Recognizing Obesity and Comorbidities in Sparse Data

Uzuner, Oezlem
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.


Machine Learning and Rule-based Approaches to Assertion Classification
Uzuner, Oezlem; Zhang, Xiaoran; Sibanda, Tawanda (Oxford University Press (OUP), 2009-01-01)
Objectives: The authors study two approaches to assertion classification. One of these approaches, Extended NegEx (ENegEx), extends the rule-based NegEx algorithm to cover alter-association assertions; the other, Statistical Assertion Classifier (StAC), presents a machine learning solution to assertion classification.
Examination of time-varying kinematic responses to support surface disturbances
Gürses, Senih; Keshner, E. A. (Elsevier BV, 2011-01-01)
To examine the evolution of inter-segmental coordination over-time, a previously developed multivariate model of postural coordination during quiet stance by Kuo et al. [1,2] has been extended. In the original model, postural coordination was treated as an eigenvalue-eigenvector problem between two segmental degrees of freedom represented by angular displacements of the trunk and shank. Strategies of postural coordination were then identified using the sign of the covariance between the two segments' angula...
Specializing for predicting obesity and its co-morbidities
Goldstein, Ira; Uzuner, Oezlem (Elsevier BV, 2009-10-01)
We present specializing, a method for combining classifiers for multi-class classification. Specializing trains one specialist classifier per class and utilizes each specialist to distinguish that class from all others in a one-versus-all manner. It then supplements the specialist classifiers with a catch-all classifier that performs multi-class classification across all classes. We refer to the resulting combined classifier as a specializing classifier.
Preliminary results of a novel enhancement method for high-frequency hearing loss
Arioz, Umut; Arda, Kemal; Tuncel, Umit (Elsevier BV, 2011-06-01)
In this study, a software program was developed for high-frequency hearing loss subjects that includes a detailed audiogram and novel enhancement methods. The software performs enhancements of the audibility of high-frequency sounds according to the subject's detailed 31-point audiogram. This provides subject-specific gains in the entire frequency spectrum, and especially for high frequencies, of sounds. Amplification, compression, and transposition are the three main processing methods used to obtain the d...
Computerized hybrid decision-making system for hormone replacement therapy in menopausal women
Bacak, Hikmet Özge; Leblebicioğlu, Mehmet Kemal; TANAÇAN, ATAKAN; BEKSAÇ, MEHMET SİNAN (IOS Press, 2019-01-01)
BACKGROUND: The diversity of the results of different hormone replacement therapy (HRT) protocols and the fuzziness of the conclusions have caused problems in routine clinical practice.
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: