A knowledge discovery and reuse pipeline for information extraction in clinical notes
A knowledge discovery and reuse pipeline for information extraction in clinical notes
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DOI：
10.1136/amiajnl-2011-000302
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发表时间：
2011-09-01
影响因子：
6.4
通讯作者：
Li, Min
中科院分区：
文献类型：
--
作者：
Patrick, Jon D.;Nguyen, Dung H. M.;Li, Min
目的临床数据的信息提取和分类是当前自然语言处理的难点。本文提出了一种级联的方法来处理临床数据的三种不同的提取和分类：概念标注、断言分类和关系分类。材料和方法为临床自然语言处理开发了一个管道系统，包括一个校对过程，具有金标准的反射验证和纠正。该信息提取系统是机器学习方法和基于规则的方法的结合。该系统的产出用于第四i2b2/VA共享任务和讲习班挑战的所有三个层次的评价。结果总体概念分类的f值为83.3%，基线值为77.0%；概念断言的最佳f值为92.4%；临床概念之间关系分类的f值为72.6%，基线值为71.0%。攻毒试验集的微平均结果分别为81.79%、91.90%和70.18%。多任务测试中的挑战需要为每个单独的任务分配时间和工作量，以便对所有三个任务的总体性能评估将提供更多信息，而不是将每个任务评估视为独立的。在这项工作中开发的模型的简单性应该与挑战中其他参与者的非常大的特征空间形成对比，他们只取得了稍好的表现。在比较结果时，需要对消息最小化理论中定义的模型的复杂性收取罚金。结论提出了一套完整的语言处理模型构建流水线系统，可用于临床病历语言结构的多种实际检测任务。
Objective Information extraction and classification of clinical data are current challenges in natural language processing. This paper presents a cascaded method to deal with three different extractions and classifications in clinical data: concept annotation, assertion classification and relation classification.Materials and Methods A pipeline system was developed for clinical natural language processing that includes a proofreading process, with gold-standard reflexive validation and correction. The information extraction system is a combination of a machine learning approach and a rule-based approach. The outputs of this system are used for evaluation in all three tiers of the fourth i2b2/VA shared-task and workshop challenge.Results Overall concept classification attained an F-score of 83.3% against a baseline of 77.0%, the optimal F-score for assertions about the concepts was 92.4% and relation classifier attained 72.6% for relationships between clinical concepts against a baseline of 71.0%. Micro-average results for the challenge test set were 81.79%, 91.90% and 70.18%, respectively.Discussion The challenge in the multi-task test requires a distribution of time and work load for each individual task so that the overall performance evaluation on all three tasks would be more informative rather than treating each task assessment as independent. The simplicity of the model developed in this work should be contrasted with the very large feature space of other participants in the challenge who only achieved slightly better performance. There is a need to charge a penalty against the complexity of a model as defined in message minimalisation theory when comparing results.Conclusion A complete pipeline system for constructing language processing models that can be used to process multiple practical detection tasks of language structures of clinical records is presented.