Recognizing Obesity and Comorbidities in Sparse Data

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Uzuner O.

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, vol.16, no.4, pp.561-570, 2009 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Editorial Material
  • Volume: 16 Issue: 4
  • Publication Date: 2009
  • Doi Number: 10.1197/jamia.m3115
  • Journal Name: JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Page Numbers: pp.561-570
  • Middle East Technical University Affiliated: No

Abstract

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.