Machine learning models to predict in-hospital mortality in septic patients with diabetes
BackgroundSepsis is a leading cause of morbidity and mortality in hospitalized patients. Up to now, there are no well-established longitudinal networks from molecular mechanisms to clinical phenotypes in sepsis. Adding to the problem, about one of the five patients presented with diabetes. For this...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2022.1034251/full |
| _version_ | 1797988559563522048 |
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| author | Jing Qi Jingchao Lei Nanyi Li Dan Huang Huaizheng Liu Kefu Zhou Zheren Dai Chuanzheng Sun |
| author_facet | Jing Qi Jingchao Lei Nanyi Li Dan Huang Huaizheng Liu Kefu Zhou Zheren Dai Chuanzheng Sun |
| author_sort | Jing Qi |
| collection | DOAJ |
| description | BackgroundSepsis is a leading cause of morbidity and mortality in hospitalized patients. Up to now, there are no well-established longitudinal networks from molecular mechanisms to clinical phenotypes in sepsis. Adding to the problem, about one of the five patients presented with diabetes. For this subgroup, management is difficult, and prognosis is difficult to evaluate.MethodsFrom the three databases, a total of 7,001 patients were enrolled on the basis of sepsis-3 standard and diabetes diagnosis. Input variable selection is based on the result of correlation analysis in a handpicking way, and 53 variables were left. A total of 5,727 records were collected from Medical Information Mart for Intensive Care database and randomly split into a training set and an internal validation set at a ratio of 7:3. Then, logistic regression with lasso regularization, Bayes logistic regression, decision tree, random forest, and XGBoost were conducted to build the predictive model by using training set. Then, the models were tested by the internal validation set. The data from eICU Collaborative Research Database (n = 815) and dtChina critical care database (n = 459) were used to test the model performance as the external validation set.ResultsIn the internal validation set, the accuracy values of logistic regression with lasso regularization, Bayes logistic regression, decision tree, random forest, and XGBoost were 0.878, 0.883, 0.865, 0.883, and 0.882, respectively. Likewise, in the external validation set 1, lasso regularization = 0.879, Bayes logistic regression = 0.877, decision tree = 0.865, random forest = 0.886, and XGBoost = 0.875. In the external validation set 2, lasso regularization = 0.715, Bayes logistic regression = 0.745, decision tree = 0.763, random forest = 0.760, and XGBoost = 0.699.ConclusionThe top three models for internal validation set were Bayes logistic regression, random forest, and XGBoost, whereas the top three models for external validation set 1 were random forest, logistic regression, and Bayes logistic regression. In addition, the top three models for the external validation set 2 were decision tree, random forest, and Bayes logistic regression. Random forest model performed well with the training and three validation sets. The most important features are age, albumin, and lactate. |
| first_indexed | 2024-04-11T08:05:57Z |
| format | Article |
| id | doaj.art-6e5d0c2f27a5469a87817a1cbd09fa8e |
| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-04-11T08:05:57Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj.art-6e5d0c2f27a5469a87817a1cbd09fa8e2022-12-22T04:35:34ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922022-11-011310.3389/fendo.2022.10342511034251Machine learning models to predict in-hospital mortality in septic patients with diabetesJing QiJingchao LeiNanyi LiDan HuangHuaizheng LiuKefu ZhouZheren DaiChuanzheng SunBackgroundSepsis is a leading cause of morbidity and mortality in hospitalized patients. Up to now, there are no well-established longitudinal networks from molecular mechanisms to clinical phenotypes in sepsis. Adding to the problem, about one of the five patients presented with diabetes. For this subgroup, management is difficult, and prognosis is difficult to evaluate.MethodsFrom the three databases, a total of 7,001 patients were enrolled on the basis of sepsis-3 standard and diabetes diagnosis. Input variable selection is based on the result of correlation analysis in a handpicking way, and 53 variables were left. A total of 5,727 records were collected from Medical Information Mart for Intensive Care database and randomly split into a training set and an internal validation set at a ratio of 7:3. Then, logistic regression with lasso regularization, Bayes logistic regression, decision tree, random forest, and XGBoost were conducted to build the predictive model by using training set. Then, the models were tested by the internal validation set. The data from eICU Collaborative Research Database (n = 815) and dtChina critical care database (n = 459) were used to test the model performance as the external validation set.ResultsIn the internal validation set, the accuracy values of logistic regression with lasso regularization, Bayes logistic regression, decision tree, random forest, and XGBoost were 0.878, 0.883, 0.865, 0.883, and 0.882, respectively. Likewise, in the external validation set 1, lasso regularization = 0.879, Bayes logistic regression = 0.877, decision tree = 0.865, random forest = 0.886, and XGBoost = 0.875. In the external validation set 2, lasso regularization = 0.715, Bayes logistic regression = 0.745, decision tree = 0.763, random forest = 0.760, and XGBoost = 0.699.ConclusionThe top three models for internal validation set were Bayes logistic regression, random forest, and XGBoost, whereas the top three models for external validation set 1 were random forest, logistic regression, and Bayes logistic regression. In addition, the top three models for the external validation set 2 were decision tree, random forest, and Bayes logistic regression. Random forest model performed well with the training and three validation sets. The most important features are age, albumin, and lactate.https://www.frontiersin.org/articles/10.3389/fendo.2022.1034251/fullmachine learningsepsisdiabetesin-hospital mortalityrisk factors |
| spellingShingle | Jing Qi Jingchao Lei Nanyi Li Dan Huang Huaizheng Liu Kefu Zhou Zheren Dai Chuanzheng Sun Machine learning models to predict in-hospital mortality in septic patients with diabetes Frontiers in Endocrinology machine learning sepsis diabetes in-hospital mortality risk factors |
| title | Machine learning models to predict in-hospital mortality in septic patients with diabetes |
| title_full | Machine learning models to predict in-hospital mortality in septic patients with diabetes |
| title_fullStr | Machine learning models to predict in-hospital mortality in septic patients with diabetes |
| title_full_unstemmed | Machine learning models to predict in-hospital mortality in septic patients with diabetes |
| title_short | Machine learning models to predict in-hospital mortality in septic patients with diabetes |
| title_sort | machine learning models to predict in hospital mortality in septic patients with diabetes |
| topic | machine learning sepsis diabetes in-hospital mortality risk factors |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2022.1034251/full |
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