Caruana Rich, Niculescu Radu S, Rao R Bharat, Simms Cynthia
Cornell University, Computer Science, Ithaca, NY 14853, USA.
AMIA Annu Symp Proc. 2003;2003:135-9.
The C-section rate of a population of 22,175 expectant mothers is 16.8%; yet the 17 physician groups that serve this population have vastly different group C-section rates, ranging from 13% to 23%. Our goal is to determine retrospectively if the variations in the observed rates can be attributed to variations in the intrinsic risk of the patient sub-populations (i.e. some groups contain more "high-risk C-section" patients), or differences in physician practice (i.e. some groups do more C-sections). We apply machine learning to this problem by training models to predict standard practice from retrospective data. We then use the models of standard practice to evaluate the C-section rate of each physician practice. Our results indicate that although there is variation in intrinsic risk among the groups, there also is much variation in physician practice.
在22175名孕妇群体中，剖宫产率为16.8%；然而，为该群体服务的17个医生团队的剖宫产率却大不相同，从13%到23%不等。我们的目标是通过回顾性研究来确定，观察到的剖宫产率差异是可归因于患者亚群体的内在风险差异（即某些团队包含更多“高风险剖宫产”患者），还是医生执业差异（即某些团队进行更多剖宫产手术）。我们通过训练模型从回顾性数据中预测标准做法，将机器学习应用于此问题。然后，我们使用标准做法模型来评估每个医生团队的剖宫产率。我们的结果表明，虽然各团队之间存在内在风险差异，但医生执业差异也很大。
