title: Machine learning for sub-population assessment: evaluating the C-section rate of different physician practices.
abstract: We apply machine learning to the problem of subpopulation assessment for Caesarian Section. In subpopulation assessment, we are interested in making predictions not for a single patient, but for groups of patients. Typically, in any large population, different subpopulations will have different "outcome" rates. In our example, the C-section rate of a population of 22,176 expectant mothers is 16.8%; yet, the 17 physician groups that serve this population have vastly different group C-section rates, ranging from 11% to 23%. The ultimate goal of subpopulation assessment is to determine if these variations in the observed rates can be attributed to (a) variations in intrinsic risk of the patient sub-populations (i.e. some groups contain more "high-risk C-section" patients), or (b) differences in physician practice (i.e. some groups do more C-sections). Our results indicate that although there is some variation in intrinsic risk, there is also much variation in physician practice.
authors.affiliation: Cornell University, Computer Science, Ithaca, NY, USA.
authors.forename: Rich
authors.initials: R
authors.lastname: Caruana
authors.forename: Radu S
authors.initials: RS
authors.lastname: Niculescu
authors.forename: R Bharat
authors.initials: RB
authors.lastname: Rao
authors.forename: Cynthia
authors.initials: C
authors.lastname: Simms
mesh_terms: D001185:Artificial Intelligence; D002585:Cesarean Section / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D003663:Decision Trees; D005260:Female; D006801:Humans; D016571:Neural Networks, Computer; D010818:Practice Patterns, Physicians' / Q000706:statistics & numerical data*; D011247:Pregnancy