文章摘要
杨志燕,黄天宝,王树山,林华日,周君艺.基于支持向量机的急性百草枯中毒预后模型的建立与评价[J].南京医科大学学报,2018,(10):1467~1471
基于支持向量机的急性百草枯中毒预后模型的建立与评价
Establishment and evaluation of prognostic model for patients with acute paraquat poisoning based on support vector machine
投稿时间:2017-09-14  
DOI:10.7655/NYDXBNS20181031
中文关键词: 百草枯中毒  预后  支持向量机  Logistic回归
英文关键词: paraquat poisoning  prognosis  support vector machine  logistic regression
基金项目:泉州市自然科学基金(Z【2014】0280)
作者单位
杨志燕 福建医科大学附属泉州第一医院急诊科福建 泉州 362000 
黄天宝 福建医科大学附属泉州第一医院急诊科福建 泉州 362000 
王树山 惠安县医院急诊科福建 泉州 362100 
林华日 永春县医院急诊科福建 泉州 362600 
周君艺 南安市医院急诊科福建 泉州 362300 
摘要点击次数: 169
全文下载次数: 293
中文摘要:
      目的:比较支持向量机(support vector machine,SVM)和传统的Logistic回归构建的急性百草枯(paraquat,PQ)中毒早期预后判别模型的预测性能。方法:收集急性PQ中毒患者152例,随访观察2个月的临床转归情况。应用随机数字表法以3∶2的比例分为两组,一组作为训练样本用于筛选变量和建立预测模型,计91例;另一组作为验证样本,用于评价模型预测效果,计61例。建模方法采用SVM和常规统计方法中的Logistic回归。结果:通过对PQ中毒患者的预测判别验证,线性核、多项式核、Sigmoid核及径向基函数核SVM模型的预测准确率分别为77.92%、74.03%、75.32%、79.22%。对所有预测模型性能对比显示,SVM模型预测性能高于Logistic回归模型,其中径向基核函数(RBF)?SVM模型效果最好,灵敏度为87.5%,特异度为70.6%。结论:采用SVM模型能更好地整合各种影响PQ中毒患者早期预后的信息,所建立的模型具有更好的预测能力,为预测PQ中毒患者的预后提供了一种新方法。
英文摘要:
      Objective:To compare the predictive performance of constructing the early prognostic model of acute paraquat(PQ)poisoning between support vector machine(SVM)and logistic regression. Methods:A total of 152 patients with acute PQ poisoning were collected and the clinical results were observed for 2 months. The patients were divided into two groups with a 3∶2 ratio by the random numerical table method. One group with a total of 91 cases was used as a training sample for selecting variables and establishing predictive models. Another group with a total of 61 cases was used as a validation sample to evaluate the predictive effect of the model. SVM and conventional logistic regression was used as the modeling method. Results:The prediction accuracy of the kernel,polynomial,sigmoid kernel and radial basis function nuclear SVM model was 77.92%,74.03%,75.32% and 79.22% respectively,when being tested by the validation group. The results of performance comparison showed that SVM models performed better than logistic regression model;RBF?SVM was the best among all the models with a sensitivity of 87.5% and a specificity of 70.6%. Conclusion:SVM model could preferably integrate all kinds of prognostic information of PQ poisoning patients,and the established model had better prediction ability,providing a new method for predicting the prognosis of patients with PQ poisoning.
查看全文   查看/发表评论  下载PDF阅读器
关闭