Uzuner Ozlem
State University of New York, Albany, NY 12222, USA; Middle East Technical University, Northern Cyprus Campus, Cyprus.
AMIA Annu Symp Proc. 2008 Nov 6:1252-3.
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.
第二届i2b2临床记录自然语言处理（NLP）研讨会提出了一项关于从患者叙述记录中自动提取肥胖信息的共享任务及挑战。肥胖挑战的目标是延续i2b2的努力，使患者记录可供自然语言处理和医学信息学社区进行研究，以推动医学语言处理技术的发展。为此，i2b2提供了一组经过去识别处理的患者记录，这些记录由医学专业人员针对肥胖相关信息进行了人工标注，并邀请开发能够根据患者记录中的信息自动标记每位患者是否存在肥胖及合并症的系统。在本次研讨会上，我们将讨论肥胖挑战，回顾一些从医疗记录中自动识别肥胖患者及肥胖合并症的方法，并展示挑战结果。i2b2肥胖挑战的研究结果将揭示用于临床应用的叙述记录多标签多类别分类的自然语言处理技术的现状。
