■机器学习(ML)越来越多地用于医学预测建模,但尚无应用ML预测格林-巴利综合征(GBS)预后的研究。
■分析了223例GBS患者的病历,以构建影响患者预后的预测模型。使用最小绝对收缩和选择算子(LASSO)来过滤变量。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)。采用光梯度提升机(LGBM)和Logistic回归(LR)构建预测模型。来自55名GBS患者的临床数据用于验证该模型。SHapley加性解释(SHAP)分析用于解释模型。单样品基因组富集分析(ssGSEA)用于免疫细胞浸润分析。
■包括DT在内的8种ML算法的AUC(曲线下面积),射频,XGBoost,KNN,NB,NN,LGBM和LR分别为:0.75、0.896、0.874、0.666、0.742、0.765、0.869和0.744。XGBoost(0.852)的精度最高,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的AUC为0.839、0.919和0.733,XGBoost,和LGBM,分别。SHAP分析显示血液中性粒细胞/淋巴细胞比值(NLR)的SHAP值,年龄,机械通气,反射减退和舌咽迷走神经异常分别为0.821、0.645、0.517、0.401和0.109。
■NLR的组合,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。
UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.