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

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

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

In: P L o S Computational Biology, Vol. 7, No. 8, 2011, p. e1002141.

Research output: Contribution to journal › Journal article › Research › peer-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, vol. 7, no. 8, pp. 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

Vancouver

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

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. In: P L o S Computational Biology. 2011 ; Vol. 7, No. 8. pp. e1002141.

Bibtex

@article{4b0328de49b14b85a63ae97bb274506e,
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.",
keywords = "Cluster Analysis, Cohort Studies, Comorbidity, Computational Biology, Data Collection, Data Mining, Electronic Health Records, Humans, International Classification of Diseases, Reproducibility of Results",
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",
doi = "10.1371/journal.pcbi.1002141",
language = "English",
volume = "7",
pages = "e1002141",
journal = "P L o S Computational Biology (Online)",
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

Y1 - 2011

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.

KW - Cluster Analysis

KW - Cohort Studies

KW - Comorbidity

KW - Computational Biology

KW - Data Collection

KW - Data Mining

KW - Electronic Health Records

KW - Humans

KW - International Classification of Diseases

KW - Reproducibility of Results

U2 - 10.1371/journal.pcbi.1002141

DO - 10.1371/journal.pcbi.1002141

M3 - Journal article

C2 - 21901084

VL - 7

SP - e1002141

JO - P L o S Computational Biology (Online)

JF - P L o S Computational Biology (Online)

SN - 1553-734X

IS - 8

ER -

ID: 40167432