...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Expert Systems with Application >Prediction of survival probabilities with Bayesian Decision Trees
【24h】

Prediction of survival probabilities with Bayesian Decision Trees

机译：贝叶斯决策树的生存概率预测

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Practitioners use Trauma and Injury Severity Score (TRISS) models for predicting the survival probability of an injured patient. The accuracy of TRISS predictions is acceptable for patients with up to three typical injuries, but unacceptable for patients with a larger number of injuries or with atypical injuries. Based on a regression model, the TRISS methodology does not provide the predictive density required for accurate assessment of risk. Moreover, the regression model is difficult to interpret. We therefore consider Bayesian inference for estimating the predictive distribution of survival. The inference is based on decision tree models which recursively split data along explanatory variables, and so practitioners can understand these models. We propose the Bayesian method for estimating the predictive density and show that it outperforms the TRISS method in terms of both goodness-of-fit and classification accuracy. The developed method has been made available for evaluation purposes as a stand-alone application.
机译：从业者使用创伤和伤害严重度评分（TRISS）模型来预测受伤患者的生存可能性。 TRISS预测的准确性对于多达三个典型伤害的患者是可以接受的，但是对于受伤数量较大或不典型的患者则不可接受。基于回归模型，TRISS方法无法提供准确评估风险所需的预测密度。而且，回归模型很难解释。因此，我们考虑贝叶斯推论来估计生存的预测分布。该推论基于决策树模型，该模型将数据沿解释变量递归拆分，因此从业人员可以理解这些模型。我们提出了用于估计预测密度的贝叶斯方法，并显示出在拟合优度和分类准确性方面都优于TRISS方法。已开发的方法可作为独立应用程序用于评估目的。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号