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
are mapped to systems biology frameworks.
| Original language | English |
|---|---|
| Journal | FEBS Journal |
| Volume | 278 |
| Issue number | Suppl. s1 |
| Pages (from-to) | 27 |
| ISSN | 1742-464X |
| DOIs | |
| Publication status | Published - 2011 |
| Event | 36th FEBS Congress, Biochemistry for Tomorrow's Medicine - Lingotto Conference Center, Torino, Italy Duration: 25 Jun 2011 → 30 Jun 2011 Conference number: 36 |
Conference
| Conference | 36th FEBS Congress, Biochemistry for Tomorrow's Medicine |
|---|---|
| Number | 36 |
| Location | Lingotto Conference Center |
| Country/Territory | Italy |
| City | Torino |
| Period | 25/06/2011 → 30/06/2011 |
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