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Modeling temporal relationships in large scale clinical associations.

David A Hanauer1, Naren Ramakrishnan

  • 1Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI 48109-5940, USA. hanauer@umich.edu

Journal of the American Medical Informatics Association : JAMIA
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Summary

Temporal analysis of electronic health records reveals new insights into disease relationships. Modeling these temporal patterns in International Classification of Diseases, Ninth Revision (ICD-9) codes aids hypothesis generation and clinical discovery.

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Area of Science:

  • Biomedical Informatics
  • Health Data Science
  • Clinical Epidemiology

Background:

  • Electronic health records (EHRs) contain vast amounts of longitudinal patient data.
  • International Classification of Diseases, Ninth Revision (ICD-9) codes represent diagnoses but lack explicit temporal relationship modeling.
  • Understanding temporal disease associations can enhance clinical hypothesis generation and discovery.

Purpose of the Study:

  • To develop and apply a novel approach for modeling temporal relationships within large-scale EHR data.
  • To analyze associations between diagnoses using time-stamped ICD-9 codes.
  • To assess the utility of temporal modeling for uncovering clinically relevant disease patterns.

Main Methods:

  • A large dataset of 41.2 million time-stamped ICD-9 codes from 1.6 million patients was utilized.
  • Two analytical methods were employed: pairwise association analysis (χ(2) test) and temporal analysis (binomial test).
  • Network diagrams were used for data visualization and clinical significance review.

Main Results:

  • Nearly 400,000 highly associated pairs of ICD-9 codes were identified.
  • Significant temporal associations were observed, ranging from 1 day to over 10 years.
  • While many findings lacked novelty, some, like the association between Helicobacter pylori infection and diabetes, showed temporal precedence (diabetes preceding H. pylori).

Conclusions:

  • Temporal modeling of EHR data provides an additional layer of meaning for interpreting clinical associations.
  • Despite limitations of ICD-9 codes and data completeness, this approach facilitates novel discovery and hypothesis generation.
  • The methodology highlights the potential of temporal analysis in uncovering complex disease relationships and etiological insights.