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Preterm Birth Prediction: Stable Selection of Interpretable Rules from High Dimensional Data
2016
Machine Learning in Health Care
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,
dblp:conf/mlhc/TranLPMRV16
fatcat:qmojonxei5evxafd27yrdu4ctq