Predicting nosocomial pneumonia of patients with acute brain injury in intensive care unit using machine-learning models
IntroductionThe aim of this study is to construct and validate new machine learning models to predict pneumonia events in intensive care unit (ICU) patients with acute brain injury.MethodsAcute brain injury patients in ICU of hospitals from January 1, 2020, to December 31, 2021 were retrospective re...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1501025/full |
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| author | Junchen Pan Zhen Yue Jing Ji Yongping You Liqing Bi Yun Liu Xinglin Xiong Genying Gu Ming Chen Shen Zhang |
| author_facet | Junchen Pan Zhen Yue Jing Ji Yongping You Liqing Bi Yun Liu Xinglin Xiong Genying Gu Ming Chen Shen Zhang |
| author_sort | Junchen Pan |
| collection | DOAJ |
| description | IntroductionThe aim of this study is to construct and validate new machine learning models to predict pneumonia events in intensive care unit (ICU) patients with acute brain injury.MethodsAcute brain injury patients in ICU of hospitals from January 1, 2020, to December 31, 2021 were retrospective reviewed. Patients were divided into training, and validation sets. The primary outcome was nosocomial pneumonia infection during ICU stay. Machine learning models including XGBoost, DecisionTree, Random Forest, Light GBM, Adaptive Boost, BP, and TabNet were used for model derivation. The predictive value of each model was evaluated using accuracy, precision, recall, F1-score, and area under the curve (AUC), and internal and external validation was performed.ResultsA total of 280 ICU patients with acute brain injury were included. Five independent variables for nosocomial pneumonia infection were identified and selected for machine learning model derivations and validations, including tracheotomy time, antibiotic use days, blood glucose, ventilator-assisted ventilation time, and C-reactive protein. The training set revealed the superior and robust performance of the XGBoost with the highest AUC value (0.956), while the Random Forest and Adaptive Boost had the highest AUC value (0.883) in validation set.ConclusionMachine learning models can effectively predict the risk of nosocomial pneumonia infection in patients with acute brain injury in the ICU. Despite differences in populations and algorithms, the models we constructed demonstrated reliable predictive performance. |
| format | Article |
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| institution | OA Journals |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
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| spelling | doaj-art-5ed2862aaf144f9282c0ebb40a0104d42025-08-20T02:09:35ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-04-011210.3389/fmed.2025.15010251501025Predicting nosocomial pneumonia of patients with acute brain injury in intensive care unit using machine-learning modelsJunchen Pan0Zhen Yue1Jing Ji2Yongping You3Liqing Bi4Yun Liu5Xinglin Xiong6Genying Gu7Ming Chen8Shen Zhang9Department of Neurosurgery, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Information, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Nursing, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Neurosurgery, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Rehabilitation, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaEngineering Research Center of Health Service System Based on Ubiquitous Wireless Networks, Ministry of Education, Nanjing, ChinaIntroductionThe aim of this study is to construct and validate new machine learning models to predict pneumonia events in intensive care unit (ICU) patients with acute brain injury.MethodsAcute brain injury patients in ICU of hospitals from January 1, 2020, to December 31, 2021 were retrospective reviewed. Patients were divided into training, and validation sets. The primary outcome was nosocomial pneumonia infection during ICU stay. Machine learning models including XGBoost, DecisionTree, Random Forest, Light GBM, Adaptive Boost, BP, and TabNet were used for model derivation. The predictive value of each model was evaluated using accuracy, precision, recall, F1-score, and area under the curve (AUC), and internal and external validation was performed.ResultsA total of 280 ICU patients with acute brain injury were included. Five independent variables for nosocomial pneumonia infection were identified and selected for machine learning model derivations and validations, including tracheotomy time, antibiotic use days, blood glucose, ventilator-assisted ventilation time, and C-reactive protein. The training set revealed the superior and robust performance of the XGBoost with the highest AUC value (0.956), while the Random Forest and Adaptive Boost had the highest AUC value (0.883) in validation set.ConclusionMachine learning models can effectively predict the risk of nosocomial pneumonia infection in patients with acute brain injury in the ICU. Despite differences in populations and algorithms, the models we constructed demonstrated reliable predictive performance.https://www.frontiersin.org/articles/10.3389/fmed.2025.1501025/fullintensive care unitarea under the curveacute brain injurynosocomial pneumoniamachine-learning models |
| spellingShingle | Junchen Pan Zhen Yue Jing Ji Yongping You Liqing Bi Yun Liu Xinglin Xiong Genying Gu Ming Chen Shen Zhang Predicting nosocomial pneumonia of patients with acute brain injury in intensive care unit using machine-learning models Frontiers in Medicine intensive care unit area under the curve acute brain injury nosocomial pneumonia machine-learning models |
| title | Predicting nosocomial pneumonia of patients with acute brain injury in intensive care unit using machine-learning models |
| title_full | Predicting nosocomial pneumonia of patients with acute brain injury in intensive care unit using machine-learning models |
| title_fullStr | Predicting nosocomial pneumonia of patients with acute brain injury in intensive care unit using machine-learning models |
| title_full_unstemmed | Predicting nosocomial pneumonia of patients with acute brain injury in intensive care unit using machine-learning models |
| title_short | Predicting nosocomial pneumonia of patients with acute brain injury in intensive care unit using machine-learning models |
| title_sort | predicting nosocomial pneumonia of patients with acute brain injury in intensive care unit using machine learning models |
| topic | intensive care unit area under the curve acute brain injury nosocomial pneumonia machine-learning models |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1501025/full |
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