Co-occurrence of medical conditions: Exposing patterns through probabilistic topic modeling of snomed codes

Co-occurrence of medical conditions: Exposing patterns through probabilistic topic modeling of... Journal of Biomedical Informatics 82 (2018) 31–40 Contents lists available at ScienceDirect Journal of Biomedical Informatics journal homepage: www.elsevier.com/locate/yjbin Co-occurrence of medical conditions: Exposing patterns through probabilistic topic modeling of snomed codes a, b a,c,d Moumita Bhattacharya , Claudine Jurkovitz , Hagit Shatkay Computational Biomedicine Lab, Computer and Information Sciences, University of Delaware, Newark, DE, USA Value Institute, Christiana Care Health System, Newark, DE, USA Center for Bioinformatics and Computational Biology, Delaware Biotechnology Inst, University of Delaware, DE, USA School of Computing, Queen's University, Kingston, ON K7L 3N6, Canada ARTIC L E I NF O ABSTRAC T Keywords: Patients associated with multiple co-occurring health conditions often face aggravated complications and less Topic modeling favorable outcomes. Co-occurring conditions are especially prevalent among individuals suffering from kidney SNOMED-CT codes disease, an increasingly widespread condition affecting 13% of the general population in the US. This study aims Electronic health records to identify and characterize patterns of co-occurring medical conditions in patients employing a probabilistic Co-occurring medical conditions framework. Specifically, we apply topic modeling in a non-traditional way to find associations across SNOMED- CT codes assigned and recorded in the EHRs of > 13,000 patients diagnosed with kidney disease. Unlike most prior work http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Biomedical Informatics Elsevier

Co-occurrence of medical conditions: Exposing patterns through probabilistic topic modeling of snomed codes

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Publisher
Elsevier
Copyright
Copyright © 2018 The Authors
ISSN
1532-0464
eISSN
1532-0480
D.O.I.
10.1016/j.jbi.2018.04.008
Publisher site
See Article on Publisher Site

Abstract

Journal of Biomedical Informatics 82 (2018) 31–40 Contents lists available at ScienceDirect Journal of Biomedical Informatics journal homepage: www.elsevier.com/locate/yjbin Co-occurrence of medical conditions: Exposing patterns through probabilistic topic modeling of snomed codes a, b a,c,d Moumita Bhattacharya , Claudine Jurkovitz , Hagit Shatkay Computational Biomedicine Lab, Computer and Information Sciences, University of Delaware, Newark, DE, USA Value Institute, Christiana Care Health System, Newark, DE, USA Center for Bioinformatics and Computational Biology, Delaware Biotechnology Inst, University of Delaware, DE, USA School of Computing, Queen's University, Kingston, ON K7L 3N6, Canada ARTIC L E I NF O ABSTRAC T Keywords: Patients associated with multiple co-occurring health conditions often face aggravated complications and less Topic modeling favorable outcomes. Co-occurring conditions are especially prevalent among individuals suffering from kidney SNOMED-CT codes disease, an increasingly widespread condition affecting 13% of the general population in the US. This study aims Electronic health records to identify and characterize patterns of co-occurring medical conditions in patients employing a probabilistic Co-occurring medical conditions framework. Specifically, we apply topic modeling in a non-traditional way to find associations across SNOMED- CT codes assigned and recorded in the EHRs of > 13,000 patients diagnosed with kidney disease. Unlike most prior work

Journal

Journal of Biomedical InformaticsElsevier

Published: Jun 1, 2018

References

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