Modeling temporal relationships in large scale clinical associations.

ABSTRACT:

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 associ

SUBMITTER: Hanauer DA 

PROVIDER: S-EPMC3638191 | biostudies-literature | 2013 Mar-Apr

REPOSITORIES: biostudies-literature