使用机器学习算法预测乙型肝炎表面抗原血清清除率。

StephenW

Comput Math Methods Med. 2019 Jun 11;2019:6915850. doi: 10.1155/2019/6915850. eCollection 2019.
Using Machine Learning Algorithms to Predict Hepatitis B Surface Antigen Seroclearance.
Tian X1, Chong Y2, Huang Y3, Guo P4, Li M1, Zhang W5, Du Z1, Li X2, Hao Y1,6.
Author information
Abstract

Hepatitis B surface antigen (HBsAg) seroclearance during treatment is associated with a better prognosis among patients with chronic hepatitis B (CHB). Significant gaps remain in our understanding on how to predict HBsAg seroclearance accurately and efficiently based on obtainable clinical information. This study aimed to identify the optimal model to predict HBsAg seroclearance. We obtained the laboratory and demographic information for 2,235 patients with CHB from the South China Hepatitis Monitoring and Administration (SCHEMA) cohort. HBsAg seroclearance occurred in 106 patients in total. We developed models based on four algorithms, including the extreme gradient boosting (XGBoost), random forest (RF), decision tree (DCT), and logistic regression (LR). The optimal model was identified by the area under the receiver operating characteristic curve (AUC). The AUCs for XGBoost, RF, DCT, and LR models were 0.891, 0.829, 0.619, and 0.680, respectively, with XGBoost showing the best predictive performance. The variable importance plot of the XGBoost model indicated that the level of HBsAg was of high importance followed by age and the level of hepatitis B virus (HBV) DNA. Machine learning algorithms, especially XGBoost, have appropriate performance in predicting HBsAg seroclearance. The results showed the potential of machine learning algorithms for predicting HBsAg seroclearance utilizing obtainable clinical data.

PMID:
    31281411
PMCID:
    PMC6594274
DOI:
    10.1155/2019/6915850

StephenW

计算数学方法Med。 2019年6月11日; 2019年：6915850。 doi：10.1155 / 2019/6915850。 eCollection 2019。
使用机器学习算法预测乙型肝炎表面抗原血清清除率。
Tian X1，Chong Y2，Huang Y3，Guo P4，Li M1，Zhang W5，Du Z1，Li X2，Hao Y1,6。
作者信息
抽象

治疗期间的乙型肝炎表面抗原（HBsAg）血清清除率与慢性乙型肝炎（CHB）患者的预后相关。根据可获得的临床信息，我们对如何准确有效地预测HBsAg血清清除率的理解存在重大差距。确定预测HBsAg血清清除率的最佳模型。我们从华南肝炎监测和管理（SCHEMA）队列中获得了2,235名CHB患者的实验室和人口统计学信息。 HBsAg血清清除总共发生在106例患者中。我们开发了基于四种算法的模型，包括极端梯度增强（XGBoost），随机森林（RF），决策树（DCT）和逻辑回归（LR）。通过接收器操作特性曲线（AUC）下的面积识别最佳模型。 XGBoost，RF，DCT和LR模型的AUC分别为0.891,0.829,0.619和0.680，XGBoost显示最佳预测XGBoost模型的变量重要性表明HBsAg水平高度重要，其次是年龄和乙型肝炎病毒（HBV）DNA的水平。机器学习算法，尤其是XGBoost，在预测HBsAg血清清除方面具有适当的性能。结果显示机器学习算法利用可获得的临床数据预测HBsAg血清清除的潜力。

结论：
31281411
PMCID：
PMC6594274
DOI：
10.1155 /六百九十一万五千八百五十零分之二千零十九

StephenW

downloads.hindawi.com/journals/cmmm/2019/6915850.pdf

乙肝人1949

极其无聊的职称论文

乙肝人1949

AHp，神经网络，这些无聊的模型搞在医效中，有卵用