Author Affiliations : Columbia University School of Nursing, New York.
Comput Inform Nurs. 2023 Jun 1;41(6):377-384. doi: 10.1097/CIN.0000000000000967.
Natural language processing includes a variety of techniques that help to extract meaning from narrative data. In healthcare, medical natural language processing has been a growing field of study; however, little is known about its use in nursing. We searched PubMed, EMBASE, and CINAHL and found 689 studies, narrowed to 43 eligible studies using natural language processing in nursing notes. Data related to the study purpose, patient population, methodology, performance evaluation metrics, and quality indicators were extracted for each study. The majority (86%) of the studies were conducted from 2015 to 2021. Most of the studies (58%) used inpatient data. One of four studies used data from open-source databases. The most common standard terminologies used were the Unified Medical Language System and Systematized Nomenclature of Medicine, whereas nursing-specific standard terminologies were used only in eight studies. Full system performance metrics (eg, F score) were reported for 61% of applicable studies. The overall number of nursing natural language processing publications remains relatively small compared with the other medical literature. Future studies should evaluate and report appropriate performance metrics and use existing standard nursing terminologies to enable future scalability of the methods and findings.
自然语言处理包括各种技术，这些技术有助于从叙述性数据中提取意义。在医疗保健领域，医学自然语言处理一直是一个不断发展的研究领域；然而，关于它在护理中的应用却知之甚少。我们在 PubMed、EMBASE 和 CINAHL 上进行了搜索，共找到了 689 项研究，通过自然语言处理对护理记录进行分析后，将研究数量缩小至 43 项。我们对每项研究的数据进行了提取，包括研究目的、患者人群、方法、性能评估指标和质量指标。大多数（86%）研究是在 2015 年至 2021 年期间进行的。大多数（58%）研究使用了住院患者的数据。有四分之一的研究使用了开源数据库中的数据。最常用的标准术语是统一医学语言系统和医学系统命名法，而只有 8 项研究使用了特定于护理的标准术语。61%的适用研究报告了完整系统性能指标（如 F 分数）。与其他医学文献相比，护理自然语言处理的出版物数量仍然相对较少。未来的研究应评估和报告适当的性能指标，并使用现有的标准护理术语，以实现方法和发现的未来可扩展性。
