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Improved risk predictions via sparse imputation of patient conditions in electronic medical records

Saha, Budhaditya, Gupta, Sunil and Venkatesh, Svetha 2015, Improved risk predictions via sparse imputation of patient conditions in electronic medical records, in DSAA 2015: IEEE International Conference on Data Science and Advanced Analytics, IEEE, Piscataway, N.J., pp. 1-10, doi: 10.1109/DSAA.2015.7344790.

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Title Improved risk predictions via sparse imputation of patient conditions in electronic medical records
Author(s) Saha, BudhadityaORCID iD for Saha, Budhaditya orcid.org/0000-0001-8011-6801
Gupta, SunilORCID iD for Gupta, Sunil orcid.org/0000-0002-3308-1930
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Data Science and Advanced Analytics. Conference (2015 : Paris, France)
Conference location Paris, France
Conference dates 19-21 Oct. 2015
Title of proceedings DSAA 2015: IEEE International Conference on Data Science and Advanced Analytics
Editor(s) Gaussier, Eric
Cao, Longbing
Gallinari, Patrick
Kwok, James
Pasi, Gabriella
Zaiane, Osmar
Publication date 2015
Start page 1
End page 10
Total pages 10
Publisher IEEE
Place of publication Piscataway, N.J.
Summary Electronic Medical Records (EMR) are increasingly used for risk prediction. EMR analysis is complicated by missing entries. There are two reasons - the “primary reason for admission” is included in EMR, but the co-morbidities (other chronic diseases) are left uncoded, and, many zero values in the data are accurate, reflecting that a patient has not accessed medical facilities. A key challenge is to deal with the peculiarities of this data - unlike many other datasets, EMR is sparse, reflecting the fact that patients have some, but not all diseases. We propose a novel model to fill-in these missing values, and use the new representation for prediction of key hospital events. To “fill-in” missing values, we represent the feature-patient matrix as a product of two low rank factors, preserving the sparsity property in the product. Intuitively, the product regularization allows sparse imputation of patient conditions reflecting common comorbidities across patients. We develop a scalable optimization algorithm based on Block coordinate descent method to find an optimal solution. We evaluate the proposed framework on two real world EMR cohorts: Cancer (7000 admissions) and Acute Myocardial Infarction (2652 admissions). Our result shows that the AUC for 3 months admission prediction is improved significantly from (0.741 to 0.786) for Cancer data and (0.678 to 0.724) for AMI data. We also extend the proposed method to a supervised model for predicting of multiple related risk outcomes (e.g. emergency presentations and admissions in hospital over 3, 6 and 12 months period) in an integrated framework. For this model, the AUC averaged over outcomes is improved significantly from (0.768 to 0.806) for Cancer data and (0.685 to 0.748) for AMI data.
ISBN 9781467382724
Language eng
DOI 10.1109/DSAA.2015.7344790
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 ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30079187

Document type: Conference Paper
Collection: Centre for Pattern Recognition and Data Analytics
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