Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts

PLoS Comput Biol. 2011 Aug;7(8):e1002141. doi: 10.1371/journal.pcbi.1002141. Epub 2011 Aug 25.

Abstract

Electronic patient records remain a rather unexplored, but potentially rich data source for discovering correlations between diseases. We describe a general approach for gathering phenotypic descriptions of patients from medical records in a systematic and non-cohort dependent manner. By extracting phenotype information from the free-text in such records we demonstrate that we can extend the information contained in the structured record data, and use it for producing fine-grained patient stratification and disease co-occurrence statistics. The approach uses a dictionary based on the International Classification of Disease ontology and is therefore in principle language independent. As a use case we show how records from a Danish psychiatric hospital lead to the identification of disease correlations, which subsequently can be mapped to systems biology frameworks.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cluster Analysis
  • Cohort Studies
  • Comorbidity
  • Computational Biology / methods
  • Data Collection / methods*
  • Data Mining / methods*
  • Electronic Health Records*
  • Humans
  • International Classification of Diseases
  • Reproducibility of Results