Characterizing environmental and phenotypic associations using information theory and electronic health records

Abstract

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%.

Conclusion: This preliminary study demonstrates that the proposed method is effective for helping to characterize phenotypic and environmental associations obtained from clinical reports.

Figure 1

Schematic diagram of the effect of DPI. (a) and (c): Among two paths between ML and SOB (ML-SOB, ML-HD-SOB), ML-SOB was eliminated by applying DPI (shown as 1 in table 1). (b) and (d): Similarly, among two paths between HF and SOB (HF-SOB, HF-HD-SOB), HF-HD-SOB was eliminated by applying DPI (shown as 6 in table 1). (e) Combining evidence from (c) and (d), the path between ML-SOB would more likely to be ML-HD-HF-SOB. ML: Metolazone; HD: Hypertensive Disease; HF: Hear Failure; SOB: Shortness of Breath.

Figure 2

Schematic diagram of methodological framework. A: Acquiring Associations; B: Characterizing Associations; EHR: Electric Health Records; MI: Mutual Information; DPI: Data Processing Inequality; Dx: disease; Rx: drug; Sx: symptom.