Machine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb fractures
Abstract Postoperative pneumonia, a prevalent complication arising from lower limb fracture surgery, can significantly prolong hospitalization periods and elevate mortality rates. Consequently, early prevention and identification of this condition are crucial in improving patient prognosis. In this...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-04623-y |
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| author | Yiqun Chen Mingxuan Ma Dandan Qu Chunxiang Xu |
| author_facet | Yiqun Chen Mingxuan Ma Dandan Qu Chunxiang Xu |
| author_sort | Yiqun Chen |
| collection | DOAJ |
| description | Abstract Postoperative pneumonia, a prevalent complication arising from lower limb fracture surgery, can significantly prolong hospitalization periods and elevate mortality rates. Consequently, early prevention and identification of this condition are crucial in improving patient prognosis. In this study, clinical indicators pertaining to postoperative pneumonia in patients with lower limb fractures at Nantong University Hospital, spanning the years 2016 to 2023, were subjected to a analysis. The patients who encountered postoperative pneumonia subsequent to their lower limb fracture surgeries during hospitalization were categorized as the case group, whereas those who did not develop such a complication served as the control group. To forecast the likelihood of postoperative pneumonia occurrence, both machine learning and deep learning algorithms were employed. The study identified Age, Gender, Fracture type, Venous thromboembolism (VTE), Hypertension, Chronic obstructive pulmonary disease (COPD), Cancer, Atrial fibrillation, Cerebrovascular disease, Hypoalbuminemia, Free fatty acid, Albumin, Albumin to globulin ratio, Calcium, Fibrinogen, D-dimer, Alcohol, Surgical grade and C-reactive protein as significant predictors of postoperative pneumonia. XGBoost and Transformer models have better performance (AUC 0.866 VS 0.946, F1 0.807 VS 0.889), and both models have better substantial prediction ability for the occurrence of postoperative pneumonia. In conclusion, XGBoost and Transformer models serve as potential tools for the prevention and treatment of postoperative pneumonia in patients with lower-extremity fractures. By adopting appropriate health management practices, the risk of developing postoperative pneumonia in this patient population may be reduced. |
| format | Article |
| id | doaj-art-56dd1a65ef0841f69f3dde545fad07d4 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-56dd1a65ef0841f69f3dde545fad07d42025-08-20T03:45:26ZengNature PortfolioScientific Reports2045-23222025-07-0115111110.1038/s41598-025-04623-yMachine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb fracturesYiqun Chen0Mingxuan Ma1Dandan Qu2Chunxiang Xu3Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong UniversityDepartment of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong UniversityDepartment of Nursing, Affiliated Hospital of Nantong UniversityDepartment of Nursing, Affiliated Hospital of Nantong UniversityAbstract Postoperative pneumonia, a prevalent complication arising from lower limb fracture surgery, can significantly prolong hospitalization periods and elevate mortality rates. Consequently, early prevention and identification of this condition are crucial in improving patient prognosis. In this study, clinical indicators pertaining to postoperative pneumonia in patients with lower limb fractures at Nantong University Hospital, spanning the years 2016 to 2023, were subjected to a analysis. The patients who encountered postoperative pneumonia subsequent to their lower limb fracture surgeries during hospitalization were categorized as the case group, whereas those who did not develop such a complication served as the control group. To forecast the likelihood of postoperative pneumonia occurrence, both machine learning and deep learning algorithms were employed. The study identified Age, Gender, Fracture type, Venous thromboembolism (VTE), Hypertension, Chronic obstructive pulmonary disease (COPD), Cancer, Atrial fibrillation, Cerebrovascular disease, Hypoalbuminemia, Free fatty acid, Albumin, Albumin to globulin ratio, Calcium, Fibrinogen, D-dimer, Alcohol, Surgical grade and C-reactive protein as significant predictors of postoperative pneumonia. XGBoost and Transformer models have better performance (AUC 0.866 VS 0.946, F1 0.807 VS 0.889), and both models have better substantial prediction ability for the occurrence of postoperative pneumonia. In conclusion, XGBoost and Transformer models serve as potential tools for the prevention and treatment of postoperative pneumonia in patients with lower-extremity fractures. By adopting appropriate health management practices, the risk of developing postoperative pneumonia in this patient population may be reduced.https://doi.org/10.1038/s41598-025-04623-yPostoperative pneumoniaRisk factorsLower extremity fractureMachine learning modelTransformer |
| spellingShingle | Yiqun Chen Mingxuan Ma Dandan Qu Chunxiang Xu Machine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb fractures Scientific Reports Postoperative pneumonia Risk factors Lower extremity fracture Machine learning model Transformer |
| title | Machine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb fractures |
| title_full | Machine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb fractures |
| title_fullStr | Machine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb fractures |
| title_full_unstemmed | Machine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb fractures |
| title_short | Machine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb fractures |
| title_sort | machine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb fractures |
| topic | Postoperative pneumonia Risk factors Lower extremity fracture Machine learning model Transformer |
| url | https://doi.org/10.1038/s41598-025-04623-y |
| work_keys_str_mv | AT yiqunchen machinelearningandtransformermodelsforpredictionofpostoperativepneumoniariskinpatientswithlowerlimbfractures AT mingxuanma machinelearningandtransformermodelsforpredictionofpostoperativepneumoniariskinpatientswithlowerlimbfractures AT dandanqu machinelearningandtransformermodelsforpredictionofpostoperativepneumoniariskinpatientswithlowerlimbfractures AT chunxiangxu machinelearningandtransformermodelsforpredictionofpostoperativepneumoniariskinpatientswithlowerlimbfractures |