Characterization of Change and Significance for Clinical Findings in Radiology Reports Through Natural Language Processing
Characterization of Change and Significance for Clinical Findings in Radiology Reports Through Natural Language Processing
复制标题
通过自然语言处理描述放射学报告中临床结果的变化和意义
DOI：
10.1007/s10278-016-9931-8
复制
发表时间：
2017-06-01
影响因子：
4.4
通讯作者：
Langlotz, Curtis P.
中科院分区：
文献类型：
--
作者：
Hassanpour, Saeed;Bay, Graham;Langlotz, Curtis P.
我们建立了一种自然语言处理（NLP）方法，可以自动提取放射学报告中的临床发现，并根据放射学特定的信息模型来表征其变化和意义的水平。为此，我们结合了机器学习和基于规则的方法。我们的方法是独特的，在文本分析中的表面，实体和话语层次的抽象捕捉不同的功能和水平。这种组合使我们能够认识到这项任务的放射学报告叙述的基本语义。我们评估了我们的方法对来自四个主要医疗机构的放射学报告。我们的评估显示了我们的方法在突出重要变化（准确率99.2%，精确率96.3%，召回率93.5%，F1评分94.7%）和识别重要观察结果（准确率75.8%，精确率75.2%，召回率75.7%，F1评分75.3%）以表征放射学报告方面的有效性。这种方法可以帮助临床医生快速理解放射学报告中的关键观察结果，并促进临床决策支持，审查优先级和疾病监测。
We built a natural language processing (NLP) method to automatically extract clinical findings in radiology reports and characterize their level of change and significance according to a radiology-specific information model. We utilized a combination of machine learning and rule-based approaches for this purpose. Our method is unique in capturing different features and levels of abstractions at surface, entity, and discourse levels in text analysis. This combination has enabled us to recognize the underlying semantics of radiology report narratives for this task. We evaluated our method on radiology reports from four major healthcare organizations. Our evaluation showed the efficacy of our method in highlighting important changes (accuracy 99.2%, precision 96.3%, recall 93.5%, and F1 score 94.7%) and identifying significant observations (accuracy 75.8%, precision 75.2%, recall 75.7%, and F1 score 75.3%) to characterize radiology reports. This method can help clinicians quickly understand the key observations in radiology reports and facilitate clinical decision support, review prioritization, and disease surveillance.