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Modeling temporal relationships in large scale clinical associations.

TitleModeling temporal relationships in large scale clinical associations.
Publication TypeJournal Article
Year of Publication2013
AuthorsHanauer, DA, Ramakrishnan, N
JournalJ Am Med Inform Assoc
Date Published2013 Mar-Apr
KeywordsComputer Graphics, Data Mining, Electronic Health Records, Epidemiologic Methods, Feasibility Studies, Humans, International Classification of Diseases, Michigan, Space-Time Clustering

OBJECTIVE: We describe an approach for modeling temporal relationships in a large scale association analysis of electronic health record data. The addition of temporal information can inform hypothesis generation and help to explain the relationships. We applied this approach on a dataset containing 41.2 million time-stamped International Classification of Diseases, Ninth Revision (ICD-9) codes from 1.6 million patients.METHODS: We performed two independent analyses including a pairwise association analysis using a χ(2) test and a temporal analysis using a binomial test. Data were visualized using network diagrams and reviewed for clinical significance.RESULTS: We found nearly 400 000 highly associated pairs of ICD-9 codes with varying numbers of strong temporal associations ranging from ≥1 day to ≥10 years apart. Most of the findings were not considered clinically novel, although some, such as an association between Helicobacter pylori infection and diabetes, have recently been reported in the literature. The temporal analysis in our large cohort, however, revealed that diabetes usually preceded the diagnoses of H pylori, raising questions about possible cause and effect.DISCUSSION: Such analyses have significant limitations, some of which are due to known problems with ICD-9 codes and others to potentially incomplete data even at a health system level. Nevertheless, large scale association analyses with temporal modeling can help provide a mechanism for novel discovery in support of hypothesis generation.CONCLUSIONS: Temporal relationships can provide an additional layer of meaning in identifying and interpreting clinical associations.

Alternate JournalJ Am Med Inform Assoc
PubMed ID23019240
PubMed Central IDPMC3638191
David Hanauer
University of Michigan Rogel Cancer Center at North Campus Research Complex
1600 Huron Parkway, Bldg 100, Rm 1004 
Mailing Address: 2800 Plymouth Rd, NCRC 100-1004
Ann Arbor, MI 48109-2800 
Ph. (734) 764-8848 Fax. (734) 615-0507

Research reported in this publication was supported by the National Cancer Institutes of
Health under Award Number P30CA046592. The content is solely the responsibility
of the authors and does not necessarily represent the official views of the
National Institutes of Health.

Research reported in this publication was supported by the National Cancer Institutes of
Health under Award Number P30CA046592 by the use of the following Cancer Center
Shared Resource(s): Biostatistics, Analytics & Bioinformatics; Flow Cytometry;
Transgenic Animal Models; Tissue and Molecular Pathology; Structure & Drug
Screening; Cell & Tissue Imaging; Experimental Irradiation; Preclinical
Imaging & Computational Analysis; Health Communications; Immune Monitoring;

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