Work

Understanding Clinical Trial Reports: Extracting Medical Entities and Their Relations

Year: 2020

Type: article

Abstract: task of both (a) extracting treatments and outcomes from full-text articles describing clinical trials (entity identification) and, (b) inferring the reported results for the former with respect to the latter (relation extraction). We introduce new data for this task, and evaluate models that have recently achieved state-of-the-art results on similar tasks in Natural Language Processing. We then propose a new method motivated by how trial results are typically presented that outperforms these purely data-driven baselines. Finally, we run a fielded evaluation of the model with a non-profit seeking to identify existing drugs that might be re-purposed for cancer, showing the potential utility of end-to-end evidence extraction systems.

Source: PubMed Central

DOI: 10.48550/arxiv.2010.03550

Authors: Nye, Benjamin E., DeYoung, Jay, Lehman, Eric, Nenkova, Ani, Marshall, Iain J., Wallace, Byron C.

Institutions: Northeastern University, University of Pennsylvania, King's College London

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Science Foundation, National Institutes of Health