Background: The health sciences literature incorporates a relatively large subset of epidemiological studies that focus on population-level findings, including various determinants, outcomes and correlations. Extracting structured information about those characteristics would be useful for more complete understanding of diseases and for meta-analyses and systematic reviews. Results: We present an information extraction approach that enables users to identify key characteristics of epidemiological studies from MEDLINE abstracts. It extracts six types of epidemiological characteristic: design of the study, population that has been studied, exposure, outcome, covariates and effect size. We have developed a generic rule-based approach that has been designed according to semantic patterns observed in text, and tested it in the domain of obesity. Identified exposure, outcome and covariate concepts are clustered into health-related groups of interest. On a manually annotated test corpus of 60 epidemiological abstracts, the system achieved precision, recall and F-score between 79-100%, 80-100% and 82-96% respectively. We report the results of applying the method to a large scale epidemiological corpus related to obesity. Conclusions: The experiments suggest that the proposed approach could identify key epidemiological characteristics associated with a complex clinical problem from related abstracts. When integrated over the literature, the extracted data can be used to provide a more complete picture of epidemiological efforts, and thus support understanding via meta-analysis and systematic reviews.
Karystianis, G., Buchan, I., & Nenadic, G. (2014). Mining characteristics of epidemiological studies from medline: A case study in obesity. Journal of Biomedical Semantics, 5(1). https://doi.org/10.1186/2041-1480-5-22