Second i2b2 workshop on natural language processing challenges for clinical records.
The second i2b2 workshop on Natural Language Processing (NLP) for clinical records presents a shared-task and challenge on the automated extraction of obesity information from narrative patient records. The goal of the obesity challenge is to continue i2b2's effort to open patient records to studies by the NLP and Medical Informatics communities for the advancement of the state of the art in medical language processing. For this, i2b2 made available a set of de-identified patient records that are hand-annotated by medical professionals for obesity-related information, and invited the development of systems that can automatically mark the presence of obesity and co-morbidities in each patient from information in their records. In this workshop, we will discuss the obesity challenge, review some approaches to automatically identifying obese patients and obesity co-morbidities from medical records, and present the challenge results. The findings of the i2b2 challenge on obesity will shed light onto the state of the art in natural language processing for multi-label multi-class classification of narrative records for clinical applications.
Uzuner
Ozlem
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State University of New York, Albany, NY 12222, USA; Middle East Technical University, Northern Cyprus Campus, Cyprus.
D000465:Algorithms; D001185:Artificial Intelligence; D004493:Education; D016247:Information Storage and Retrieval / Q000379:methods*; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D013358:Subject Headings; D009626:Terminology as Topic; D018875:Vocabulary, Controlled