Characterizing environmental and phenotypic associations using information theory and electronic health records.
BACKGROUND
The availability of up-to-date, executable, evidence-based medical knowledge is essential for many clinical applications, such as pharmacovigilance, but executable knowledge is costly to obtain and update. Automated acquisition of environmental and phenotypic associations in biomedical and clinical documents using text mining has showed some success. The usefulness of the association knowledge is limited, however, due to the fact that the specific relationships between clinical entities remain unknown. In particular, some associations are indirect relations due to interdependencies among the data.


RESULTS
In this work, we develop methods using mutual information (MI) and its property, the data processing inequality (DPI), to help characterize associations that were generated based on use of natural language processing to encode clinical information in narrative patient records followed by statistical methods. Evaluation based on a random sample consisting of two drugs and two diseases indicates an overall precision of 81%.


CONCLUSIONS
This preliminary study demonstrates that the proposed method is effective for helping to characterize phenotypic and environmental associations obtained from clinical reports.
Wang
Xiaoyan
X
Dept of Biomedical Informatics, Columbia University, New York 10032, USA. xiaoyan.wang@dbmi.columbia.edu
Hripcsak
George
G
Friedman
Carol
C
D019295:Computational Biology / Q000379:methods*; D003628:Database Management Systems; D016247:Information Storage and Retrieval / Q000379:methods*; D007257:Information Theory; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing