Title |
Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts
|
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Published in |
PLoS Computational Biology, August 2011
|
DOI | 10.1371/journal.pcbi.1002141 |
Pubmed ID | |
Authors |
Francisco S. Roque, Peter B. Jensen, Henriette Schmock, Marlene Dalgaard, Massimo Andreatta, Thomas Hansen, Karen Søeby, Søren Bredkjær, Anders Juul, Thomas Werge, Lars J. Jensen, Søren Brunak |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 7 | 28% |
Spain | 3 | 12% |
Canada | 2 | 8% |
Italy | 1 | 4% |
Korea, Republic of | 1 | 4% |
United Kingdom | 1 | 4% |
Denmark | 1 | 4% |
Netherlands | 1 | 4% |
Germany | 1 | 4% |
Other | 0 | 0% |
Unknown | 7 | 28% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 16 | 64% |
Scientists | 7 | 28% |
Practitioners (doctors, other healthcare professionals) | 1 | 4% |
Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 17 | 4% |
United Kingdom | 6 | 1% |
Spain | 6 | 1% |
Germany | 5 | 1% |
Netherlands | 2 | <1% |
Denmark | 2 | <1% |
Canada | 2 | <1% |
Brazil | 1 | <1% |
Sweden | 1 | <1% |
Other | 7 | 2% |
Unknown | 404 | 89% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 96 | 21% |
Student > Ph. D. Student | 87 | 19% |
Student > Master | 64 | 14% |
Other | 25 | 6% |
Student > Bachelor | 25 | 6% |
Other | 99 | 22% |
Unknown | 57 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 118 | 26% |
Medicine and Dentistry | 87 | 19% |
Agricultural and Biological Sciences | 65 | 14% |
Biochemistry, Genetics and Molecular Biology | 23 | 5% |
Engineering | 18 | 4% |
Other | 66 | 15% |
Unknown | 76 | 17% |