Paper Info
Title
Mining characteristics of epidemiological studies from Medline: a case study in obesity.
Abstract
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.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.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.
Year
DOI
Venue
2014
10.1186/2041-1480-5-22
J. Biomedical Semantics
Keywords
Field
DocType
epidemiology,key characteristics,rule-based methodology,text mining,biomedical research,bioinformatics
Data science,Data mining,Population,Covariate,Systematic review,Computer science,Epidemiology,Obesity,Information extraction,MEDLINE,Recall
Journal
Volume
Issue
ISSN
5
1
2041-1480
Citations 
PageRank 
References 
3
0.42
16
Authors
3
Name
Order
Citations
PageRank
George Karystianis1222.15
Iain Buchan211713.63
Goran Nenadic322813.18