issn | 0886-022X 1525-6049 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Renal Failure |
| spelling | doaj-art-a5fc5cd40a5a4ba4be571c2dbcab99162025-01-23T04:17:47ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492024-12-0146110.1080/0886022X.2024.2315298Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney diseaseXunliang Li0Zhijuan Wang1Wenman Zhao2Rui Shi3Yuyu Zhu4Haifeng Pan5Deguang Wang6Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaInstitute of Kidney Disease, Inflammation and Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, ChinaBackground The objective of this study was to develop and validate a machine learning (ML) model for predict in-hospital mortality among critically ill patients with congestive heart failure (CHF) combined with chronic kidney disease (CKD).Methods After employing least absolute shrinkage and selection operator regression for feature selection, six distinct methodologies were employed in the construction of the model. The selection of the optimal model was based on the area under the curve (AUC). Furthermore, the interpretation of the chosen model was facilitated through the utilization of SHapley Additive exPlanation (SHAP) values and the Local Interpretable Model-Agnostic Explanations (LIME) algorithm.Results This study collected data and enrolled 5041 patients on CHF combined with CKD from 2008 to 2019, utilizing the Medical Information Mart for Intensive Care Unit. After selection, 22 of the 47 variables collected post-intensive care unit admission were identified as mortality-associated and subsequently utilized in the development of ML models. Among the six models generated, the eXtreme Gradient Boosting (XGBoost) model demonstrated the highest AUC at 0.837. Notably, the SHAP values highlighted the sequential organ failure assessment score, age, simplified acute physiology score II, and urine output as the four most influential variables in the XGBoost model. In addition, the LIME algorithm explains the individualized predictions.Conclusions In conclusion, our study accomplished the successful development and validation of ML models for predicting in-hospital mortality in critically ill patients with CHF combined with CKD. Notably, the XGBoost model emerged as the most efficacious among all the ML models employed.https://www.tandfonline.com/doi/10.1080/0886022X.2024.2315298Congestive heart failurechronic kidney diseasemachine learningmortalitycritically care |
| spellingShingle | Xunliang Li Zhijuan Wang Wenman Zhao Rui Shi Yuyu Zhu Haifeng Pan Deguang Wang Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease Renal Failure Congestive heart failure chronic kidney disease machine learning mortality critically care |
| title | Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease |
| title_full | Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease |
| title_fullStr | Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease |
| title_full_unstemmed | Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease |
| title_short | Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease |
| title_sort | machine learning algorithm for predict the in hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease |
| topic | Congestive heart failure chronic kidney disease machine learning mortality critically care |
| url | https://www.tandfonline.com/doi/10.1080/0886022X.2024.2315298 |
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