论文标题
一种学习关系政策的初步方法，用于管理重病的孩子
A Preliminary Approach for Learning Relational Policies for the Management of Critically Ill Children
论文作者
论文摘要
电子健康记录的使用增加使从患者记录中自动提取医疗政策，以帮助开发临床决策支持系统。我们改编了一个增强的统计关系学习（SRL）框架，以从临床医院记录中学习概率规则，以管理患有严重心脏或呼吸衰竭儿童的生理参数，这些儿童通过体外膜氧合进行了管理。在这项初步研究中，结果是有希望的。特别是，该算法返回了与医学推理一致的医疗行动逻辑规则。
The increased use of electronic health records has made possible the automated extraction of medical policies from patient records to aid in the development of clinical decision support systems. We adapted a boosted Statistical Relational Learning (SRL) framework to learn probabilistic rules from clinical hospital records for the management of physiologic parameters of children with severe cardiac or respiratory failure who were managed with extracorporeal membrane oxygenation. In this preliminary study, the results were promising. In particular, the algorithm returned logic rules for medical actions that are consistent with medical reasoning.
