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A systematic review of natural language processing applied to radiology reports

机译：对放射学报告的自然语言处理系统审查

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Natural language processing (NLP) has a significant role in advancing healthcare and has been found to be key in extracting structured information from radiology reports. Understanding recent developments in NLP application to radiology is of significance but recent reviews on this are limited. This study systematically assesses and quantifies recent literature in NLP applied to radiology reports. We conduct an automated literature search yielding 4836 results using automated filtering, metadata enriching steps and citation search combined with manual review. Our analysis is based on 21 variables including radiology characteristics, NLP methodology, performance, study, and clinical application characteristics. We present a comprehensive analysis of the 164 publications retrieved with publications in 2019 almost triple those in 2015. Each publication is categorised into one of 6 clinical application categories. Deep learning use increases in the period but conventional machine learning approaches are still prevalent. Deep learning remains challenged when data is scarce and there is little evidence of adoption into clinical practice. Despite 17% of studies reporting greater than 0.85 F1 scores, it is hard to comparatively evaluate these approaches given that most of them use different datasets. Only 14 studies made their data and 15 their code available with 10 externally validating results. Automated understanding of clinical narratives of the radiology reports has the potential to enhance the healthcare process and we show that research in this field continues to grow. Reproducibility and explainability of models are important if the domain is to move applications into clinical use. More could be done to share code enabling validation of methods on different institutional data and to reduce heterogeneity in reporting of study properties allowing inter-study comparisons. Our results have significance for researchers in the field providing a systematic synthesis of existing work to build on, identify gaps, opportunities for collaboration and avoid duplication.
机译：自然语言处理（NLP）在推进医疗保健方面具有重要作用，并且已被发现是从放射学报告中提取结构化信息的关键。了解NLP应用于放射学的最新进展具有重要意义，但最近的评论是有限的。本研究系统地评估和量化了NLP最近的文献应用于放射学报告。我们进行自动化滤波的自动化文献搜索结果4836结果，元数据丰富步骤和引用搜索结合手动评论。我们的分析基于21个变量，包括放射学特征，NLP方法，性能，研究和临床应用特征。我们对2019年的出版物检索的164个出版物进行了全面的分析，几乎是2015年的三倍。每种出版物分为6个临床应用类别中的一个。深度学习使用期间增加，但传统的机器学习方法仍然是普遍存在的。当数据稀缺时，深入学习仍然受到挑战，并且几乎没有通过临床实践的证据。尽管有17％的研究报告大于0.85 F1分数，但仍然很难评估这些方法，因为它们中的大多数都使用不同的数据集。只有14项研究通过10个外部验证结果提供了14项研究，其代码可用。自动理解放射学报告的临床叙述具有增强医疗过程的潜力，我们表明该领域的研究仍在继续增长。如果域名将应用程序移动到临床用途中，模型的再现性和解释性很重要。可以更好地分享守则能够在不同机构数据上验证方法，并降低报告研究属性的异质性，允许学习间比较。我们的结果对该领域的研究人员具有重要性，为现有工作进行了系统的合成，识别差距，协作的机会，避免重复。

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