Detect Attributes of Medical Concepts via Sequence Labeling.
Detect Attributes of Medical Concepts via Sequence Labeling.
复制标题
通过序列标记检测医学概念的属性。
DOI：
10.1109/ichi.2019.8904714
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Wu,Stephen
中科院分区：
文献类型：
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作者：
Xu,Jun;Xiang,Yang;Li,Zhiheng;Lee,Hee-Jin;Xu,Hua;Wei,Qiang;Zhang,Yaoyun;Wu,Yonghui;Wu,Stephen
在这项研究中，我们提出了一种新的方法来检测医学概念的属性，它使用一个序列标记的方法来识别属性实体和分类概念和属性之间的关系，同时在一个步骤。采用双向长短期记忆网络和条件随机场（Bi-LSTMs-CRF）相结合的神经网络结构来检测临床文本中的疾病修饰语对。在ShARe语料库上的测试结果表明，该方法比传统的两步方法具有更高的准确率和F1分数，表明该方法具有加速实际临床NLP应用的潜力。
In this study, we present a new method for detecting attributes of medical concepts, which uses a sequence labeling approach to recognize attribute entities and classify relations between concepts and attributes simultaneously within one step. A neural architecture combining bidirectional Long Short-Term Memory networks and Conditional Random fields (Bi-LSTMs-CRF) was adopted to detect disorder-modifier pairs in clinical text. Evaluations on the ShARe corpus show that the proposed method achieved higher accuracy and F1 scores than the traditional two-step approaches, indicating its potential to accelerate practical clinical NLP applications.