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

Publikation: Bidrag til tidsskriftTidsskriftartikelpeer review

Harvard

Roque, FS, Jensen, PB, Schmock, H, Dalgaard, M, Andreatta, M, Hansen, T, Søeby, K, Bredkjær, S, Juul, A, Werge, T, Jensen, LJ & Brunak, S 2011, 'Using electronic patient records to discover disease correlations and stratify patient cohorts', P L o S Computational Biology, bind 7, nr. 8, s. e1002141. https://doi.org/10.1371/journal.pcbi.1002141

APA

Roque, F. S., Jensen, P. B., Schmock, H., Dalgaard, M., Andreatta, M., Hansen, T., Søeby, K., Bredkjær, S., Juul, A., Werge, T., Jensen, L. J., & Brunak, S. (2011). Using electronic patient records to discover disease correlations and stratify patient cohorts. P L o S Computational Biology, 7(8), e1002141. https://doi.org/10.1371/journal.pcbi.1002141

CBE

Roque FS, Jensen PB, Schmock H, Dalgaard M, Andreatta M, Hansen T, Søeby K, Bredkjær S, Juul A, Werge T, Jensen LJ, Brunak S. 2011. Using electronic patient records to discover disease correlations and stratify patient cohorts. P L o S Computational Biology. 7(8):e1002141. https://doi.org/10.1371/journal.pcbi.1002141

MLA

Vancouver

Author

Roque, Francisco S. ; Jensen, Peter B ; Schmock, Henriette ; Dalgaard, Marlene ; Andreatta, Massimo ; Hansen, Thomas ; Søeby, Karen ; Bredkjær, Søren ; Juul, Anders ; Werge, Thomas ; Jensen, Lars J ; Brunak, Søren. / Using electronic patient records to discover disease correlations and stratify patient cohorts. I: P L o S Computational Biology. 2011 ; Bind 7, Nr. 8. s. e1002141.

Bibtex

@article{89be12ab4f7e4e53abaed499be07a4be,
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.",
author = "Roque, {Francisco S.} and Jensen, {Peter B} and Henriette Schmock and Marlene Dalgaard and Massimo Andreatta and Thomas Hansen and Karen S{\o}eby and S{\o}ren Bredkj{\ae}r and Anders Juul and Thomas Werge and Jensen, {Lars J} and S{\o}ren Brunak",
year = "2011",
month = aug,
day = "1",
doi = "10.1371/journal.pcbi.1002141",
language = "English",
volume = "7",
pages = "e1002141",
journal = "PLOS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "8",

}

RIS

TY - JOUR

T1 - Using electronic patient records to discover disease correlations and stratify patient cohorts

AU - Roque, Francisco S.

AU - Jensen, Peter B

AU - Schmock, Henriette

AU - Dalgaard, Marlene

AU - Andreatta, Massimo

AU - Hansen, Thomas

AU - Søeby, Karen

AU - Bredkjær, Søren

AU - Juul, Anders

AU - Werge, Thomas

AU - Jensen, Lars J

AU - Brunak, Søren

PY - 2011/8/1

Y1 - 2011/8/1

N2 - 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.

AB - 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.

U2 - 10.1371/journal.pcbi.1002141

DO - 10.1371/journal.pcbi.1002141

M3 - Journal article

C2 - 21901084

VL - 7

SP - e1002141

JO - PLOS Computational Biology

JF - PLOS Computational Biology

SN - 1553-734X

IS - 8

ER -

ID: 32767485