Preterm birth prediction: Stable selection of interpretable rules from high dimensional data

Truyen Tran, Wei Luo, Dinh Phung, Jonathan Morris, Kristen Rickard, Svetha Venkatesh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch


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%.
Original languageEnglish
Title of host publication1st Machine Learning for Healthcare Conference
Subtitle of host publication19-20 August 2016, Children's Hospital LA, Los Angeles, CA, USA
EditorsFinale Doshi-Velez, Jim Fackler, David Kale, Byron Wallace, Jenna Wiens
Place of PublicationSheffield UK
PublisherProceedings of Machine Learning Research (PMLR)
Number of pages13
ISBN (Electronic)2640-3498
Publication statusPublished - 2016
Externally publishedYes
EventMachine Learning for Healthcare Conference 2016 - Los Angeles, United States of America
Duration: 19 Aug 201620 Aug 2016
Conference number: 1st


ConferenceMachine Learning for Healthcare Conference 2016
Country/TerritoryUnited States of America
CityLos Angeles
Internet address

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