Inferring Which Medical Treatments Work from Reports of Clinical Trials
Name
N19-1371.pdf
Description
Published version
Size
961.59 KB
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Unknown
Checksum (MD5)
6439080373bc53d92d669fd050f3a282
Author(s)
Lehman, Eric
DeYoung, Jay
Barzilay, Regina
Wallace, Byron C
Date Issued
2019
Journal
NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference
Publisher
Association for Computational Linguistics
Citation
Lehman, Eric, DeYoung, Jay, Barzilay, Regina and Wallace, Byron C. "Inferring Which Medical Treatments Work from Reports of Clinical Trials." NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, 1.
Version
Final published version
Abstract
© 2019 Association for Computational Linguistics How do we know if a particular medical treatment actually works? Ideally one would consult all available evidence from relevant clinical trials. Unfortunately, such results are primarily disseminated in natural language scientific articles, imposing substantial burden on those trying to make sense of them. In this paper, we present a new task and corpus for making this unstructured evidence actionable. The task entails inferring reported findings from a full-text article describing a randomized controlled trial (RCT) with respect to a given intervention, comparator, and outcome of interest, e.g., inferring if an article provides evidence supporting the use of aspirin to reduce risk of stroke, as compared to placebo. We present a new corpus for this task comprising 10,000+ prompts coupled with full-text articles describing RCTs. Results using a suite of models - ranging from heuristic (rule-based) approaches to attentive neural architectures - demonstrate the difficulty of the task, which we believe largely owes to the lengthy, technical input texts. To facilitate further work on this important, challenging problem we make the corpus, documentation, a website and leaderboard, and code for baselines and evaluation available at http://evidence-inference.ebm-nlp.com/.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.18653/V1/N19-1371
