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Preterm births occur at an alarming rate of 10-15%. Preemies have a higherrisk of infant mortality, developmental retardation and long-term disabilities.Predicting preterm birth is difficult, even for the most experiencedclinicians. The most well-designed clinical study thus far reaches a modestsensitivity of 18.2-24.2% at specificity of 28.6-33.3%. We take a differentapproach by exploiting databases of normal hospital operations. We aims aretwofold: (i) to derive an easy-to-use, interpretable prediction rule withquantified uncertainties, and (ii) to construct accurate classifiers forpreterm birth prediction. Our approach is to automatically generate and selectfrom hundreds (if not thousands) of possible predictors using stability-awaretechniques. Derived from a large database of 15,814 women, our simplifiedprediction rule with only 10 items has sensitivity of 62.3% at specificity of81.5%.

Preterm Birth Prediction: Deriving Stable and Interpretable Rules from High Dimensional Data