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Preterm birth prediction : deriving stable and interpretable rules from high dimensional data

conference contribution
posted on 2016-08-19, 00:00 authored by Truyen TranTruyen Tran, Wei LuoWei Luo, Quoc-Dinh Phung, J Morris, K Rickard, Svetha VenkateshSvetha Venkatesh
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%.

History

Event

Machine Learning in Health Care. Conference (2016: Los Angeles, California)

Pagination

1 - 13

Publisher

[The Conference]

Location

Los Angeles, California

Place of publication

[Los Angeles, Calif.]

Start date

2016-08-19

End date

2016-08-20

Language

eng

Publication classification

E Conference publication; E1 Full written paper - refereed

Copyright notice

2016, The Authors

Title of proceedings

MLHC 2016 : Proceedings on Conference on Machine Learning in Healthcare

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