Preterm birth prediction : deriving stable and interpretable rules from high dimensional data
Tran, Truyen, Luo, Wei, Phung, Quoc-Dinh, Morris, Jonathan, Rickard, Kristen and Venkatesh, Svetha 2016, Preterm birth prediction : deriving stable and interpretable rules from high dimensional data, in MLHC 2016 : Proceedings on Conference on Machine Learning in Healthcare, [The Conference], [Los Angeles, Calif.], pp. 1-13.
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Title
Preterm birth prediction : deriving stable and interpretable rules from high dimensional data
MLHC 2016 : Proceedings on Conference on Machine Learning in Healthcare
Publication date
2016
Start page
1
End page
13
Total pages
13
Publisher
[The Conference]
Place of publication
[Los Angeles, Calif.]
Summary
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%.
Language
eng
Field of Research
080109 Pattern Recognition and Data Mining
Socio Economic Objective
970108 Expanding Knowledge in the Information and Computing Sciences
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