Recognizing Obesity and Comorbidities in Sparse Data

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. 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 of some disease classes in the data. The Obesity Challenge paid particular attention to the performance of systems on these less well-represented classes. A total of 30 teams participated in the Obesity Challenge. Each team was allowed to submit two sets of up to three system runs for evaluation, resulting in a total of 136 submissions. The submissions represented a combination of rule-based and machine learning approaches. Evaluation of system runs shows that the best predictions of textual judgments come from systems that filter the potentially noisy portions of the narratives, project dictionaries of disease names onto the remaining text, apply negation extraction, and process the text through rules. Information on disease-related concepts, such as symptoms and medications, and general medical knowledge help systems infer intuitive judgments on the diseases.

Body fatness and endogenous sex hormones in the menopausal transition

“…Morbidity and mortality risk is associated with obese nutritional status due to the comorbidities of obesity (e.g., insulin resistance, glucose intolerance, diabetes mellitus, cardiovascular diseases, sleep apnea, arthritis, certain types of cancer) [59][60][61][62][63][64][65][66][67][68][69][70]. In addition, abdominal obesity was found to increase the prediction of health risk in obese nutritional status [71][72][73][74][75][76][77][78][79].…”

Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi)

“…The Informatics for Integrating Biology at the Bedside (i2b2) NLP series of challenges have released a corpus of de-identified clinical records annotated to support a number of IE challenges with multiple levels of annotation, i.e., entities and relations (Uzuner et al, 2008;Uzuner, 2009). The 2010 challenge included the release of a corpus of discharge summaries and patient reports in which named entities and relations concerning medical problems, tests and treatments were annotated (Uzuner et al, 2011).…”

Building a semantically annotated corpus for congestive heart and renal failure from clinical records and the literature

“…The discharge summaries in our PhenoCHF corpus constitute a subset of the data released for the second i2b2 shared task, known as "recognising obesity" (Uzuner, 2009). PhenoCHF corpus was created by filtering the original i2b2 corpus, such that only those summaries (a total of 300) for patients with CHF and kidney failure were retained.…”