BACKGROUND To detect attributes of medical concepts in clinical text, a traditional method often consists of two steps: named entity recognition of attributes and then relation classification between medical concepts and attributes. Here we present a novel solution, in which attribute detection of given concepts is converted into a sequence labeling problem, thus attribute entity recognition and relation classification are done simultaneously within one step. METHODS A neural architecture combining bidirectional Long Short-Term Memory networks and Conditional Random fields (Bi-LSTMs-CRF) was adopted to detect various medical concept-attribute pairs in an efficient way. We then compared our deep learning-based sequence labeling approach with traditional two-step systems for three different attribute detection tasks: disease-modifier, medication-signature, and lab test-value. RESULTS Our results show that the proposed method achieved higher accuracy than the traditional methods for all three medical concept-attribute detection tasks. CONCLUSIONS This study demonstrates the efficacy of our sequence labeling approach using Bi-LSTM-CRFs on the attribute detection task, indicating its potential to speed up practical clinical NLP applications.

中文翻译：

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应用基于深度学习的序列标记方法来检测临床文本中医学概念的属性。


背景技术为了检测临床文本中的医学概念的属性，传统的方法通常包括两个步骤：属性的命名实体识别以及医学概念与属性之间的关系分类。在这里，我们提出了一种新颖的解决方案，将给定概念的属性检测转换为序列标记问题，从而在一步内同时完成属性实体识别和关系分类。方法采用双向长短期记忆网络和条件随机场相结合的神经架构（Bi-LSTMs-CRF）来有效地检测各种医学概念属性对。然后，我们将基于深度学习的序列标记方法与传统的两步系统进行比较，以完成三种不同的属性检测任务：疾病修正、药物特征和实验室测试值。结果我们的结果表明，对于所有三个医学概念属性检测任务，所提出的方法都比传统方法取得了更高的准确性。结论 这项研究证明了我们使用 Bi-LSTM-CRF 的序列标记方法在属性检测任务上的有效性，表明它有加速实际临床 NLP 应用的潜力。