Recognizing Obesity and Co-morbidities in Sparse Data

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 co-morbidities from patient discharge summaries. For each patient, obesity and any of the co-morbidities could be Present, Absent, or Questionable (i.e., possible), 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 co-morbidities into these four classes based on individual discharge summaries. This article refers to obesity and co-morbidities as diseases. It refers to the categories Present, Absent, Questionable, and Unmentioned as classes. The task of classifying obesity and co-morbidities is called the Obesity Challenge. The data released by i2b2 was annotated for textual judgments reflecting the explicitly reported information on diseases, and intuitive judgments reflecting medical professionals’ reading of the information presented in discharge summaries. There were very few examples o