Preterm Birth Prediction: Deriving Stable and Interpretable Rules from High Dimensional Data
This addresses the challenge of preterm birth prediction for clinicians and healthcare systems, offering a more accurate and interpretable tool compared to prior methods.
The paper tackled the problem of predicting preterm births, which are difficult to predict with existing clinical methods, by using hospital operation data to derive an interpretable rule, achieving a sensitivity of 62.3% at specificity of 81.5% with only 10 items.
Preterm births occur at an alarming rate of 10-15%. Preemies have a higher risk of infant mortality, developmental retardation and long-term disabilities. Predicting preterm birth is difficult, even for the most experienced clinicians. The most well-designed clinical study thus far reaches a modest sensitivity of 18.2-24.2% at specificity of 28.6-33.3%. We take a different approach by exploiting databases of normal hospital operations. We aims are twofold: (i) to derive an easy-to-use, interpretable prediction rule with quantified uncertainties, and (ii) to construct accurate classifiers for preterm birth prediction. Our approach is to automatically generate and select from hundreds (if not thousands) of possible predictors using stability-aware techniques. Derived from a large database of 15,814 women, our simplified prediction rule with only 10 items has sensitivity of 62.3% at specificity of 81.5%.