Developing a rapid screening tool for high-risk ICU patients of sepsis: integrating electronic medical records with machine learning methods for mortality prediction in hospitalized patients—model establishment, internal and external validation, and visualization
Abstract Objectives To develop a machine learning-based prediction model using clinical data from the first 24 h of ICU admission to enable rapid screening and early intervention for sepsis patients. Methods This multicenter retrospective cohort study analyzed electronic medical records of sepsis pa...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | Journal of Translational Medicine |
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| Online Access: | https://doi.org/10.1186/s12967-025-06102-4 |
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| author | Songchang Shi Lihui Zhang Shujuan Zhang Jinyang Shi Donghuang Hong Siqi Wu Xiaobin Pan Wei Lin |
| author_facet | Songchang Shi Lihui Zhang Shujuan Zhang Jinyang Shi Donghuang Hong Siqi Wu Xiaobin Pan Wei Lin |
| author_sort | Songchang Shi |
| collection | DOAJ |
| description | Abstract Objectives To develop a machine learning-based prediction model using clinical data from the first 24 h of ICU admission to enable rapid screening and early intervention for sepsis patients. Methods This multicenter retrospective cohort study analyzed electronic medical records of sepsis patients using machine learning methods. We evaluated model performance in predicting sepsis outcomes within the first 24 h of ICU admission across US and Chinese healthcare settings. Results From 31 clinical features, machine learning models demonstrated significantly better predictive performance than traditional approaches for sepsis outcomes. While linear regression achieved low test scores (0.25), machine learning methods reached scores of 0.78 and AUCs above 0.8 in testing. Importantly, these models maintained robust performance (scores 0.63–0.77) in external validation. Conclusions The application of machine learning-based prediction models for sepsis could significantly improve patient outcomes through early detection and timely intervention in the critical first 24 h of ICU admission, supporting clinical decision-making. |
| format | Article |
| id | doaj-art-f267ec7d289d46c6b728b5305b07b0ad |
| institution | Kabale University |
| issn | 1479-5876 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj-art-f267ec7d289d46c6b728b5305b07b0ad2025-01-26T12:50:22ZengBMCJournal of Translational Medicine1479-58762025-01-0123111110.1186/s12967-025-06102-4Developing a rapid screening tool for high-risk ICU patients of sepsis: integrating electronic medical records with machine learning methods for mortality prediction in hospitalized patients—model establishment, internal and external validation, and visualizationSongchang Shi0Lihui Zhang1Shujuan Zhang2Jinyang Shi3Donghuang Hong4Siqi Wu5Xiaobin Pan6Wei Lin7Department of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital South Branch, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial HospitalDepartment of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital South Branch, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial HospitalDepartment of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital South Branch, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial HospitalFujian Medical UniversityDepartment of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial HospitalShengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial HospitalDepartment of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital South Branch, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial HospitalDepartment of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial HospitalAbstract Objectives To develop a machine learning-based prediction model using clinical data from the first 24 h of ICU admission to enable rapid screening and early intervention for sepsis patients. Methods This multicenter retrospective cohort study analyzed electronic medical records of sepsis patients using machine learning methods. We evaluated model performance in predicting sepsis outcomes within the first 24 h of ICU admission across US and Chinese healthcare settings. Results From 31 clinical features, machine learning models demonstrated significantly better predictive performance than traditional approaches for sepsis outcomes. While linear regression achieved low test scores (0.25), machine learning methods reached scores of 0.78 and AUCs above 0.8 in testing. Importantly, these models maintained robust performance (scores 0.63–0.77) in external validation. Conclusions The application of machine learning-based prediction models for sepsis could significantly improve patient outcomes through early detection and timely intervention in the critical first 24 h of ICU admission, supporting clinical decision-making.https://doi.org/10.1186/s12967-025-06102-4SepsisMachine learningMortalityPredictionVisualization |
| spellingShingle | Songchang Shi Lihui Zhang Shujuan Zhang Jinyang Shi Donghuang Hong Siqi Wu Xiaobin Pan Wei Lin Developing a rapid screening tool for high-risk ICU patients of sepsis: integrating electronic medical records with machine learning methods for mortality prediction in hospitalized patients—model establishment, internal and external validation, and visualization Journal of Translational Medicine Sepsis Machine learning Mortality Prediction Visualization |
| title | Developing a rapid screening tool for high-risk ICU patients of sepsis: integrating electronic medical records with machine learning methods for mortality prediction in hospitalized patients—model establishment, internal and external validation, and visualization |
| title_full | Developing a rapid screening tool for high-risk ICU patients of sepsis: integrating electronic medical records with machine learning methods for mortality prediction in hospitalized patients—model establishment, internal and external validation, and visualization |
| title_fullStr | Developing a rapid screening tool for high-risk ICU patients of sepsis: integrating electronic medical records with machine learning methods for mortality prediction in hospitalized patients—model establishment, internal and external validation, and visualization |
| title_full_unstemmed | Developing a rapid screening tool for high-risk ICU patients of sepsis: integrating electronic medical records with machine learning methods for mortality prediction in hospitalized patients—model establishment, internal and external validation, and visualization |
| title_short | Developing a rapid screening tool for high-risk ICU patients of sepsis: integrating electronic medical records with machine learning methods for mortality prediction in hospitalized patients—model establishment, internal and external validation, and visualization |
| title_sort | developing a rapid screening tool for high risk icu patients of sepsis integrating electronic medical records with machine learning methods for mortality prediction in hospitalized patients model establishment internal and external validation and visualization |
| topic | Sepsis Machine learning Mortality Prediction Visualization |
| url | https://doi.org/10.1186/s12967-025-06102-4 |
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