Using electronic patient records to discover disease correlations and stratify patient cohorts
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...
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| Published in | PLoS computational biology Vol. 7; no. 8; p. e1002141 |
|---|---|
| Main Authors | , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
01.08.2011
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
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| 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. |
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| AbstractList | 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. 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. 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. Text mining and information extraction can be seen as the challenge of converting information hidden in text into manageable data. We have used text mining to automatically extract clinically relevant terms from 5543 psychiatric patient records and map these to disease codes in the International Classification of Disease ontology (ICD10). Mined codes were supplemented by existing coded data. For each patient we constructed a phenotypic profile of associated ICD10 codes. This allowed us to cluster patients together based on the similarity of their profiles. The result is a patient stratification based on more complete profiles than the primary diagnosis, which is typically used. Similarly we investigated comorbidities by looking for pairs of disease codes cooccuring in patients more often than expected. Our high ranking pairs were manually curated by a medical doctor who flagged 93 candidates as interesting. For a number of these we were able to find genes/proteins known to be associated with the diseases using the OMIM database. The disease-associated proteins allowed us to construct protein networks suspected to be involved in each of the phenotypes. Shared proteins between two associated diseases might provide insight to the disease comorbidity. |
| Author | Schmock, Henriette Andreatta, Massimo Dalgaard, Marlene Bredkjær, Søren Jensen, Lars J Jensen, Peter B Søeby, Karen Roque, Francisco S Hansen, Thomas Juul, Anders Werge, Thomas Brunak, Søren |
| AuthorAffiliation | Vanderbilt University, United States of America 4 Department of Growth and Reproduction GR, Rigshospitalet, Copenhagen, Denmark 2 NNF Center for Protein Research, University of Copenhagen, Copenhagen, Denmark 1 Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark 5 Department of Clinical Biochemistry, Hvidovre Hospital, Copenhagen University Hospital, Hvidovre, Denmark 3 Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Copenhagen University Hospital, Roskilde, Denmark 6 Psychiatry Region Sealand, Ringsted, Denmark |
| AuthorAffiliation_xml | – name: 6 Psychiatry Region Sealand, Ringsted, Denmark – name: 4 Department of Growth and Reproduction GR, Rigshospitalet, Copenhagen, Denmark – name: 2 NNF Center for Protein Research, University of Copenhagen, Copenhagen, Denmark – name: 3 Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Copenhagen University Hospital, Roskilde, Denmark – name: 5 Department of Clinical Biochemistry, Hvidovre Hospital, Copenhagen University Hospital, Hvidovre, Denmark – name: 1 Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark – name: Vanderbilt University, United States of America |
| Author_xml | – sequence: 1 givenname: Francisco S surname: Roque fullname: Roque, Francisco S organization: Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark – sequence: 2 givenname: Peter B surname: Jensen fullname: Jensen, Peter B – sequence: 3 givenname: Henriette surname: Schmock fullname: Schmock, Henriette – sequence: 4 givenname: Marlene surname: Dalgaard fullname: Dalgaard, Marlene – sequence: 5 givenname: Massimo surname: Andreatta fullname: Andreatta, Massimo – sequence: 6 givenname: Thomas surname: Hansen fullname: Hansen, Thomas – sequence: 7 givenname: Karen surname: Søeby fullname: Søeby, Karen – sequence: 8 givenname: Søren surname: Bredkjær fullname: Bredkjær, Søren – sequence: 9 givenname: Anders surname: Juul fullname: Juul, Anders – sequence: 10 givenname: Thomas surname: Werge fullname: Werge, Thomas – sequence: 11 givenname: Lars J surname: Jensen fullname: Jensen, Lars J – sequence: 12 givenname: Søren surname: Brunak fullname: Brunak, Søren |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/21901084$$D View this record in MEDLINE/PubMed |
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| Copyright | Roque et al. 2011 2011 Roque et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Roque FS, Jensen PB, Schmock H, Dalgaard M, Andreatta M, et al. (2011) Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts. PLoS Comput Biol 7(8): e1002141. doi:10.1371/journal.pcbi.1002141 |
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| Keywords | International Classification of Diseases Reproducibility of Results Comorbidity Humans Computational Biology Data Mining Data Collection Electronic Health Records Cluster Analysis Cohort Studies |
| Language | English |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Conceived and designed the experiments: F. Roque, P. Jensen, S. Bredkjær, L. Jensen, S. Brunak. Performed the experiments: F. Roque, P. Jensen, H. Schmock, M. Andreatta. Analyzed the data: F. Roque, P. Jensen, H. Schmock, M. Dalgaard, M. Andreatta, T. Hansen, K. Søeby, A. Juul, T. Werge, S. Brunak. Wrote the paper: F. Roque, P. Jensen. |
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| Title | Using electronic patient records to discover disease correlations and stratify patient cohorts |
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