本研究通过特征选择的方法,分析肝癌患者术前临床信息,提高患者的预后模型的准确性。基于多类支持向量机递归特征消除(recursive feature elimination based on multiple support vector machine,MSVM-RFE)方法对进行过肝切除手术的原...本研究通过特征选择的方法,分析肝癌患者术前临床信息,提高患者的预后模型的准确性。基于多类支持向量机递归特征消除(recursive feature elimination based on multiple support vector machine,MSVM-RFE)方法对进行过肝切除手术的原发性肝癌患者的临床变量进行重要特征排序,使用5折交叉验证的支持向量机确定最优特征子集,构造原发性肝癌患者术后的1年、3年无瘤生存和总体生存的列线图。通过与临床医生沟通,确认特征排序结果为合理的。患者3年无瘤生存风险和总生存风险的列线图的一致性指数分别为0.701和0.706。使用多类支持向量机递归特征消除方法后的预测模型准确率有所提高,列线图在临床实践中能够提供患者生存风险信息,简单清晰的反映患者的生存风险。展开更多
目的探讨人高迁移率族蛋白B-1(human high mobility group protein B1,HMGB-1)在神经梅毒患者脑脊液中的表达。方法研究对象来自2017年9月至2021年9月广州市皮肤病防治所梅毒患者47例,其中神经梅毒组患者21例,非神经梅毒组患者26例。收...目的探讨人高迁移率族蛋白B-1(human high mobility group protein B1,HMGB-1)在神经梅毒患者脑脊液中的表达。方法研究对象来自2017年9月至2021年9月广州市皮肤病防治所梅毒患者47例,其中神经梅毒组患者21例,非神经梅毒组患者26例。收集两组患者脑脊液检测HMGB-1含量、脑脊液总蛋白量、白细胞数量进行甲苯胺红不加热血清试验(tolulized red unheated serum test,TRUST)、梅毒螺旋体明胶凝集试验(treponema pallidum particle agglutination,TPPA)、荧光梅毒螺旋体抗体吸收试验(fluoresent treponemal antibody-absorption test,FTA-ABS)。采用χ^(2)检验和独立样本t检验。结果神经梅毒组男18例,女3例,年龄(51.3±15.1)岁;非神经梅毒组男20例,女6例,年龄(48.6±8.3)岁。神经梅毒组患者脑脊液TRUST稀释倍数[(5.29±3.89)比(0.96±1.48),中位数4(2,8)比0(0,0)]、白细胞数量[(82.91±66.42)×10^(6)/L比(14.00±17.23)×10^(6)/L,中位数50.0(26.5,69.0)×10^(6)/L比3.0(2.0,4.0)×10^(6)/L]、总蛋白量[(714.57±134.69)mg/L比(288.22±115.20)mg/L]、HMGB-1[(1265.16±345.10)ng/L比(693.92±166.26)ng/L]均高于非神经梅毒组,差异均有统计学意义(均P<0.05)。神经梅毒组脑脊液HMGB-1与白细胞数量呈正相关(r=0.62,P<0.01)。结论神经梅毒患者脑脊液HMGB-1水平显著上升,有助于神经梅毒的诊断。展开更多
In case of machine learning,the problem of class imbalance is always troubling,i.e.one class of the samples has a larger magnitude than the other classes.This problem brings a preference of the classifier to the major...In case of machine learning,the problem of class imbalance is always troubling,i.e.one class of the samples has a larger magnitude than the other classes.This problem brings a preference of the classifier to the majority class,which leads to worse performance of the classifier on the minority class.We proposed an improved boosting tree(BT) algorithm for learning imbalanced data,called cost BT.In each iteration of the cost BT,only the weights of the misclassified minority class samples are increased.Meanwhile,the error rate in the weight formula of the base classifier is replaced by 1 minus F-measure.In this study,the performance of the cost BT algorithm is compared with other known methods on 9 public data sets.The compared methods include the decision tree and random forest algorithm,and both of them were combined with the sampling techniques such as synthetic minority oversampling technique(SMOTE),Borderline-SMOTE,adaptive synthetic sampling approach(ADASYN) and one sided selection.The cost BT algorithm performed better than the other compared methods in F-measure,G-mean and area under curve(AUC).In 6 of the 9 data sets,the cost BT algorithm has a superior performance to the other published methods.It can promote the prediction performance of the base classifiers by increasing the proportion of the minority class in the whole samples with only increasing the weights of the misclassified minority class samples in each iteration of the BT.In addition,computing the weights of the base classifiers with F-measure is helpful to the ensemble decisions.展开更多
