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Preterm Birth Prediction: Stable Selection of Interpretable Rules from High Dimensional Data

Truyen Tran, Wei Luo, Dinh Phung, Jonathan Morris, Kristen Rickard, Svetha Venkatesh
Proceedings of the 1st Machine Learning for Healthcare Conference, pp. 164–177, 2016

Abstract

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%.

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