• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>MEDINFO >Causal Discovery Using A Bayesian Local Causal Discovery Algorithm
【24h】

Causal Discovery Using A Bayesian Local Causal Discovery Algorithm

机译：因果发现使用贝叶斯本地因果发现算法

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This study focused on the development and application of an efficient algorithm to induce causal relationships from observational data. The algorithm, called BLCD, is based on a causal Bayesian network framework. BLCD initially uses heuristic greedy search to derive the Markov Blanket (MB) of a node that serves as the "locality "for the identification of pair-wise causal relationships. BLCD takes as input a dataset and outputs potential causes of the form variable X causally influences variable Y. Identification of the causal factors of diseases and outcomes, can help formulate better management, prevention and control strategies for the improvement of health care. In this study we focused on investigating factors that may contribute causally to infant mortality in the United States. We used the U.S. Linked Birth/Infant Death dataset for 1991 with more than four million records and about 200 variables for each record. Our sample consisted of 41,155 records randomly selected from the whole dataset. Each recordhad maternal, paternal and child factors and the outcome at the end of the first year-whether the infant survived or not. Using the infant birth and death dataset as input, BLCD output six purported causal relationships. Three out of the six relationships seem plausible. Even though we have not yet discovered a clinically novel causal link, we plan to look for novel causal pathways using the full sample.
机译：本研究专注于开发和应用高效算法，从观察数据引起因果关系。该算法称为BLCD，基于因果贝叶斯网络框架。 BLCD最初使用启发式贪婪搜索来导出节点的Markov毯子（MB），该节点用作“地区”，用于识别配对的因果关系。 BLCD作为输入数据集并输出表单变量X的潜在原因因果影响变量Y.鉴定疾病和结果的因果因素，可以帮助制定改善医疗保健的更好的管理，预防和控制策略。在这项研究中，我们专注于调查可能导致美国婴儿死亡率的因素。我们使用了1991年的美国链接的出生/婴儿死亡数据集，每个记录超过400万条记录和大约200个变量。我们的示例由整个数据集随机选择的41,155条记录组成。每个唱片母亲，父亲，儿童因素以及第一年结束时的结果 - 无论婴儿是否幸存下来。使用婴儿出生和死亡数据集作为输入，BLCD输出六个声称的因果关系。六个关系中的三个似乎是合理的。即使我们尚未发现临床新的因果关系，我们计划使用完整样品寻找新的因果途径。

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