文摘本研究通过特征选择的方法,分析肝癌患者术前临床信息,提高患者的预后模型的准确性。基于多类支持向量机递归特征消除(recursive feature elimination based on multiple support vector machine,MSVM-RFE)方法对进行过肝切除手术的原发性肝癌患者的临床变量进行重要特征排序,使用5折交叉验证的支持向量机确定最优特征子集,构造原发性肝癌患者术后的1年、3年无瘤生存和总体生存的列线图。通过与临床医生沟通,确认特征排序结果为合理的。患者3年无瘤生存风险和总生存风险的列线图的一致性指数分别为0.701和0.706。使用多类支持向量机递归特征消除方法后的预测模型准确率有所提高,列线图在临床实践中能够提供患者生存风险信息,简单清晰的反映患者的生存风险。
文摘目的探讨人高迁移率族蛋白B-1(human high mobility group protein B1,HMGB-1)在神经梅毒患者脑脊液中的表达。方法研究对象来自2017年9月至2021年9月广州市皮肤病防治所梅毒患者47例,其中神经梅毒组患者21例,非神经梅毒组患者26例。收集两组患者脑脊液检测HMGB-1含量、脑脊液总蛋白量、白细胞数量进行甲苯胺红不加热血清试验(tolulized red unheated serum test,TRUST)、梅毒螺旋体明胶凝集试验(treponema pallidum particle agglutination,TPPA)、荧光梅毒螺旋体抗体吸收试验(fluoresent treponemal antibody-absorption test,FTA-ABS)。采用χ^(2)检验和独立样本t检验。结果神经梅毒组男18例,女3例,年龄(51.3±15.1)岁;非神经梅毒组男20例,女6例,年龄(48.6±8.3)岁。神经梅毒组患者脑脊液TRUST稀释倍数[(5.29±3.89)比(0.96±1.48),中位数4(2,8)比0(0,0)]、白细胞数量[(82.91±66.42)×10^(6)/L比(14.00±17.23)×10^(6)/L,中位数50.0(26.5,69.0)×10^(6)/L比3.0(2.0,4.0)×10^(6)/L]、总蛋白量[(714.57±134.69)mg/L比(288.22±115.20)mg/L]、HMGB-1[(1265.16±345.10)ng/L比(693.92±166.26)ng/L]均高于非神经梅毒组,差异均有统计学意义(均P<0.05)。神经梅毒组脑脊液HMGB-1与白细胞数量呈正相关(r=0.62,P<0.01)。结论神经梅毒患者脑脊液HMGB-1水平显著上升,有助于神经梅毒的诊断。
基金supported by the National Key Research and Development Program of China(2018YFC0116902,2016YFC0901602)the National Natural Science Foundation of China(NSFC)(61876194)+2 种基金Joint Foundation for the NSFC and Guangdong Science Center for Big Data(U1611261)Medical Scientific Research Foundation of Guangdong Province of China(C2017037)Science and Technology Program of Guangzhou(201604020016)
文摘In case of machine learning,the problem of class imbalance is always troubling,i.e.one class of the samples has a larger magnitude than the other classes.This problem brings a preference of the classifier to the majority class,which leads to worse performance of the classifier on the minority class.We proposed an improved boosting tree(BT) algorithm for learning imbalanced data,called cost BT.In each iteration of the cost BT,only the weights of the misclassified minority class samples are increased.Meanwhile,the error rate in the weight formula of the base classifier is replaced by 1 minus F-measure.In this study,the performance of the cost BT algorithm is compared with other known methods on 9 public data sets.The compared methods include the decision tree and random forest algorithm,and both of them were combined with the sampling techniques such as synthetic minority oversampling technique(SMOTE),Borderline-SMOTE,adaptive synthetic sampling approach(ADASYN) and one sided selection.The cost BT algorithm performed better than the other compared methods in F-measure,G-mean and area under curve(AUC).In 6 of the 9 data sets,the cost BT algorithm has a superior performance to the other published methods.It can promote the prediction performance of the base classifiers by increasing the proportion of the minority class in the whole samples with only increasing the weights of the misclassified minority class samples in each iteration of the BT.In addition,computing the weights of the base classifiers with F-measure is helpful to the ensemble decisions.
