Stabilized sparse ordinal regression for medical risk stratification

Tran, Truyen, Phung, Dinh, Luo, Wei and Venkatesh, Svetha 2015, Stabilized sparse ordinal regression for medical risk stratification, Knowledge and information systems: an international journal, vol. 43, no. 3, pp. 555-582, doi: 10.1007/s10115-014-0740-4.

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Title Stabilized sparse ordinal regression for medical risk stratification
Author(s) Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Luo, WeiORCID iD for Luo, Wei orcid.org/0000-0002-4711-7543
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Journal name Knowledge and information systems: an international journal
Volume number 43
Issue number 3
Start page 555
End page 582
Total pages 28
Publisher Springer
Place of publication Berlin, Germany
Publication date 2015-06
ISSN 0219-1377
0219-3116
Keyword(s) electronic medical record
feature graph
medical risk stratification
sparse ordinal regression
stability
Summary The recent wide adoption of electronic medical records (EMRs) presents great opportunities and challenges for data mining. The EMR data are largely temporal, often noisy, irregular and high dimensional. This paper constructs a novel ordinal regression framework for predicting medical risk stratification from EMR. First, a conceptual view of EMR as a temporal image is constructed to extract a diverse set of features. Second, ordinal modeling is applied for predicting cumulative or progressive risk. The challenges are building a transparent predictive model that works with a large number of weakly predictive features, and at the same time, is stable against resampling variations. Our solution employs sparsity methods that are stabilized through domain-specific feature interaction networks. We introduces two indices that measure the model stability against data resampling. Feature networks are used to generate two multivariate Gaussian priors with sparse precision matrices (the Laplacian and Random Walk). We apply the framework on a large short-term suicide risk prediction problem and demonstrate that our methods outperform clinicians to a large margin, discover suicide risk factors that conform with mental health knowledge, and produce models with enhanced stability. © 2014 Springer-Verlag London.
Language eng
DOI 10.1007/s10115-014-0740-4
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30067690

Document type: Journal Article
Collection: Centre for Pattern Recognition and Data Analytics
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