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+
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+ ---
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+ language:
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+ - en
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+ license: other
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+ license_bigbio_shortname: DUA
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+ pretty_name: n2c2 2008 Obesity
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+ ---
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+
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+
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+ # Dataset Card for n2c2 2008 Obesity
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+
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+ ## Dataset Description
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+
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+ - **Homepage:** https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/
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+ - **Pubmed:** True
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+ - **Public:** False
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+ - **Tasks:** Text Classification
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+
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+
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+ The data for the n2c2 2008 obesity challenge consisted of discharge summaries from
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+ the Partners HealthCare Research Patient Data Repository. These data were chosen
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+ from the discharge summaries of patients who were overweight or diabetic and had
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+ been hospitalized for obesity or diabetes sometime since 12/1/04. De-identification
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+ was performed semi-automatically. All private health information was replaced with
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+ synthetic identifiers.
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+
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+ The data for the challenge were annotated by two obesity experts from the
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+ Massachusetts General Hospital Weight Center. The experts were given a textual task,
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+ which asked them to classify each disease (see list of diseases above) as Present,
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+ Absent, Questionable, or Unmentioned based on explicitly documented information in
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+ the discharge summaries, e.g., the statement “the patient is obese”. The experts were
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+ also given an intuitive task, which asked them to classify each disease as Present,
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+ Absent, or Questionable by applying their intuition and judgment to information in
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+ the discharge summaries.
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+
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+
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+
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+ ## Citation Information
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+
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+ ```
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+ @article{uzuner2009recognizing,
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+ author = {
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+ Uzuner, Ozlem
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+ },
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+ title = {Recognizing Obesity and Comorbidities in Sparse Data},
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+ journal = {Journal of the American Medical Informatics Association},
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+ volume = {16},
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+ number = {4},
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+ pages = {561-570},
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+ year = {2009},
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+ month = {07},
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+ url = {https://doi.org/10.1197/jamia.M3115},
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+ doi = {10.1197/jamia.M3115},
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+ eprint = {https://academic.oup.com/jamia/article-pdf/16/4/561/2302602/16-4-561.pdf}
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+ }
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+
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+ ```