Connectionists: Shared Task: Classification of changes in medication treatments in Social Media - #SMM4H'22

[Davy Weissenbacher]

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Call for shared task participation, evaluation period starts July 11, 2022
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*Apologies if you received multiple copies of this CFP*

Test set release Date: July 15, 2022
Important links:
Shared task: https://healthlanguageprocessing.org/smm4h-2022/
Codalab: https://codalab.lisn.upsaclay.fr/competitions/2138

Task 3 – Classification of changes in medication treatments in tweets and
WebMD reviews (in English)

We invite participants to design binary classifiers for detecting posts
where users self-declare changing their medication treatments, regardless
of being advised by a health care professional to do so. Such changes are,
for example, not filling a prescription, stopping a treatment, changing a
dosage, forgetting to take the drugs, etc. This task is the first step
toward detecting patients non-adherent to their treatments and their
reasons on Social Media. The data consists of two corpora: a set of tweets
and a set of drug reviews from WebMD.com. Negative and positive reviews are
naturally balanced whereas positive and negative tweets are naturally
imbalanced. Each set is split into a training, a validation, and a test
subset. The participants will be given the training and validation subsets
for both corpora and evaluated on both test sets independently.
Participants are expected to submit their predictions for both test sets.
Evaluation script, annotation guidelines, and baseline code will be
provided to registered participants.

    Training data: 5,898 Tweets / 10,378 Reviews
    Validation data: 1,572 Tweets / 1,297 Reviews
    Test data: 2,360 Tweets / 1,297 Reviews
    Evaluation metric: F1-score for the change class

Contact
All questions should be emailed to Davy Weissenbacher (
dweissen at pennmedicine.upenn.edu)
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