What patient information allows us to make accurate predictions of outcome?

Only some of the information contained in a medical record will be useful to the prediction of patient outcome. The authors describe a novel method for selecting those outcome predictors which allow them to reliably discriminate between adverse and benign end results. Using the area under the receiver operating characteristic as a nonparametric measure of discrimination, the authors show how to calculate the maximum discrimination attainable with a given set of discrete valued features. This upper limit forms the basis of their feature selection algorithm. They use the algorithm to select features (from maternity records) relevant to the prediction of failure to progress in labour. The results of this analysis motivate investigation of those predictors of failure to progress relevant to parous and nulliparous-sub-populations.