Taylor Stuart J, Harabagiu Sanda M
The University of Texas at Dallas, Richardson, TX, USA.
AMIA Annu Symp Proc. 2018 Dec 5;2018:1018-1027. eCollection 2018.
Detecting negation in biomedical texts entails the automatic identification of negation cues (e.g. "never", "not", "no longer") as well as the scope of these cues. When medical concepts or terms are identified within the scope of a negation cue, their polarity is inferred as "negative". All the other concepts or words receive a positive polarity. Correctly inferring the polarity is essential for patient cohort retrieval systems, as all inclusion criteria need to be automatically assigned positive polarity, whereas exclusion criteria should receive negative polarity. Motivated by the recent development of techniques using deep learning, we have experimented with a neural negation detection technique and compared it against an existing neural polarity recognition system, which were incorporated in a patient cohort system operating on clinical electroencephalography (EEG) reports. Our experiments indicate that the neural negation detection method produces better patient cohorts then the polarity recognition method.
在生物医学文本中检测否定需要自动识别否定线索（如“从不”“不”“不再”）及其范围。当在否定线索范围内识别出医学概念或术语时，其极性被推断为“否定”。所有其他概念或词则具有正极性。正确推断极性对于患者队列检索系统至关重要，因为所有纳入标准都需要自动赋予正极性，而排除标准应具有负极性。受深度学习技术近期发展的推动，我们试验了一种神经否定检测技术，并将其与现有的神经极性识别系统进行比较，这两种技术都被整合到一个基于临床脑电图（EEG）报告运行的患者队列系统中。我们的实验表明，神经否定检测方法比极性识别方法能产生更好的患者队列。
