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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.

SUBMITTER: Roque FS 

PROVIDER: S-EPMC3161904 | biostudies-literature | 2011 Aug

REPOSITORIES: biostudies-literature

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Using electronic patient records to discover disease correlations and stratify patient cohorts.

Roque Francisco S FS   Jensen Peter B PB   Schmock Henriette H   Dalgaard Marlene M   Andreatta Massimo M   Hansen Thomas T   Søeby Karen K   Bredkjær Søren S   Juul Anders A   Werge Thomas T   Jensen Lars J LJ   Brunak Søren S  

PLoS computational biology 20110825 8


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 strati  ...[more]

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