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PHEE: A Dataset for Pharmacovigilance Event Extraction from Text

Authors: Sun, Zhaoyue; Li, Jiazheng; Pergola, Gabriele; Wallace, Byron C.; John, Bino; Greene, Nigel; He, Yulan;

PHEE: A Dataset for Pharmacovigilance Event Extraction from Text

Abstract

The PHEE dataset contains over 5,000 finely annotated pharmacovigilance events from public medical case reports. Two types of events, the adverse events and the potential therapeutic events, are annotated. For each event, we annotate the event trigger and hierarchical arguments. The main arguments (coarse-grained spans) include subject, treatment and effect. Further fine-grained sub-arguments - age, gender, race, number of patients (labelled as population) and preexisting conditions (labelled as subject.disorder) for the subject argument and drug (and their combinations), dosage, frequency, route, time-elapsed, duration, target disorder (labelled as treatment.disorder) for the treatment argument - are then annotated upon main arguments. We provide two formats of data: visualisation-friendly brat-format data and structured json data for the convenience of use.

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Keywords

event extraction, pharmacovigilance, medical NLP, NLP

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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