ArticleJournal of biomedical semantics2014
Mining characteristics of epidemiological studies from Medline: a case study in obesity.
Article in Journal of biomedical semantics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Data extraction methods for systematic review (semi)automation: Update of a living systematic review.F1000Research · 2021Pooled it
- An automation framework for clinical codelist development validated with UK data from patients with multiple long-term conditions.BMC medical research methodology · 2025Article
- Population Characteristics in Justice Health Research Based on PubMed Abstracts From 1963 to 2023: Text Mining Study.JMIR formative research · 2024Article
- An analysis of published study designs in PubMed prisoner health abstracts from 1963 to 2023: a text mining study.BMC medical research methodology · 2024Review
- Supporting the working life exposome: Annotating occupational exposure for enhanced literature search.PloS one · 2024Article
- Automatic Extraction of Research Themes in Epidemiological Criminology From PubMed Abstracts From 1946 to 2020: Text Mining Study.JMIR formative research · 2023Article
- A Scoping Review of Adopted Information Extraction Methods for RCTs.Medical journal of the Islamic Republic of Iran · 2023Article
- A Narrative Literature Review of Natural Language Processing Applied to the Occupational Exposome.International journal of environmental research and public health · 2022Review
- Improving reference prioritisation with PICO recognition.BMC medical informatics and decision making · 2019Article
- Interpretable Topic Features for Post-ICU Mortality Prediction.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
- Double sampling with multiple imputation to answer large sample meta-research questions: introduction and illustration by evaluating adherence to two simple CONSORT guidelines.Frontiers in nutrition · 2015Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundThe 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.
resultsWe 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.
conclusionsThe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.