Research on multi-algorithm and explainable AI techniques for predictive modeling of acute spinal cord injury using multimodal data

作者：Jiaojiao Tai, Linbang Wang, Yijun Xie, Yang Li, Hua Fu, Xiaowen Ma, Haiyan Li, Xinying Li, Ziqiang Yan, Jingkun Liu · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-93006-4 · 被引用次数：6 · 研究领域：Medical Imaging and Analysis、Stroke Rehabilitation and Recovery、Advanced Technologies in Various Fields

Machine learning technology has been extensively applied in the medical field, particularly in the context of disease prediction and patient rehabilitation assessment. Acute spinal cord injury (ASCI) is a sudden trauma that frequently results in severe neurological deficits and a significant decline in quality of life. Early prediction of neurological recovery is crucial for the personalized treatment planning. While extensively explored in other medical fields, this study is the first to apply multiple machine learning methods and Shapley Additive Explanations (SHAP) analysis specifically to ASCI for predicting neurological recovery. A total of 387 ASCI patients were included, with clinical, imaging, and laboratory data collected. Key features were selected using univariate analysis, Lasso regression, and other feature selection techniques, integrating clinical, radiomics, and laboratory data. A range of machine learning models, including XGBoost, Logistic Regression, KNN, SVM, Decision Tree, Random Forest, LightGBM, ExtraTrees, Gradient Boosting, and Gaussian Naive Bayes, were evaluated, with Gaussian Naive Bayes exhibiting the best performance. Radiomics features extracted from T2-weighted fat-suppressed MRI scans, such as original_glszm_SizeZoneNonUniformity and wavelet-HLL_glcm_SumEntropy, significantly enhanced predictive accuracy. SHAP analysis identified critical clinical features, including IMLL, INR, BMI, Cys C, and RDW-CV, in the predictive model. The model was val...