Radiology reports cover different aspects from radiological observation to the diagnosis of an imaging examination, such as x-rays, magnetic resonance imaging, and computed tomography scans. Abundant patient information presented in radiology reports poses a few major challenges. First, radiology reports follow a free-text reporting format, which causes the loss of a large amount of information in unstructured text. Second, the extraction of important features from these reports is a huge bottleneck for machine learning models. These challenges are important, particularly the extraction of key features such as symptoms, comparison/priors, technique, finding, and impression because they facilitate the decision-making on patients' health. To alleviate this issue, a novel architecture CCheXR-Attention is proposed to extract the clinical features from the radiological reports and classify each report into normal and abnormal categories based on the extracted information. We have proposed a modified Mogrifier long short-term memory model and integrated a multihead attention method to extract the more relevant features. Experimental outcomes on two benchmark datasets demonstrated that the proposed model surpassed state-of-the-art models.

中文翻译：

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CCheXR-Attention：使用改进的 Mogrifier 和具有多头注意力的双向 LSTM 进行临床概念提取和胸部 X 射线报告分类

放射学报告涵盖从放射学观察到影像检查诊断的不同方面，例如 X 射线、磁共振成像和计算机断层扫描。放射学报告中提供的大量患者信息带来了一些重大挑战。首先，放射学报告遵循自由文本报告格式，这导致非结构化文本中大量信息的丢失。其次，从这些报告中提取重要特征是机器学习模型的巨大瓶颈。这些挑战很重要，特别是提取关键特征，如症状、比较/先验、技术、发现和印象，因为它们有助于做出有关患者健康的决策。为了缓解这个问题，提出了一种新颖的架构 CCheXR-Attention，从放射学报告中提取临床特征，并根据提取的信息将每个报告分为正常和异常类别。我们提出了一种改进的 Mogrifier 长短期记忆模型，并集成了多头注意力方法来提取更多相关特征。两个基准数据集的实验结果表明，所提出的模型超越了最先进的模型。