Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing.
Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing.
复制标题
通过癌症自然语言处理的范围审查评估癌症研究和患者护理的电子健康记录。
DOI：
10.1200/cci.22.00006
复制
发表时间：
2022-07
影响因子：
4.2
通讯作者：
Liu, Hongfang
中科院分区：
文献类型：
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作者：
Wang, Liwei;Fu, Sunyang;Wen, Andrew;Ruan, Xiaoyang;He, Huan;Liu, Sijia;Moon, Sungrim;Mai, Michelle;Riaz, Irbaz B.;Wang, Nan;Yang, Ping;Xu, Hua;Warner, Jeremy L.;Liu, Hongfang
自然语言处理（NLP）的发展推动了电子健康记录（EHR）中详细文本数据在癌症研究及患者护理方面的应用。在这篇综述中，我们旨在通过使用最小通用肿瘤学数据元素（mCODE）来评估电子健康记录在癌症研究和患者护理中的作用。mCODE是一项由业界共同推动的工作，旨在为癌症研究和实践定义一组最小的数据元素集。具体而言，我们旨在评估通过自然语言处理提取的数据元素与mCODE的契合度，并回顾现有的用于提取这些数据元素的自然语言处理方法。
我们检索了已发表的文献研究，从主要文献数据库中获取2010年1月至2020年9月期间发表的、以英文撰写的与癌症相关的自然语言处理文章。检索完成后，人工筛选出以电子健康记录为数据源的文章。我们制定了一个图表形式用于相关研究分析，并将数据分为四个主要主题进行分类：元数据、电子健康记录数据及目标癌症类型、自然语言处理方法，以及肿瘤学数据元素和标准。
最终，我们共筛选出123篇出版物并纳入分析。正如预期的那样，我们发现癌症研究和患者护理需要一些mCODE之外的数据元素。自然语言处理方法在透明度和可重复性方面存在不足，并且自然语言处理评估中存在不一致的情况。
我们利用电子健康记录数据，对用于癌症研究和患者护理的自然语言处理进行了全面综述。我们识别并讨论了广泛应用癌症自然语言处理技术所面临的问题和障碍。
The advancement of natural language processing (NLP) has promoted the use of detailed textual data in electronic health records (EHRs) to support cancer research and to facilitate patient care. In this review, we aim to assess EHR for cancer research and patient care by using the Minimal Common Oncology Data Elements (mCODE), which is a community-driven effort to define a minimal set of data elements for cancer research and practice. Specifically, we aim to assess the alignment of NLP-extracted data elements with mCODE and review existing NLP methodologies for extracting said data elements. Published literature studies were searched to retrieve cancer-related NLP articles that were written in English and published between January 2010 and September 2020 from main literature databases. After the retrieval, articles with EHRs as the data source were manually identified. A charting form was developed for relevant study analysis and used to categorize data including four main topics: metadata, EHR data and targeted cancer types, NLP methodology, and oncology data elements and standards. A total of 123 publications were selected finally and included in our analysis. We found that cancer research and patient care require some data elements beyond mCODE as expected. Transparency and reproductivity are not sufficient in NLP methods, and inconsistency in NLP evaluation exists. We conducted a comprehensive review of cancer NLP for research and patient care using EHRs data. Issues and barriers for wide adoption of cancer NLP were identified and discussed.
影响因子：
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作者：
Wilson RA;Chapman WW;Defries SJ;Becich MJ;Chapman BE
通讯作者：
Chapman BE