标题： 基于自然语言处理的癌症相关电子健康记录注释的框架语义概述 显示英文标题

标题： A frame semantic overview of NLP-based information extraction for cancer-related EHR notes

摘要： 目标：电子健康记录（EHR）笔记中有关癌症的信息很多，如果能够使用自然语言处理（NLP）方法提取和结构化这些信息，将对生物医学研究有帮助。在本文中，我们对现有的临床NLP文献进行了范围综述，以针对癌症进行研究。方法：我们从PubMed、Google Scholar、ACL Anthology和现有综述中识别出描述从EHR来源提取特定癌症相关信息的NLP方法的研究。本研究使用了两个排除标准。我们排除了提取技术过于宽泛无法表示为框架的文章，以及使用了非常低级的提取方法的文章。最终共纳入79篇论文。我们根据框架语义原则组织这些信息，以帮助识别共同的重叠领域和潜在的空白。结果：从所审查的关于癌症信息的论文中创建了框架，如癌症诊断、肿瘤描述、癌症手术、乳腺癌诊断、前列腺癌诊断和前列腺癌患者的疼痛。这些框架包括定义以及具体的框架元素（即可提取属性）。我们发现癌症诊断是所审查论文中最常见的框架（79篇中的36篇），近期的研究重点是提取与治疗和乳腺癌诊断相关的信息。结论：本文描述的常见框架列表识别了现有NLP技术提取的重要癌症相关信息，并作为未来需要从EHR笔记中提取癌症信息的研究人员的有用资源。由于癌症NLP系统的大量重复，我们认为一个注释癌症框架及其相应NLP工具的通用资源将是有价值的。 显示英文摘要

摘要： Objective: There is a lot of information about cancer in Electronic Health Record (EHR) notes that can be useful for biomedical research provided natural language processing (NLP) methods are available to extract and structure this information. In this paper, we present a scoping review of existing clinical NLP literature for cancer. Methods: We identified studies describing an NLP method to extract specific cancer-related information from EHR sources from PubMed, Google Scholar, ACL Anthology, and existing reviews. Two exclusion criteria were used in this study. We excluded articles where the extraction techniques used were too broad to be represented as frames and also where very low-level extraction methods were used. 79 articles were included in the final review. We organized this information according to frame semantic principles to help identify common areas of overlap and potential gaps. Results: Frames were created from the reviewed articles pertaining to cancer information such as cancer diagnosis, tumor description, cancer procedure, breast cancer diagnosis, prostate cancer diagnosis and pain in prostate cancer patients. These frames included both a definition as well as specific frame elements (i.e. extractable attributes). We found that cancer diagnosis was the most common frame among the reviewed papers (36 out of 79), with recent work focusing on extracting information related to treatment and breast cancer diagnosis. Conclusion: The list of common frames described in this paper identifies important cancer-related information extracted by existing NLP techniques and serves as a useful resource for future researchers requiring cancer information extracted from EHR notes. We also argue, due to the heavy duplication of cancer NLP systems, that a general purpose resource of annotated cancer frames and corresponding NLP tools would be valuable.