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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
Author(s) Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Luo, WeiORCID iD for Luo, Wei orcid.org/0000-0002-4711-7543
Phung, Quoc-DinhORCID iD for Phung, Quoc-Dinh orcid.org/0000-0002-9977-8247
Morris, Jonathan
Rickard, Kristen
Venkatesh, Svetha
Conference name Machine Learning in Health Care. Conference (2016: Los Angeles, California)
Conference location Los Angeles, California
Conference dates 19-20 Aug. 2016
Title of proceedings 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
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, The Authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30085681

Document type: Conference Paper
Collections: Centre for Pattern Recognition and Data Analytics
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Created: Wed, 12 Oct 2016, 09:34:54 EST

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