学习识别临床文本中的治疗关系。
Learning to identify treatment relations in clinical text.
作者信息
Bejan Cosmin A, Denny Joshua C
机构信息
Department of Biomedical Informatics, Vanderbilt University, Nashville, TN.
Department of Biomedical Informatics, Vanderbilt University, Nashville, TN ; Department of Medicine, Vanderbilt University, Nashville, TN.
出版信息
AMIA Annu Symp Proc. 2014 Nov 14;2014:282-8. eCollection 2014.
In clinical notes, physicians commonly describe reasons why certain treatments are given. However, this information is not typically available in a computable form. We describe a supervised learning system that is able to predict whether or not a treatment relation exists between any two medical concepts mentioned in clinical notes. To train our prediction model, we manually annotated 958 treatment relations in sentences selected from 6,864 discharge summaries. The features used to indicate the existence of a treatment relation between two medical concepts consisted of lexical and semantic information associated with the two concepts as well as information derived from the MEDication Indication (MEDI) resource and SemRep. The best F1-measure results of our supervised learning system (84.90) were significantly better than the F1-measure results achieved by SemRep (72.34).
在临床记录中，医生通常会描述进行某些治疗的原因。然而，这些信息通常不是以可计算的形式提供的。我们描述了一种监督学习系统，它能够预测临床记录中提到的任意两个医学概念之间是否存在治疗关系。为了训练我们的预测模型，我们从6864份出院小结中选取句子，手动标注了958个治疗关系。用于表明两个医学概念之间存在治疗关系的特征包括与这两个概念相关的词汇和语义信息，以及从药物适应症（MEDI）资源和SemRep中获得的信息。我们的监督学习系统的最佳F1值结果（84.90）明显优于SemRep的F1值结果（72.34）。
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