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

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

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; Bredkjaer, Søren; Juul, Anders; Werge, Thomas; Jensen, Lars J.; Brunak, Søren.

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

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

Harvard

Roque, FS, Jensen, PB, Schmock, H, Dalgaard, M, Andreatta, M, Hansen, T, Søeby, K, Bredkjaer, 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., 10.1371/journal.pcbi.1002141

APA

CBE

Roque FS, Jensen PB, Schmock H, Dalgaard M, Andreatta M, Hansen T, Søeby K, Bredkjaer 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). Available from: 10.1371/journal.pcbi.1002141

MLA

Vancouver

Author

Roque, Francisco S.; Jensen, Peter B.; Schmock, Henriette; Dalgaard, Marlene; Andreatta, Massimo; Hansen, Thomas; Søeby, Karen; Bredkjaer, 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, Vol. 7, No. 8, 2011.

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

Bibtex

@article{a3a518a4654943cab132b2e73c14b64c,
title = "Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts",
publisher = "Public Library of Science",
author = "Roque, {Francisco S.} and Jensen, {Peter B.} and Henriette Schmock and Marlene Dalgaard and Massimo Andreatta and Thomas Hansen and Karen Søeby and Søren Bredkjaer and Anders Juul and Thomas Werge and Jensen, {Lars J.} and Søren Brunak",
year = "2011",
doi = "10.1371/journal.pcbi.1002141",
volume = "7",
number = "8",
journal = "P L o S Computational Biology",
issn = "1553-734X",

}

RIS

TY - JOUR

T1 - Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts

A1 - Roque,Francisco S.

A1 - Jensen,Peter B.

A1 - Schmock,Henriette

A1 - Dalgaard,Marlene

A1 - Andreatta,Massimo

A1 - Hansen,Thomas

A1 - Søeby,Karen

A1 - Bredkjaer,Søren

A1 - Juul,Anders

A1 - Werge,Thomas

A1 - Jensen,Lars J.

A1 - Brunak,Søren

AU - Roque,Francisco S.

AU - Jensen,Peter B.

AU - Schmock,Henriette

AU - Dalgaard,Marlene

AU - Andreatta,Massimo

AU - Hansen,Thomas

AU - Søeby,Karen

AU - Bredkjaer,Søren

AU - Juul,Anders

AU - Werge,Thomas

AU - Jensen,Lars J.

AU - Brunak,Søren

PB - Public Library of Science

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.

U2 - 10.1371/journal.pcbi.1002141

DO - 10.1371/journal.pcbi.1002141

JO - P L o S Computational Biology

JF - P L o S Computational Biology

SN - 1553-734X

IS - 8

VL - 7

ER -