Guo Junshuang, Zhang Ruike, Dong Ruirui, Yang Fan, Wang Yating, Miao Wang
Neuro-Intensive Care Unit of the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, People's Republic of China.
Department of Immunology, School of Basic Medical Science, Central South University, Changsha City, Hunan Province, People's Republic of China.
J Inflamm Res. 2024 Sep 2;17:5901-5913. doi: 10.2147/JIR.S471626. eCollection 2024.
Machine learning (ML) is increasingly used in medical predictive modeling, but there are no studies applying ML to predict prognosis in Guillain-Barré syndrome (GBS).
The medical records of 223 patients with GBS were analyzed to construct predictive models that affect patient prognosis. Least Absolute Shrinkage and Selection Operator (LASSO) was used to filter the variables. Decision Trees (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), k-nearest Neighbour (KNN), Naive Bayes (NB), Neural Network (NN). Light Gradient Boosting Machine (LGBM) and Logistic Regression (LR) were used to construct predictive models. Clinical data from 55 GBS patients were used to validate the model. SHapley additive explanation (SHAP) analysis was used to explain the model. Single sample gene set enrichment analysis (ssGSEA) was used for immune cell infiltration analysis.
The AUCs (area under the curves) of the 8 ML algorithms including DT, RF, XGBoost, KNN, NB, NN, LGBM and LR were as follows: 0.75, 0.896 0.874, 0.666, 0.742, 0.765, 0.869 and 0.744. The accuracy of XGBoost (0.852) was the highest, followed by LGBM (0.803) and RF (0.758), with F1 index of 0.832, 0.794, and 0.667, respectively. The results of the validation set data analysis showed AUCs of 0.839, 0.919, and 0.733 for RF, XGBoost, and LGBM, respectively. SHAP analysis showed that the SHAP values of blood neutrophil/lymphocyte ratio (NLR), age, mechanical ventilation, hyporeflexia and abnormal glossopharyngeal vagus nerve were 0.821, 0.645, 0.517, 0.401 and 0.109, respectively.
The combination of NLR, age, mechanical ventilation, hyporeflexia and abnormal glossopharyngeal vagus used to predict short-term prognosis in patients with GBS has a good predictive value.
机器学习（ML）在医学预测建模中的应用日益广泛，但尚无将ML应用于预测吉兰-巴雷综合征（GBS）预后的研究。
分析223例GBS患者的病历，构建影响患者预后的预测模型。采用最小绝对收缩和选择算子（LASSO）进行变量筛选。使用决策树（DT）、随机森林（RF）、极端梯度提升（XGBoost）、k近邻（KNN）、朴素贝叶斯（NB）、神经网络（NN）、轻梯度提升机（LGBM）和逻辑回归（LR）构建预测模型。使用55例GBS患者的临床数据对模型进行验证。采用SHapley加法解释（SHAP）分析对模型进行解释。采用单样本基因集富集分析（ssGSEA）进行免疫细胞浸润分析。
DT、RF、XGBoost、KNN、NB、NN、LGBM和LR这8种ML算法的曲线下面积（AUC）分别为：0.75、0.896、0.874、0.666、0.742、0.765、0.869和0.744。XGBoost的准确率最高（0.852），其次是LGBM（0.803）和RF（0.758），F1指数分别为0.832、0.794和0.667。验证集数据分析结果显示，RF、XGBoost和LGBM的AUC分别为0.839、0.919和0.733。SHAP分析显示，血液中性粒细胞/淋巴细胞比值（NLR）、年龄、机械通气、反射减退和舌咽迷走神经异常的SHAP值分别为0.821、0.645、0.517、0.4
联合NLR、年龄、机械通气、反射减退和舌咽迷走神经异常用于预测GBS患者的短期预后具有良好的预测价值。
