Interpretable machine learning model for predicting delirium in patients with sepsis: a study based on the MIMIC data
Abstract Objective The aim of this study was to construct interpretable machine learning models to predict the risk of developing delirium in patients with sepsis and to explore the impact of delirium on the 28-day survival rate of patients. Methods We enrolled 10,321 patients with sepsis older than...
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BMC
2025-04-01
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| Series: | BMC Infectious Diseases |
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| Online Access: | https://doi.org/10.1186/s12879-025-10982-8 |
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| author | Jing Fu Aifeng He Lulu Wang Xia Li Jiangquan Yu Ruiqiang Zheng |
| author_facet | Jing Fu Aifeng He Lulu Wang Xia Li Jiangquan Yu Ruiqiang Zheng |
| author_sort | Jing Fu |
| collection | DOAJ |
| description | Abstract Objective The aim of this study was to construct interpretable machine learning models to predict the risk of developing delirium in patients with sepsis and to explore the impact of delirium on the 28-day survival rate of patients. Methods We enrolled 10,321 patients with sepsis older than eighteen years from the MIMIC-IV (Medical Information Mart for Intensive Care) database. Sepsis is defined as the presence or suspected presence of infection, along with a SOFA (Sequential Organ Failure Assessment) score of ≥ 2. Four machine learning models, namely XGBoost (extreme gradient Boost), SVM (support vector machine), Logistic (logistic regression) and RF (random forest), were established for prediction, and the prediction model was constructed. Results A total of 10,321 sepsis patients were included, among whom 4,691 (45.45%) developed delirium. The 28-day mortality rate was markedly elevated in the delirium group (log-rank P < 0.001). The XGBoost model has the best performance. Finally, 5 variables were selected to draw a nomogram: hypertension, SOFA score, chlorine, Hb (hemoglobin), creatinine. The receiver operating characteristic (ROC) curve of the predictive delirium model showed better predictive efficiency, with an AUC of 0.767 (95%CI (confidence interval): 0.726–0.798). Conclusion The nomogram built on the XGBoost model provides clinicians with an easy tool to quickly assess the risk of developing delirium in patients with sepsis. It provides a new idea and direction for the best model to predict delirium in patients with sepsis, so as to promote the development of delirium related research. |
| format | Article |
| id | doaj-art-24b83fe941a44fe6a4aa028dd90af5ea |
| institution | OA Journals |
| issn | 1471-2334 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
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| series | BMC Infectious Diseases |
| spelling | doaj-art-24b83fe941a44fe6a4aa028dd90af5ea2025-08-20T02:30:20ZengBMCBMC Infectious Diseases1471-23342025-04-0125111310.1186/s12879-025-10982-8Interpretable machine learning model for predicting delirium in patients with sepsis: a study based on the MIMIC dataJing Fu0Aifeng He1Lulu Wang2Xia Li3Jiangquan Yu4Ruiqiang Zheng5Northern Jiangsu People’s Hospital Affiliated to Yangzhou University/Clinical Medical College, Yangzhou UniversityNorthern Jiangsu People’s Hospital Affiliated to Yangzhou University/Clinical Medical College, Yangzhou UniversityHuai ’an Second People’s HospitalHuai ’an Second People’s HospitalNorthern Jiangsu People’s Hospital Affiliated to Yangzhou University/Clinical Medical College, Yangzhou UniversityNorthern Jiangsu People’s Hospital Affiliated to Yangzhou University/Clinical Medical College, Yangzhou UniversityAbstract Objective The aim of this study was to construct interpretable machine learning models to predict the risk of developing delirium in patients with sepsis and to explore the impact of delirium on the 28-day survival rate of patients. Methods We enrolled 10,321 patients with sepsis older than eighteen years from the MIMIC-IV (Medical Information Mart for Intensive Care) database. Sepsis is defined as the presence or suspected presence of infection, along with a SOFA (Sequential Organ Failure Assessment) score of ≥ 2. Four machine learning models, namely XGBoost (extreme gradient Boost), SVM (support vector machine), Logistic (logistic regression) and RF (random forest), were established for prediction, and the prediction model was constructed. Results A total of 10,321 sepsis patients were included, among whom 4,691 (45.45%) developed delirium. The 28-day mortality rate was markedly elevated in the delirium group (log-rank P < 0.001). The XGBoost model has the best performance. Finally, 5 variables were selected to draw a nomogram: hypertension, SOFA score, chlorine, Hb (hemoglobin), creatinine. The receiver operating characteristic (ROC) curve of the predictive delirium model showed better predictive efficiency, with an AUC of 0.767 (95%CI (confidence interval): 0.726–0.798). Conclusion The nomogram built on the XGBoost model provides clinicians with an easy tool to quickly assess the risk of developing delirium in patients with sepsis. It provides a new idea and direction for the best model to predict delirium in patients with sepsis, so as to promote the development of delirium related research.https://doi.org/10.1186/s12879-025-10982-8Machine learningDeliriumSepsisNomogramMIMIC-IV database |
| spellingShingle | Jing Fu Aifeng He Lulu Wang Xia Li Jiangquan Yu Ruiqiang Zheng Interpretable machine learning model for predicting delirium in patients with sepsis: a study based on the MIMIC data BMC Infectious Diseases Machine learning Delirium Sepsis Nomogram MIMIC-IV database |
| title | Interpretable machine learning model for predicting delirium in patients with sepsis: a study based on the MIMIC data |
| title_full | Interpretable machine learning model for predicting delirium in patients with sepsis: a study based on the MIMIC data |
| title_fullStr | Interpretable machine learning model for predicting delirium in patients with sepsis: a study based on the MIMIC data |
| title_full_unstemmed | Interpretable machine learning model for predicting delirium in patients with sepsis: a study based on the MIMIC data |
| title_short | Interpretable machine learning model for predicting delirium in patients with sepsis: a study based on the MIMIC data |
| title_sort | interpretable machine learning model for predicting delirium in patients with sepsis a study based on the mimic data |
| topic | Machine learning Delirium Sepsis Nomogram MIMIC-IV database |
| url | https://doi.org/10.1186/s12879-025-10982-8 |
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