ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2023
Machine learning for the prediction of postoperative nosocomial pulmonary infection in patients with spinal cord injury.
Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 14 citations in OpenAlex.
- Development and External Validation of an Explainable Machine-Learning Model for Predicting Postoperative Pulmonary Complications in Older Adults Undergoing Degenerative Spine Surgery.Journal of clinical medicine · 2026Article
- Machine learning prediction of postoperative pulmonary infection in patients who underwent thoracoscopic lung cancer resection: a retrospective case-control study.BMC medical informatics and decision making · 2026Article
- Predicting Spinal Cord Injury Prognosis Using Machine Learning: Systematic Review and Meta-Analysis.JMIR AI · 2025Review
- Incidence, risk factors, and predictive modeling of pulmonary infection after high-risk surgery for lung cancer: a retrospective case-control study.Journal of thoracic disease · 2025Article
- Prediction model for postoperative pulmonary complications after thoracoscopic surgery with machine learning algorithms and SHapley Additive exPlanations.Journal of thoracic disease · 2025Article
- XGBoost model for predicting erectile dysfunction risk after radical prostatectomy: development and validation using machine learning.Discover oncology · 2025Article
- Advancing infection prevention and control through artificial intelligence: a scoping review of applications, barriers, and a decision-support checklist.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2025Article
- Non-invasive detection of Parkinson's disease based on speech analysis and interpretable machine learning.Frontiers in aging neuroscience · 2025Article
- Plasma fibrinogen level is independent risk factor associated with the incidence of pulmonary infection in patients with spinal cord injury: a retrospective cohort study.BMC pulmonary medicine · 2024Article
- A nomogram for predicting the hospital-acquired infections in children with spinal cord injuries: a retrospective, multicenter, observational study.Spinal cord · 2024Observational
- Development and validation of a lung biological equivalent dose-based multiregional radiomic model for predicting symptomatic radiation pneumonitis after SBRT in lung cancer patients.Frontiers in oncology · 2024Article
- Predicting postoperative pulmonary infection risk in patients with diabetes using machine learning.Frontiers in physiology · 2024Article
- Construction and validation of a machine learning model for the diagnosis of juvenile idiopathic arthritis based on fecal microbiota.Frontiers in cellular and infection microbiology · 2024Article
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10 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeThe purpose of this study was to establish the best prediction model for postoperative nosocomial pulmonary infection through machine learning (ML) and assist physicians to make accurate diagnosis and treatment decisions.
methodsPatients with spinal cord injury (SCI) who admitted to a general hospital between July 2014 and April 2022 were included in this study. The data were segmented according to the ratio of seven to three, 70% were randomly selected to train the model, and the other 30% were used for testing. We used LASSO regression to screen the variables, and the selected variables were used in the construction of six different ML models. Shapley additive explanations and permutation importance were used to explain the output of the ML models. Finally, sensitivity, specificity, accuracy and area under receiver operating characteristic curve (AUC) were used as the evaluation index of the model.
resultsA total of 870 patients were enrolled in this study, of whom 98 (11.26%) developed pulmonary infection. Seven variables were used for ML model construction and multivariate logistic regression analysis. Among these variables, age, ASIA scale and tracheotomy were found to be the independent risk factors for postoperative nosocomial pulmonary infection in SCI patients. Meanwhile, the prediction model based on RF algorithm performed best in the training and test sets. (AUC = 0.721, accuracy = 0.664, sensitivity = 0.694, specificity = 0.656).
conclusionAge, ASIA scale and tracheotomy were the independent risk factors of postoperative nosocomial pulmonary infection in SCI. The prediction model based on RF algorithm had the best performance.
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