Construction and clinical visualization application of a predictive model for mortality risk in sepsis patients based on an improved machine learning model
ObjectiveTo explore the construction and clinical visualization application of a mortality risk prediction model for sepsis patients based on an improved machine learning model.MethodsThis retrospective study analyzed 1,050 sepsis patients admitted to Longyou County People’s Hospital between January...
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| Format: | Article |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2025.1560659/full |
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| author | Ting Chen Xuefeng Zhang Qunfeng Yu Qin Yang Lingmin Yuan Fei Tong |
| author_facet | Ting Chen Xuefeng Zhang Qunfeng Yu Qin Yang Lingmin Yuan Fei Tong |
| author_sort | Ting Chen |
| collection | DOAJ |
| description | ObjectiveTo explore the construction and clinical visualization application of a mortality risk prediction model for sepsis patients based on an improved machine learning model.MethodsThis retrospective study analyzed 1,050 sepsis patients admitted to Longyou County People’s Hospital between January 2010 and August 2023. Patients were divided into a survival group (n = 877) and a death group (n = 173) based on their 30-day mortality status. Clinical and laboratory data were collected and used as feature variables. A Self-Weighted Self-Evolutionary Learning Model (SWSELM) was developed to identify independent risk factors for sepsis mortality and to create a visualization system for clinical application.ResultsThe improved algorithm significantly outperformed other algorithms on 23 standard test functions. The SWSELM model achieved ROC-AUC and PR-AUC values of 0.9760 and 0.9624, respectively, on the training set, and 0.9387 and 0.9390, respectively, on the test set, both significantly higher than those of three other prediction models. The SWSELM model identified 10 important features, with multivariate logistic regression retaining five variables: B-type Natriuretic Peptide Precursor (NT-proBNP), Lactate, Albumin, Oxygenation Index, and Mean Arterial Pressure (MAP) (OR = 4.889, 3.770, 3.083, 1.872, 1.297), consistent with the top five features selected by the SWSELM model.ConclusionNT-proBNP, Lactate, Albumin, Oxygenation Index, and Mean Arterial Pressure are independent risk factors for mortality in sepsis patients. This study successfully created a self-evolutionary prediction model using machine learning methods, demonstrating significant clinical application potential and value for broader implementation. |
| format | Article |
| id | doaj-art-45eca65ac1d646848a01710717cf8ced |
| institution | DOAJ |
| issn | 1664-042X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj-art-45eca65ac1d646848a01710717cf8ced2025-08-20T03:08:18ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2025-05-011610.3389/fphys.2025.15606591560659Construction and clinical visualization application of a predictive model for mortality risk in sepsis patients based on an improved machine learning modelTing Chen0Xuefeng Zhang1Qunfeng Yu2Qin Yang3Lingmin Yuan4Fei Tong5Emergency Department of Longyou County People’s Hospital, Quzhou, Zhejiang, ChinaEmergency Department of Longyou County People’s Hospital, Quzhou, Zhejiang, ChinaIntensive Care Unit of Longyou County People’s Hospital, Quzhou, Zhejiang, ChinaIntensive Care Unit of Longyou County People’s Hospital, Quzhou, Zhejiang, ChinaIntensive Care Unit of Longyou County People’s Hospital, Quzhou, Zhejiang, ChinaDepartment of Thoracic Surgery, Longyou County People's Hospital, Quzhou, ChinaObjectiveTo explore the construction and clinical visualization application of a mortality risk prediction model for sepsis patients based on an improved machine learning model.MethodsThis retrospective study analyzed 1,050 sepsis patients admitted to Longyou County People’s Hospital between January 2010 and August 2023. Patients were divided into a survival group (n = 877) and a death group (n = 173) based on their 30-day mortality status. Clinical and laboratory data were collected and used as feature variables. A Self-Weighted Self-Evolutionary Learning Model (SWSELM) was developed to identify independent risk factors for sepsis mortality and to create a visualization system for clinical application.ResultsThe improved algorithm significantly outperformed other algorithms on 23 standard test functions. The SWSELM model achieved ROC-AUC and PR-AUC values of 0.9760 and 0.9624, respectively, on the training set, and 0.9387 and 0.9390, respectively, on the test set, both significantly higher than those of three other prediction models. The SWSELM model identified 10 important features, with multivariate logistic regression retaining five variables: B-type Natriuretic Peptide Precursor (NT-proBNP), Lactate, Albumin, Oxygenation Index, and Mean Arterial Pressure (MAP) (OR = 4.889, 3.770, 3.083, 1.872, 1.297), consistent with the top five features selected by the SWSELM model.ConclusionNT-proBNP, Lactate, Albumin, Oxygenation Index, and Mean Arterial Pressure are independent risk factors for mortality in sepsis patients. This study successfully created a self-evolutionary prediction model using machine learning methods, demonstrating significant clinical application potential and value for broader implementation.https://www.frontiersin.org/articles/10.3389/fphys.2025.1560659/fullmachine learningsepsismortalityNT-ProBNPpredictionvisualization |
| spellingShingle | Ting Chen Xuefeng Zhang Qunfeng Yu Qin Yang Lingmin Yuan Fei Tong Construction and clinical visualization application of a predictive model for mortality risk in sepsis patients based on an improved machine learning model Frontiers in Physiology machine learning sepsis mortality NT-ProBNP prediction visualization |
| title | Construction and clinical visualization application of a predictive model for mortality risk in sepsis patients based on an improved machine learning model |
| title_full | Construction and clinical visualization application of a predictive model for mortality risk in sepsis patients based on an improved machine learning model |
| title_fullStr | Construction and clinical visualization application of a predictive model for mortality risk in sepsis patients based on an improved machine learning model |
| title_full_unstemmed | Construction and clinical visualization application of a predictive model for mortality risk in sepsis patients based on an improved machine learning model |
| title_short | Construction and clinical visualization application of a predictive model for mortality risk in sepsis patients based on an improved machine learning model |
| title_sort | construction and clinical visualization application of a predictive model for mortality risk in sepsis patients based on an improved machine learning model |
| topic | machine learning sepsis mortality NT-ProBNP prediction visualization |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2025.1560659/full |
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