Data and systems for medication-related text classification and concept normalization from Twitter: Insights from the Social Media Mining for Health (SMM4H)-2017 shared task

Published: 1 Oct 2018 | Version 2 | DOI: 10.17632/rxwfb3tysd.2
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Description of this data

This data accompanies the following publication:

Title: Data and systems for medication-related text classification and concept normalization from Twitter: Insights from the Social Media Mining for Health (SMM4H) 2017 shared task

Journal: Journal of the American Medical Informatics Association (JAMIA)

The evaluation data (in addition to the training data) was used for the SMM4H-2017 shared tasks, co-located with AMIA-2017 (Washington DC).

Please use the latest version of these files to avoid inconsistencies: (currently v2)

Experiment data files

Steps to reproduce

Please refer to the README files within each subtask folder

Latest version

  • Version 2

    2018-10-01

    Published: 2018-10-01

    DOI: 10.17632/rxwfb3tysd.2

    Cite this dataset

    Sarker, Abeed (2018), “Data and systems for medication-related text classification and concept normalization from Twitter: Insights from the Social Media Mining for Health (SMM4H)-2017 shared task”, Mendeley Data, v2 http://dx.doi.org/10.17632/rxwfb3tysd.2

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Institutions

University of Pennsylvania

Categories

Epidemiology, Health Informatics, Data Science, Drug Adverse Reactions, Natural Language Processing, Machine Learning, Pharmacovigilance, Medication, Text Mining, Twitter

Licence

CC BY 4.0 Learn more

The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence.

What does this mean?

You can share, copy and modify this dataset so long as you give appropriate credit, provide a link to the CC BY license, and indicate if changes were made, but you may not do so in a way that suggests the rights holder has endorsed you or your use of the dataset. Note that further permission may be required for any content within the dataset that is identified as belonging to a third party.

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