Recognizing Obesity and Comorbidities in Sparse Data

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2009-07-01

Author

Uzuner, Oezlem

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In order to survey, facilitate, and evaluate studies of medical language processing on clinical narratives, i2b2 (Informatics for Integrating Biology to the Bedside) organized its second challenge and workshop. This challenge focused on automatically extracting information on obesity and fifteen of its most common comorbidities from patient discharge summaries. For each patient, obesity and any of the comorbidities could be Present, Absent, or Questionable (i.e., possible) in the patient, or Unmentioned in the discharge summary of the patient. i2b2 provided data for, and invited the development of, automated systems that can classify obesity and its comorbidities into these four classes based on individual discharge summaries. This article refers to obesity and comorbidities as diseases. It refers to the categories Present, Absent, Questionable, and Unmentioned as classes. The task of classifying obesity and its comorbidities is called the Obesity Challenge.

Subject Keywords

Health Informatics

URI

https://hdl.handle.net/11511/64123

Journal

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION

DOI

https://doi.org/10.1197/jamia.m3115

Collections

Engineering, Article

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Citation Formats

O. Uzuner, “Recognizing Obesity and Comorbidities in Sparse Data,” JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, pp. 561–570, 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/64123.