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PHEE : a dataset for pharmacovigilance event extraction from text
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Sun, Zhaoyue, Li, Jiazheng, Pergola, Gabriele, Wallace, Byron, John, Bino, Greene, Nigel, Kim, Joseph and He, Yulan (2022) PHEE : a dataset for pharmacovigilance event extraction from text. In: Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 7-11 Dec 2022. Published in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing pp. 5571-5587.
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WRAP-PHEE-dataset-pharmacovigilance-event-extraction-text-22.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (611Kb) | Preview |
Official URL: https://aclanthology.org/2022.emnlp-main.376
Abstract
The primary goal of drug safety researchers and regulators is to promptly identify adverse drug reactions. Doing so may in turn prevent or reduce the harm to patients and ultimately improve public health. Evaluating and monitoring drug safety (i.e., pharmacovigilance) involves analyzing an ever growing collection of spontaneous reports from health professionals, physicians, and pharmacists, and information voluntarily submitted by patients. In this scenario, facilitating analysis of such reports via automation has the potential to rapidly identify safety signals. Unfortunately, public resources for developing natural language models for this task are scant. We present PHEE, a novel dataset for pharmacovigilance comprising over 5000 annotated events from medical case reports and biomedical literature, making it the largest such public dataset to date. We describe the hierarchical event schema designed to provide coarse and fine-grained information about patients' demographics, treatments and (side) effects. Along with the discussion of the dataset, we present a thorough experimental evaluation of current state-of-the-art approaches for biomedical event extraction, point out their limitations, and highlight open challenges to foster future research in this area.
| Item Type: | Conference Item (Paper) | ||||||||||||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > RM Therapeutics. Pharmacology |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Pharmacovigilance -- Data processing, Drugs -- Side effects -- Reporting, Drug monitoring -- Data processing, Natural language processing (Computer science), Data mining | ||||||||||||||||||
| Journal or Publication Title: | Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing | ||||||||||||||||||
| Publisher: | Association for Computational Linguistics | ||||||||||||||||||
| Place of Publication: | Abu Dhabi, United Arab Emirates | ||||||||||||||||||
| Official Date: | December 2022 | ||||||||||||||||||
| Dates: |
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| Page Range: | pp. 5571-5587 | ||||||||||||||||||
| Status: | Peer Reviewed | ||||||||||||||||||
| Publication Status: | Published | ||||||||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||||||||
| Date of first compliant deposit: | 20 February 2023 | ||||||||||||||||||
| Date of first compliant Open Access: | 20 February 2023 | ||||||||||||||||||
| RIOXX Funder/Project Grant: |
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| Conference Paper Type: | Paper | ||||||||||||||||||
| Title of Event: | Conference on Empirical Methods in Natural Language Processing | ||||||||||||||||||
| Type of Event: | Conference | ||||||||||||||||||
| Location of Event: | Abu Dhabi, United Arab Emirates | ||||||||||||||||||
| Date(s) of Event: | 7-11 Dec 2022 | ||||||||||||||||||
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