دورية أكاديمية
Prediction model for spinal cord injury in spinal tuberculosis patients using multiple machine learning algorithms: a multicentric study
| العنوان: | Prediction model for spinal cord injury in spinal tuberculosis patients using multiple machine learning algorithms: a multicentric study |
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| المؤلفون: | Sitan Feng, Shujiang Wang, Chong Liu, Shaofeng Wu, Bin Zhang, Chunxian Lu, Chengqian Huang, Tianyou Chen, Chenxing Zhou, Jichong Zhu, Jiarui Chen, Jiang Xue, Wendi Wei, Xinli Zhan |
| المصدر: | Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024) |
| بيانات النشر: | Nature Portfolio, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Spinal tuberculosis, Spinal cord injury, Machine learning, Predictive model, Model interpretation, Model deployment, Medicine, Science |
| الوصف: | Abstract Spinal cord injury (SCI) is a prevalent and serious complication among patients with spinal tuberculosis (STB) that can lead to motor and sensory impairment and potentially paraplegia. This research aims to identify factors associated with SCI in STB patients and to develop a clinically significant predictive model. Clinical data from STB patients at a single hospital were collected and divided into training and validation sets. Univariate analysis was employed to screen clinical indicators in the training set. Multiple machine learning (ML) algorithms were utilized to establish predictive models. Model performance was evaluated and compared using receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration curve analysis, decision curve analysis (DCA), and precision-recall (PR) curves. The optimal model was determined, and a prospective cohort from two other hospitals served as a testing set to assess its accuracy. Model interpretation and variable importance ranking were conducted using the DALEX R package. The model was deployed on the web by using the Shiny app. Ten clinical characteristics were utilized for the model. The random forest (RF) model emerged as the optimal choice based on the AUC, PRs, calibration curve analysis, and DCA, achieving a test set AUC of 0.816. Additionally, MONO was identified as the primary predictor of SCI in STB patients through variable importance ranking. The RF predictive model provides an efficient and swift approach for predicting SCI in STB patients. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2045-2322 |
| Relation: | https://doaj.org/toc/2045-2322 |
| DOI: | 10.1038/s41598-024-56711-0 |
| URL الوصول: | https://doaj.org/article/bc47bd56f2be47dda0fd47abd88f11b2 |
| رقم الأكسشن: | edsdoj.bc47bd56f2be47dda0fd47abd88f11b2 |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 20452322 |
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| DOI: | 10.1038/s41598-024-56711-0 |