title: Using electronic patient records to discover disease correlations and stratify patient cohorts.
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.
authors: Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark.
authors: Francisco S
authors: FS
authors: Roque
authors: Peter B
authors: PB
authors: Jensen
authors: Henriette
authors: H
authors: Schmock
authors: Marlene
authors: M
authors: Dalgaard
authors: Massimo
authors: M
authors: Andreatta
authors: Thomas
authors: T
authors: Hansen
authors: Karen
authors: K
authors: Søeby
authors: Søren
authors: S
authors: Bredkjær
authors: Anders
authors: A
authors: Juul
authors: Thomas
authors: T
authors: Werge
authors: Lars J
authors: LJ
authors: Jensen
authors: Søren
authors: S
authors: Brunak
mesh_terms: D016000:Cluster Analysis; D015331:Cohort Studies; D015897:Comorbidity; D019295:Computational Biology / Q000379:methods; D003625:Data Collection / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records; D006801:Humans; D038801:International Classification of Diseases; D015203:Reproducibility of Results