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

Publication: Research - peer-reviewJournal article – Annual report year: 2011

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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.
Original languageEnglish
JournalP L o S Computational Biology
Publication date2011
Volume7
Journal number8
ISSN1553-734X
DOIs
StatePublished
CitationsWeb of Science® Times Cited: 21
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