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. Attached Files Name Description MIMEType Size Downloads Title Preterm birth prediction : deriving stable and interpretable rules from high dimensional data Author(s) Tran, Truyen orcid.org/0000-0001-6531-8907 Luo, Wei orcid.org/0000-0002-4711-7543 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 Open Access Checking Related Links Link Description Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 25 Aug 2016, 17:14:19 EST Thu, 25 Aug 2016, 17:15:32 EST Fri, 26 Aug 2016, 05:00:59 EST Wed, 12 Oct 2016, 09:19:52 EST Wed, 12 Oct 2016, 09:21:09 EST Wed, 12 Oct 2016, 09:34:54 EST Thu, 13 Oct 2016, 05:10:12 EST Mon, 24 Oct 2016, 13:22:40 EST Tue, 25 Oct 2016, 11:20:20 EST Tue, 25 Oct 2016, 11:23:55 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 133 Abstract Views, 7 File Downloads  -  Detailed Statistics Created: Wed, 12 Oct 2016, 09:34:54 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.