Mining interpretable and predictive diagnosis codes from multi-source electronic health records

Sanjoy Dey, Gyorgy Simon, Bonnie Westra, Michael Steinbach, Vipin Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Mining patterns from electronic health-care records (EHR) can potentially lead to better and more cost-effective treatments. We aim to find the groups of ICD-9 diagnosis codes from EHRs that can predict the improvement of urinary incontinence of home health care (HHC) patients and also are interpretable to domain experts. In this paper, we propose two approaches for increasing the interpretability of the obtained groups of ICD-9 codes. First, we incorporate prior information available from clinical domain knowledge using the clinical classification system (CCS). Second, we incorporate additional types of clinical information for the same patients, such as demographic, behavioral, physiological, and psycho-social variables available from survey questions during the hospital visits. Finally, we develop a hybrid framework that can combine both prior information and the datadriven clinical information in the predictive model framework. Our results obtained from a large-scale EHR data set show that the hybrid framework enhances clinical interpretability as compared to the baseline model obtained from ICD-9 codes only, while achieving almost the same predictive capability.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining 2014, SDM 2014
EditorsPang Ning-Tan, Arindam Banerjee, Srinivasan Parthasarathy, Zoran Obradovic, Chandrika Kamath, Mohammed Zaki
PublisherSociety for Industrial and Applied Mathematics Publications
Pages1055-1063
Number of pages9
ISBN (Electronic)9781510811515
DOIs
StatePublished - 2014
Event14th SIAM International Conference on Data Mining, SDM 2014 - Philadelphia, United States
Duration: Apr 24 2014Apr 26 2014

Publication series

NameSIAM International Conference on Data Mining 2014, SDM 2014
Volume2

Other

Other14th SIAM International Conference on Data Mining, SDM 2014
CountryUnited States
CityPhiladelphia
Period4/24/144/26/14

Bibliographical note

Funding Information:
This work was supported by NSF grant IIS-1344135. The first author was also funded by the Doctoral Dissertation Fellowship 2013- 14 awarded by the University of Minnesota.

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