Vibha Anand, Erhan Bilal, et al.
AMIA Annual Symposium 2020
When healthcare providers review the results of a clinical trial study to understand its applicability to their practice, they typically analyze how well the characteristics of the study cohort correspond to those of the patients they see. We have previously created a study cohort ontology to standardize this information and make it accessible for knowledge-based decision support. The extraction of this information from research publications is challenging, however, given the wide variance in reporting cohort characteristics in a tabular representation. To address this issue, we have developed an ontology-enabled knowledge extraction pipeline for automatically constructing knowledge graphs from the cohort characteristics found in PDF-formatted research papers. We evaluated our approach using a training and test set of 41 research publications and found an overall accuracy of 83.3% in correctly assembling the knowledge graphs. Our research provides a promising approach for extracting knowledge more broadly from tabular information in research publications.
Vibha Anand, Erhan Bilal, et al.
AMIA Annual Symposium 2020
Francesca Bonin, Martin Gleize, et al.
AMIA Annual Symposium 2020
Diwakar Mahajan, Jennifer J. Liang
AMIA Annual Symposium 2020
Nandana Mihindukulasooriya, Sarthak Dash, et al.
ISWC 2023