2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text
2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text
复制标题
DOI：
10.1136/amiajnl-2011-000203
复制
发表时间：
2011-09-01
影响因子：
6.4
通讯作者：
DuVall, Scott L.
中科院分区：
文献类型：
--
作者：
Uzuner, Oezlem;South, Brett R.;DuVall, Scott L.
2010年i2 b2/VA临床记录自然语言处理挑战研讨会提出了三个任务：概念提取任务，重点是从患者报告中提取医疗概念;断言分类任务，重点是为医疗问题概念分配断言类型;关系分类任务，重点是分配医疗问题，测试和治疗之间的关系类型。i2 b2和VA为这三个任务提供了一个带注释的参考标准语料库。使用该参考标准，开发了22个概念提取系统、21个断言分类系统和16个关系分类系统。这些系统表明，机器学习方法可以通过基于规则的系统来增强，以确定概念，断言和关系。根据任务的不同，基于规则的系统可以为机器学习提供输入，也可以对机器学习的输出进行后处理。当训练数据不足时，分类器、来自未标记数据的信息和外部知识源的集合可以提供帮助。
The 2010 i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records presented three tasks: a concept extraction task focused on the extraction of medical concepts from patient reports; an assertion classification task focused on assigning assertion types for medical problem concepts; and a relation classification task focused on assigning relation types that hold between medical problems, tests, and treatments. i2b2 and the VA provided an annotated reference standard corpus for the three tasks. Using this reference standard, 22 systems were developed for concept extraction, 21 for assertion classification, and 16 for relation classification. These systems showed that machine learning approaches could be augmented with rule-based systems to determine concepts, assertions, and relations. Depending on the task, the rule-based systems can either provide input for machine learning or post-process the output of machine learning. Ensembles of classifiers, information from unlabeled data, and external knowledge sources can help when the training data are inadequate.